(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(bright upbeat music) >> The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from Enterprise Technology Research shows Kubernetes specifically leads the pack into both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges in particular insuring consistent and predictable application performance is tricky especially because containers, they're so flexible and they enable portability. Things are constantly changing. DevOps pros have to way through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes resources and kills operational efficiency. So teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded mid last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns is Matt Provo, founder and CEO of StormForge. Matt, welcome to theCUBE. Good to see you. >> Good to see you. Thanks for having me. >> Yeah, so we saw you guys at a KubeCon sort of first introduce you to our community but add a little color to my intro there if you will. >> Yeah, well, Semi stole my thunder but I'm okay with that. Absolutely agree with everything you said in the intro. You know, the problem that we have set out to solve which is tailor made for the use of real machine learning not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so a number of years ago, we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today as they're moving more workloads over, sort of drink the Kool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn. And developers in many ways love it. Once they start to operationalize the use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it. >> So you've got news. You had launch coming to further address these problems. Tell us about that. >> Yeah, so historically, you know, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we've kind of been single threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly amazing results. My vision has been for us to be able to close the loop, however, between data coming out of pre-production and the associated optimizations and data coming out of production environment and our ability to optimize that. A lot of our users along the way have said these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce kind of the a second core module for our platform called Optimize Live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. >> So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed... And I used to ask, you know, technology companies all the time. Okay, so you're telling me beforehand what the optimal configuration should be and resource allocation. What happens if something changes? >> Yeah. >> And then it's always, always a pause. >> Yeah. >> And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So specifically the problem you're addressing. Maybe talk about that a little bit. >> Yeah, so we view what happens in pre-production as sort of the experimentation phase. And our machine learning is allowing the user to experiment in scenario plan. What we're doing with Optimize Live and adding the the production piece is what we kind of also call kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance, from a cost standpoint is with your SLOs and your SLAs as well as your business objectives. And so that's the entire point of this edition is to allow our users to experience hopefully the Nirvana associated with that because it's an exciting opportunity for them and really something that no else is doing from the standpoint of closing that loop. >> So you said front machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? >> Yeah, I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning or AI gets tagged to a lot of stuff. It's very buzzwordy. It looks good. I'm fortunate to have found a number of folks from the outset of the company with, you know, PhDs in Applied Mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so, you know, for the first three or four years of the company's history, we really operated as a lab. And that was our focus. We then decided, we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all these pain points around how fast the adoption of containers and Kubernetes have taken place but the pain that the developers are running into, we actually found for ourselves that this was the perfect use case. >> So how specifically does Optimize Live work? Can you add a little detail on that? >> Yes, so when you... Many organizations today have an existing monitoring APM observability suite really in place. They've also got a metric source. So this could be something like Datadog or Prometheus. And once that data starts flowing, there's an out of the box or kind of a piece of Kubernetes that ships with it called the VPA or the Vertical Pod Autoscaler. And less than, really than 1% of Kubernetes users take advantage of the VPA mostly because it's really challenging to configure and it's not super compatible with the the tool set or, you know, the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this environment or in this world for developers is they're having to make decisions on a number of different metrics or resource elements typically things like memory and CPU. And they have to decide what are the requests I'm going to allow application and what are the limits? So what are those thresholds that I'm going to be okay with so that I can, again, try to hit my business objectives and keep in line with my SLAs? And to your earlier point in the intro, it's often guesswork. You know, they either have to rely on out of the box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this. And so with Optimize Live, we're making that one click. And so we're continuously and consistently observing and watching the data that's flowing through these tools and we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over are the recommendations and manually deploy them into their environment themselves. And we, again, really believe that the user knows their application. They know the goals that they have and we don't. But we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. >> So the business objectives are an input from the application team? >> Yep. >> And then your system is smart enough to adapt and address those. >> Application over application, right? And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to predefine that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. >> And we're going to hear later from a customer who's hosting a Drupal, one of the largest Drupal hosts. So it's all do it yourself across thousands of customers so it's, you know, very unpredictable. I want to make something clear though as to where you fit in the ecosystem. You're not an observability platform, you leverage observability platforms, right? So talk about that and where you fit into the ecosystem. >> Yeah, so it's a great point. We're also, you know, a series B startup and growing. We've the choice to be very intentionally focused on the problems that we've solve. And we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We are really an intelligence platform. And that intelligence and insights that we're able to draw is because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to to Datadog or Dynatrace or Splunk or AppDynamics or some of the other. And we're totally fine with that. They've got great market share and penetration. They do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the Intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive. They're visualized and somewhat reactive. And we add that proactive nature onto it, the insights and ultimately the appropriate level of automation. >> So when I think, Matt, about cloud native and I go back to the sort of origins of CNCF who's a, you know, handful of companies. And now you look at the participants it'll, you know, make your eyes bleed. How do you address dealing with all those companies and what is the partnership strategy? >> Yeah, it's so interesting because it's just that even that CNCF landscape has exploded. It was not too long ago where it was as small or smaller than the FinOps landscape today which by the way, the FinOps piece is also on a a neck breaking, you know, growth curve. We, I do see, although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain, you know, with our customers and users. And so we've made strategic and intentional decisions on deep partnerships in some cases like OEM uses of our technology and certainly, you know, intelligent and seamless integrations into a few. So, you know, we'll be announcing a really exciting partnership with AWS and that specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus and specifically a few other cloud providers that are operating, manage Prometheus environments. >> Okay, so where do you want to take this thing? You're not taking the observability guys head on, smart move. So many of those even entering the market now. But what is the vision? >> Yeah, so we've had this debate a lot as well 'cause it's super difficult to create a category. You know, on one hand, you know, I have a lot of respect for founders and companies that do that. On the other hand from a market timing standpoint, you know we fit into AIOps, that's really where we fit. You know, we've made a bet on the future of Kubernetes and what that's going to look like. And so from a containers and Kubernetes standpoint, that's our bet. But we're an AIOps platform. You know, we'll continue getting better at the problems we solve with machine learning and we'll continue adding data inputs. So we'll go, you know, we'll go beyond the application layer which is really where we play now. We'll add, you know, kind of whole cluster optimization capabilities across the full stack. And the way we will get there is by continuing to add different data inputs that make sense across the different layers of the stack. And it's exciting. We can stay vertically oriented on the problems that we're really good at solving but we can become more applicable and compatible over time. >> So that's your next concentric circle. As the observability vendors expand their observation space, you can just play right into that. >> Yeah. >> The more data you get because your purpose built to solving these types of problems. >> Yeah, so you can imagine a world right now out of observability, we're taking things like telemetry data pretty quickly. You can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow, it just feeds our own, you know, we are reliant on data. >> Excellent, Matt, thank you so much. >> Thanks for having me. >> Appreciate for coming on. Okay, keep it right there in a moment. We're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier. They went through a major replatforming with Kubernetes on AWS. You're watching theCUBE, you are leader in enterprise tech coverage. (bright upbeat music)

(upbeat music) >> We're back with Charley Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charley welcome to theCUBE. >> Nice to meet you Dave. >> Acquia, tell us about the company. >> Sure, so Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space, just behind Adobe. Very strong business growing well and innovating every day. >> Drupal open source, super deep high quality content management system. And more experience, you call it an experience platform. >> An experience platform, open, flexible. We want our customers to have choice the ability to solve their problems how they want leveraging the power of the open source community. >> What were the big challenges? Just describe your, kind of the business drivers. We're going to talk about StormForge but the things that you were facing some of the challenges that's kind of led you to StormForge. >> Sure, so our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself. But that has to run on a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and to be able to support those choices on our platform. >> So optionality is key. Sometimes that creates other challenges. Like you've got one of everything. How do you deal with that challenge? >> That's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice but to do it in a cost efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. >> So you guys had to go through a major replatforming effort around containers and Kubernetes can you talk about that and what role StormForge played? >> Sure, so tied to the last point, our objective is to provide customers the highest performance, most secure platform. The entire industry of course is moving to Kubernetes and leveraging containers. We are a large consumer of AWS Services and are undergoing a major replatforming away from Legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to and the power of Kubernetes and containers is to support that. And so we looked at StormForge as a way for us to right size resource capacity to support our customer's applications. >> I love it, AWS is now Legacy. But Andy Jassy one time said that if they had to redo Amazon they'd it in Lambda using serverless and so, it's been around a long time now. Okay so what were the outcomes that you were seeking? Was it, better management, cost reduction and how'd that go? >> Our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging StormForge would put us in a position where we'd be able to right size resource to those different kinds of applications. Essentially let the platform align to how customers wanted to operate their applications. And so StormForge's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want, and when they need it on demand. A lot of the auto scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. >> So this move toward containers and microservices sort of modern application development coincides with a modern platform like StormForge. And so there are, I'm sure there are alternatives out there, why StormForge? Maybe you could explain a little bit more about why, from your perspective what it does and why you chose them. >> So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best of Drupal. And so we felt like for us to true really maximize Kubernetes and containers and the power of those underlying technologies. On the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage StormForge to enable us to rightsize applications for performance, provide us cost benefits, allocate what you need when you need it for our customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally, but as you start getting to scale from, a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS and StormForge provided that solution. >> Make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs if you will, to automation and scale. >> Correct. >> And what was that like? Was the team leaning into that, loving it? Was it a, a challenging thing for you guys to get there? >> That's a good question. The benefit in the way that StormForge applies it. So they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome. Sort of the stuff and resource allocation resource consumption. That's an enabler we can outsource that to StormForge >> This is not batch it's, you're basically doing this in sort of near realtime Optimize Live, is the capability, maybe you can describe what it is. >> So Optimize Live is new, we're in testing with that. We've done extensive testing with StormForge on the core call it decision making logic that allows for the right sizing of consumption and resources for our customer application. So that has already been tested. So the core engine's been tested. Optimize Live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance cost, other parameters. Again, it informs our decision making and our management of our platform. That would be very, very difficult otherwise. Without StormForge we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information, and, and, and. And that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and StormForge for provides that. >> So it's kind of human in the loop thing. Hey, here's what like our recommendation or here's some options that you might want to, here's a path that you want to go down, but it's not taking that action for you necessarily. You don't want that. You want to make sure that the experts are have a hand in it still, is that correct? >> Correct, you still want the experts to have a hand in it but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability that insight that allows you to do that at scale. >> So if you had to step back and think about your relationship with StormForge what was the business impact of bringing them in? >> First, from a time to market perspective we're able to get to market with a higher performing more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that StormForge brings to the table is world class. I've run machine learning teams, data science teams and would put them in the top 1% of any team that I've worked with in terms of their expertise. The logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product in Optimize Live with that automation component you mentioned, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications. More real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating a capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. >> So that puts money in your pocket and your customers are happier. So there are higher renewal rates, less churn, high air prices over time as you add more capabilities. >> That's correct. >> What's it like, new application approach, Kubernetes containers, fine. Okay I need a modern platform but it's a relatively new company StormForge. What's it like working with them? >> Their talent level is world class. I wasn't familiar with them when I joined Acquia came to know them and been very impressed. There's many other providers in the market that will speak to some similar capabilities and will make many claims. But from our assessment our view is that they're the right partner for us, they're the right size, they're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think again the quality that people that's been manifested in the product itself, it's a high quality product. I think it's unique to the market. >> So Napoleon Hill famous writer, thinker, he wrote "Think and Grow Rich." If you haven't read it, check it out. One of his concepts is this a lever, small lever can move a big rock. It can be very powerful. Do you see StormForge as having that kind of effect on your business that change on your business? >> I do. Like I said, I think the engagement with them has proven, and this isn't, debatable based on the results that we've had with them. We ran that team through the ringer to validate the technology. Again, we'd heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases, to really stress test their capabilities. And they came out well ahead of any metric we put forth even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here. Why I'm an advocate. Why I think they're an outstanding company with a tremendous amount of potential. >> Thinking about, what can you tell us about where you want to take the company and the partnership with StormForge. >> I think the main next step is for us to engage with StormForge to drive automation drive decisioning, as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think the, it's a learning process for both both sides, but I think the it's been successful so far and has a lot potential. >> Sounds like you had a great business and a great new partnership. So thanks so much for coming on theCUBE, appreciate it. >> Thank you very much, appreciate your time. >> My pleasure. And thank you for watching theCUBE, you're global leader in enterprise tech coverage. (upbeat music)

(upbeat music) >> The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from enterprise technology research shows Kubernetes specifically, leads the pack into both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges, in particular ensuring consistent and predictable application performance is tricky especially because containers they're so flexible and they enable portability, things are constantly changing. DevOps Pros have to wade through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes resources and kills operational efficiency. So teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded mid last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns is Matt Provo, founder and CEO of StormForge. Matt, welcome to The CUBE. Good to see you. >> Good to see you. Thanks for having me. >> Yeah. So we saw you guys at CUBE con, sort of first introduce you to our community, but add a little color to my intro there if you want. >> Well, you semi stole my thunder, but I'm okay with that. Absolutely agree with everything you said in the intro. The problem that we have set out to solve, which is tailor made for the use of real machine learning, not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so a number of years ago, we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today, as they're moving more workloads over, sort of drink the cool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn and developers in many ways love it. Once they start to operationalize use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively and ensuring and implementing the right kind of automation that empowers developers into the process, ultimately does not automate them out of it. >> So you've got news, you a hard launch coming to further address these problems. Tell us about that. >> Yeah. So historically, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we've kind of been single threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly, amazing results. My vision has been for us to be able to close the loop, however, between data coming out of pre-production and the associated optimizations and data coming out of production environment and our ability to optimize that. A lot of our users along the way have said, these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce, kind of the second core module for our platform called optimized live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. >> So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed, and I used to ask technology companies all the time. Okay. So you're telling me beforehand what the optimal configuration should be and resource allocation, what happens if something changes? And then it's always, always a pause. And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So this is specifically the problem you're addressing, maybe talk about that a little bit more. >> Yeah. So we view what happens in pre-production as sort of the experimentation phase. And our machine learning is is allowing the user to experiment and scenario plan. What we're doing with optimized live and adding the production piece is what we kind of also call, kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance, from a cost standpoint is aligning with your SLOs and your SLAs, as well as your business objectives. And so that's the entire point of this edition, is to allow our users to experience, hopefully the the Nirvana associated with that, because it's an exciting opportunity for them and really something that nobody else is doing from the standpoint of closing that loop. >> So you said up front, machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? >> Yeah. I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning or AI gets tagged to a lot of stuff. It's very buzz wordy, it looks good. I'm fortunate to have found a number of folks from the outset of the company with PhDs and applied mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so for the first three or four years of the company's history, we really operated as a lab. And that was our focus. We then decided, we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all these pain points around, how fast the adoption of containers and Kubernetes have taken place, but the pain that developers are running into, we actually found for ourselves that this was the perfect use case. >> So how specifically does optimize live work? Can you add a little detail on that? >> Yeah. So when you... Many organizations today have an existing monitoring APM, observability suite really in place, they've also got a metric source. So this could be something like Datadog, or Prometheus. And once that data starts flowing there's an out of the box or kind of a piece of Kubernetes that ships with it called the VPA or the vertical pod auto scaler. And less than, really less than 1% of Kubernetes users take advantage of of the VPA, mostly because it's really challenging to configure and it's not super compatible with the tool set or the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this world for developers is they're having to make decisions on a number of different metrics or resource elements, typically things like memory and CPU, and they have to decide, what are the requests I'm going to allow for this application and what are the limits? So what are those thresholds that I'm going to be okay with? So that I can, again, try to hit my business objectives and keep in line with my SLAs. And to your earlier point in the intro, it's often guesswork. They either have to rely on out of the box recommendations that ship with the databases and other services that they are using, or it's a super manual process to go through and try to configure and tune this. And so with optimized live, we're making that one click. And so we're continuously and consistently observing and watching the data that's flowing through these tools and we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy, or they can retain some semblance of control over the recommendations and manually deploy them into their environment themselves. And we, again, really believe that the user knows their application. They know the goals that they have, we don't, but we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. >> So the business objectives are an input from the application team. And then your system is smart enough to a adapt and address those? >> Application over application. And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to predefine that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. >> And we're going to hear later from a customer who's hosting a Drupal, one of the largest Drupal hosts. So it's all do it yourself across that of customers. So it's very unpredictable. I want to make something clear though. As to where you fit in the ecosystem, you're not an observability platform, you leverage observability platforms. So talk about that and where you fit in into the ecosystem. >> Yeah. So this is a great point. We're also a series B startup and growing where we've the choice to be very intentionally focused on the problems that we've solve and we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We are really an intelligence platform and that intelligence and insights that we're able to draw is because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to Datadog or Dynatrace or Splunk or UpDynamics or some of the other. And we're totally fine with that. They've got great market share and penetration. They do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the Intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive, they're visualized and somewhat reactive and we make those, we add that proactive nature onto it, the insights and ultimately the appropriate level of automation. >> So when I think Matt about cloud native and I go back to the sort of origins of CNCF, it was a handful of companies, and now you look at the participants make your eyes bleed. How do you address dealing with all those companies and what's the partnership strategy? >> Yeah, it's so interesting because, just that even that CNCF landscape has exploded. It was not too long ago where it was as small or smaller than the Finops landscape today, which by the way, the Finops piece is also on a neck breaking growth curve. I do see, although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain with our customers and users. And so we've made strategic and intentional decisions on deep partnerships, in some cases like OEM, uses of our technology and certainly, intelligent and seamless integrations into a few. So we'll be announcing a really exciting partnership with AWS and that specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus, and specifically a few other cloud providers that are operating manage Prometheus environments. >> Okay. So where do you want to take this thing? You're not taking the observability guys head on, smart move. So many of those even entering the market now. But what is the vision? >> Yeah. So we've had this debate a lot as well 'cause it's super difficult to create a category. On one hand, I have a lot of respect for founders and companies that do that, on the other hand, from a market timing standpoint, we fit into AI Ops, that's really where we fit. We've made a bet on the future of Kubernetes and what that's going to look like. And so from a containers and Kubernetes standpoint that's our bet, but we're an AI Ops platform, we'll continue getting better at the problems we solve with machine learning and we'll continue adding data inputs. So we'll go beyond the application layer, which is really where we play now. We'll add kind of whole cluster optimization capabilities across the full stack. And the way we will get there is by continuing to add different data inputs that make sense across the different layers of the stack. And it's exciting. We can stay vertically oriented on the problems that we're really good at solving but we can become more applicable and compatible over time. >> So that's your next concentric circle. As the observability vendors expand their observation space, you can just play right into that? More data you get because your purpose built to solving these types of problems. >> Yeah. So you can imagine a world right now out of observability, we're taking things like telemetry data. Pretty quickly you can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow. It just feeds our own, we are reliant on data. >> Excellent. Matt, thank you so much. Appreciate you coming on. >> Thanks for having me. >> Okay. Keep it right there. In a moment, we're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier. They went through a major replatforming with Kubernetes on AWS. You're watching The CUBE, your leader in enterprise tech coverage. (upbeat music)

[Music] the adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise i.t survey data from enterprise technology research shows kubernetes specifically leads the pack in both spending velocity and market share now like virtualization in its early days containers bring many new performance and tuning challenges in particular ensuring consistent and predictable application performance is tricky especially because containers they're so flexible and they enable portability things are constantly changing devops pros have to wade through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error this endless cycle taxes resources and kills operational efficiency so teams often just capitulate and simply dial up and throw unnecessary resources at the problem stormforge is a company founded mid last decade that is attacking these issues with a combination of machine learning and data analysis and with me to talk about a new offering that directly addresses these concerns is matt provo founder and ceo of stormforge matt welcome to the cube good to see you good to see you thanks for having me yeah so we saw you guys at a kubecon sort of first introduce you to our community but add a little color to my intro there yeah well you semi stole my thunder but uh i'm okay with that uh absolutely agree with everything you said in the intro um you know the the problem that we have set out to solve which is tailor-made for the use of real machine learning not machine learning kind of as a as a marketing tag uh is is connected to how workloads on kubernetes are are really managed from a resource efficiency standpoint and so a number of years ago we built uh the the core machine learning engine and have now turned that into a platform around how kubernetes resources are managed at scale and so organizations today as they're moving more workloads over uh sort of drink the kool-aid of the flexibility that comes with kubernetes and how many knobs you can turn and developers in many many ways love it once they start to operationalize the use of kubernetes and move uh workloads from pre-production into production they run into a pretty significant complexity wall and and this is where stormforge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it so you've got news yeah hard launch coming and to further address these problems tell us about that yeah so historically um uh you know like any machine learning engine we think about data inputs and what kind of data is going to feed our our system to be able to draw the appropriate insights out out for the user and so historically we are we've kind of been single threaded on load and performance tests in a pre-production environment and there's been a lot of adoption of that a lot of excitement around it and and frankly amazing results my vision has been uh for us to be able to close the loop however between uh data coming out of pre-production and opt in the associated optimizations and data coming out of production a production environment uh and and our ability to optimize that a lot of our users along the way have have said these results in pre-production are are fantastic how do i know they reflect reality of what my application is going to experience in a production environment and so we're super excited to to announce kind of the second core module for our platform called optimizelive the data input for that is uh observability and telemetry data coming out of apm platforms and and other data sources so this is like nirvana so i wonder if we could talk a little bit more about the the challenges that this address is i mean i've been around a while and it really have observed and i used to ask you know technology companies all the time okay so you're telling me beforehand what the optimal configuration should be and resource allocation what happens if something changes yeah and then it's always always a pause yeah and kubernetes is more of a rapidly changing environment than anything we've ever seen yeah so this is specifically the problem you're addressing maybe talk about that yeah so we view what happens in pre-production as sort of the experimentation phase and our machine learning is is allowing the user to experiment and design and scenario plan what we're doing uh with optimize live and adding the the production piece is uh what we kind of also call kind of our observation phase and so you need to be able to to to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance from a cost standpoint is aligning with your slos and your slas as well as your business objectives and so that's the entire point of of this edition is to is to allow our users uh to experience uh hopefully the nirvana associated with that because it's an exciting er it's an exciting opportunity for them and really something that uh nobody else is doing from the standpoint of of closing that loop so you said upfront machine learning not as a marketing tag so i want you to sort of double click on that what's different than how other companies approach this problem yeah i mean part of it is a bias for me and a frustration as a founder of of the reason i started the company in the first place i think machine learning or ai gets tagged to a lot of stuff it's very buzz wordy it's it looks good i'm fortunate to have found a number of folks from the outset of the company with you know phds in applied mathematics and a focus on actually building real ai at the core uh that is connected to solving the right kind of actual business problems and so you know for the first three or four years of the company's history we really operated as a lab and that was our our focus we were we then decided we're trying to connect a fantastic team with differentiated technology to the right market timing and when we saw all these pain points around how fast the adoption of containers and kubernetes have taken place but the pain that the developers are running into we found it we actually found for ourselves uh that this was the perfect use case so how specifically does optimize live work can you add a little detail on that yeah so when you um many organizations today have an existing monitoring apm observability suite really in in place they've also got they've also got a metric source so this could be something like datadog or prometheus and once that data starts flowing there's an out of the box or or kind of a piece of kubernetes that ships with it called the vpa or the vertical pod auto scaler and uh less than really less than one percent of kubernetes users take advantage of the of the vpa mostly because it's really challenging to configure and it's not super compatible with the the tool set or the eco you know the ecosystem of tools uh in a kubernetes environment and so our biggest competitor is the vpa and what's happening in this environment or in in this world for developers is they're having to make decisions on on a number of different metrics or or resource elements typically things like memory and cpu and they have to decide what are the what are the limitations what are the requests i'm going to allow for this uh application and what are the limits so what are those thresholds that i'm going to be okay with so that i can again try to hit my business objectives and keep in line with my slas and to your earlier point in the intro it's often guesswork um you know they either have to rely on out of the box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this and so with optimize live we're making that one click and so we're continuously and consistently uh observing and watching the data that's flowing through these tools and we're serving back recommendations for the user they can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over the recommendations and manually deploy them into their environment themselves and we again really believe that the the user knows their application they know their the goals that they have we don't uh but we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations so the business objectives are an input from the application team yeah and then your system is smart enough to adapt and address those application over application right and and so the the thresholds in any given organization across their different ecosystem of apps or environment could be different the business objectives could be different and so we don't want to predefine that for people we want to give them the opportunity to build those thresholds in and then allow the machine learning to uh to learn and to send recommendations within those bounds and we're going to hear later from a customer who's uh hosting a drupal one of the largest drupal hosts so it's all do-it-yourself across thousands of customers so it's you know very unpredictable i want to make something clear though as to where you fit in the ecosystem you're not an observability platform you leverage observability platforms right so talk about that and where you fit in into the ecosystem yeah so that's a great point um we uh we're also you know a series b startup and and growing where we've made the choice to be very intentionally focused on the problems that we've solved and we've uh chosen to partner or integrate otherwise and so we do get put into the apm category from from time to time we're really an intelligence platform and that intelligence and insights that we're able to draw is because we because of the core machine learning we've built over the years and we also don't want organizations or users to have to switch from tools and investments that they've already made and so we were never going to we were never going to catch up to to to datadog or dynatrace or or splunk or app dynamics or some of the other and and we're totally fine with that they've got great market share and penetration they they do solve real problems instead we felt like users would want a seamless integration uh into the the tools they're already using and so we we we view ourselves as uh kind of the intel inside uh for that kind of a scenario and uh it takes observability and apm data and insights that were somewhat reactive uh they're visualized and somewhat reactive and and we make those uh we add that we add that proactive nature onto it the insights and and ultimately the the appropriate level of automation so when i think matt about cloud native and i go back to the sort of origins of cncf it was a handful of companies and now you look at the participants it'll you know make your eyes bleed how do you address dealing with all those companies and what are the what's the partnership strategy yeah it's so interesting because um it's just that even that cncf landscape has exploded um it was not too long ago where it was as small or smaller than the finnops landscape today which by the way the phenops pieces is also on a neck-breaking you know growth curve we i do see although there are a lot of companies and a lot of tools we're starting to see a significant amount of consistency or hardening of the tool chain uh you know for with our customers and end users and so we've made strategic and intentional decisions on deep partnerships in some cases like oem uh uses of our technology and and certainly you know intelligent and seamless integrations uh into a few so you know we're we'll be announcing uh a really exciting partnership with aws uh and and uh specifically what they're doing with eks their their kubernetes distribution and services we've got a deep partnership and integration with datadog and then with prometheus and specifically cloud provider a few other cloud providers that are operating managed prometheus environments okay so where do you want to take this thing it's not you're not taking the observability guys head on smart move so many of those even entering the market now but what is the vision yeah so we've had this debate a lot as well because it's super difficult to create a category uh you know on one hand um you know you know i have a lot of respect for founders and and companies that do that on the other hand um from a market timing standpoint you know we fit into ai ops that's really where we fit um you know we are we've made a bet on the future of kubernetes uh and and what that's going to look like and so um from a containers and kubernetes standpoint that's our bet uh but we're an aiops platform you know we'll continue getting better at what at the problems we solve with machine learning and we'll continue adding data inputs so we'll go you know we'll go beyond the application layer which is really where we play now we'll add kind of whole cluster optimization capabilities across across the full stack and the way we'll get there is by continuing to add different data inputs that make sense across the different layers of the stack and it's exciting we can stay vertically oriented on the problems that we're really good at solving but we can become more applicable and compatible over time so that's your next concentric circle as the observability vendors expand their observation space you can just play right into that yeah more data you get because you're a purpose built to solving these types of problems yeah so you can imagine a world right now out of observability we're taking things like telemetry data pretty quickly you can imagine a world where we take traces and logs and other data inputs as as that ecosystem continues to grow it just feeds our own uh you know we are reliant on data um so excellent matt thank you so much appreciate you for having me okay keep it right there in a moment we're gonna hear from a customer with a highly diverse and constantly changing environment that i mentioned earlier they went through a major re-platforming with kubernetes on aws you're watching thecube your leader in enterprise tech coverage [Music] you

(bright upbeat music) >> The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from Enterprise Technology Research shows Kubernetes specifically leads the pack in both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges. In particular, ensuring consistent and predictable application performance is tricky especially because containers they're so flexible and the enabled portability things are constantly changing. DevOps pros have to wade through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes, resources, and kills operational efficiencies so teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded in mid last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns, is Matt Provo, founder and CEO of StormForge. Matt, welcome to thecube. Good to see you. >> Good to see you, thanks for having me. >> Yeah. So we saw you guys at CubeCon, sort of first introduce you to our community but add a little color to my intro if you will. >> Yeah, well you semi stole my thunder but I'm okay with that. Absolutely agree with everything you said in the intro. You know, the problem that we have set out to solve which is tailor made for the use of real machine learning not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so a number of years ago we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today as they're moving more workloads over sort of drink the Kool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn and developers in many ways love it. Once they start to operationalize the use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it. >> So you've got news, your hard launch coming in to further address these problems. Tell us about that. >> Yeah so historically, you know, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we are, we've kind of been single-threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly, amazing results. My vision has been for us to be able to close the loop however between data coming out of pre-production and the associated optimizations and data coming out of production, a production environment, and our ability to optimize that. A lot of our users along the way have said these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce kind of the second core module for our platform called Optimize Live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. >> So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed and I used to ask technology companies all the time, okay, so you're telling me beforehand what the optimal configuration should be in resource allocation, what happens if something changes? And then it's always a pause. And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So this is specifically the problem you're addressing. Maybe talk about that a little bit. >> Yeah so we view what happens in pre-production as sort of the experimentation phase and our machine learning is allowing the user to experiment and scenario plan. What we're doing with Optimize Live and adding the production piece is what we kind of also call kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance, from a cost standpoint, is aligning with your SLOs and your SLAs as well as your business objectives. And so that's the entire point of this addition is to allow our users to experience hopefully the Nirvana associated with that because it's an exciting opportunity for them and really something that nobody else is doing from the standpoint of closing that loop. >> So you said upfront machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? >> Yeah I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning our AI gets tagged to a lot of stuff. It's very buzzwordy, it looks good. I'm fortunate to have found a number of folks from the outset of the company with, you know, PhDs in Applied Mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so, you know, for the first three or four years of the company's history, we really operated as a lab and that was our focus. We then decided we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all of these pain points around how fast the adoption of containers and Kubernetes have taken place but the pain that the developers are running into, we found it, we actually found for ourselves that this was the perfect use case. >> So how specifically does Optimize Live work? Can you add a little detail on that? >> Yeah so when you, many organizations today have an existing monitoring APM observability suite really in place. They've also got, they've also got a metric source, so this could be something like Datadog or Prometheus. And once that data starts flowing, there's an out of the box or kind of a piece of Kubernetes that ships with it called the VPA or the Vertical Pod Autoscaler. And less than really less than 1% of Kubernetes users take advantage of the VPA mostly because it's really challenging to configure and it's not super compatible with the tool set or the, you know, the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this environment or in this world for developers is they're having to make decisions on a number of different metrics or resource elements typically things like memory and CPU. And they have to decide what are the, what are the requests I'm going to allow for this application and what are the limits? So what are those thresholds that I'm going to be okay with? So that I can again try to hit my business objectives and keep in line with my SLAs. And to your earlier point in the intro, it's often guesswork. You know, they either have to rely on out of the box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this. And so with Optimize Live, we're making that one-click. And so we're continuously and consistently observing and watching the data that's flowing through these tools and we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over the recommendations and manually deploy them into their environment themselves. And we again, really believe that the user knows their application, they know the goals that they have, we don't. But we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. >> So the business objectives are an input from the application team and then your system is smart enough to adapt and adjust those. >> Application over application, right? And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to predefine that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. >> And we're going to hear later from a customer who is hosting a Drupal, one of the largest Drupal host, is it? So it's all do it yourself across thousands of customers so it's very unpredictable. I want to make something clear though, as to where you fit in the ecosystem. You're not an observability platform, you leverage observability platforms, right? So talk about that and where you fit in into the ecosystem. >> Yeah so it's a great point. We, we're also you know, a series B startup and growing. We've made the choice to be very intentionally focused on the problems that we've solve and we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We're really an intelligence platform. And that intelligence and insights that we're able to draw is because we, because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to Datadog or Dynatrace or Splunk or AppDynamics or some of the other, and we're totally fine with that. They've got great market share and penetration and they do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the Intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive, they're visualized and somewhat reactive and we make those, we add that proactive nature onto it, the insights and ultimately the appropriate level of automation. >> So when I think Matt about cloud native and I go back to the sort of origins of CNCF, it was a, you know, handful of companies, and now you look at the participants, you know, make your eyes bleed. How do you address dealing with all those companies and what's the partnership strategy? >> Yeah it's so interesting because it's just that even at CNCF landscape has exploded. It was not too long ago where it was as smaller than the finOps Landscape today which by the way the FinOps pieces is also on a neck breaking, you know, growth curve. We, I do see although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain with our customers and users. And so we've made strategic and intentional decisions on deep partnerships in some cases like OEM users of our technology and certainly, you know, intelligent and seamless integrations into a few. So, you know, we'll be announcing a really exciting partnership with AWS and specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus and specifically cloud provider, a few other cloud providers that are operating manage Prometheus environments. >> Okay so where do you want to take this thing? If it's not, you're not taking the observability guys head on, smart move, so many of those even entering the market now, but what is the vision? >> Yeah so we've had this debate a lot as well because it's super difficult to create a category. You know, on one hand, I have a lot of respect for founders and companies that do that, on the other hand from a market timing standpoint, you know, we fit into AIOps. That's really where we fit. You know we are, we've made a bet on the future of Kubernetes and what that's going to look like. And so from a containers and Kubernetes standpoint that's our bet. But we're an AIOps platform, we'll continue getting better at what, at the problems we solve with machine learning and we'll continue adding data inputs so we'll go beyond the application layer which is really where we play now. We'll add kind of whole cluster optimization capabilities across the full stack. And the way we'll get there is by continuing to add different data inputs that make sense across the different layers of the stack and it's exciting. We can stay vertically oriented on the problems that we're really good at solving but we become more applicable and compatible over time. >> So that's your next concentric circle. As the observability vendors expand their observation space you can just play right into that. The more data you get could be because you're purpose built to solving these types of problems. >> Yeah so you can imagine a world right now out of observability, we're taking things like telemetry data pretty quickly. You can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow, it just feeds our own, you know, we are reliant on data. So. >> Excellent. Matt, thank you so much. Thanks for hoping on. >> Yeah, appreciate it. >> Okay. Keep it right there. In a moment, We're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier, they went through a major re-platforming with Kubernetes on AWS. You're watching theCube, your a leader in enterprise tech coverage. (bright music)

>> Welcome back to Los Angeles. The cube is live. It feels so good to say that. I'm going to say that again. The cube is alive in Los Angeles. We are a coop con cloud native con 21. Lisa Martin with Dave Nicholson. We're talking to storm forge next. Cool name, right? We're going to get to the bottom of that. Please welcome Matt Provo, the founder and CEO of storm forge and Tom Ellery, the SVP of revenue storm forge, guys, welcome to the program. Thanks for having us. So storm forge, you have to say it like that. Like I feel like do you guys wear Storm trooper outfits on Halloween. >> Sometimes Storm trooper? The colors are black. You know, we hit anvils from time to time. >> I thought I, I thought they, that I saw >> Or may not be a heavy metal band that might be infringing on our name. It's all good. That's where we come from. >> I see. So you, so you started the company in 2015. Talk to me about the Genesis of the company. What were some of the gaps in the market that you saw that said we got to come in here and solve this? >> Yeah, so I was fortunate to always know. I think when you start a company, sometimes you, you know exactly the set of problems that you want to go after and potentially why you might be uniquely set up to solve it. What we knew at the beginning was we had a number of really talented data scientists. I was frustrated by the buzzwords around AI and machine learning when under the hood, this really a lot of vaporware. And so at the outset, really the, the point was build something real at the core, connect that to a set of problems that could drive value. And when we looked at really the beginnings of Kubernetes and containerization five, six years ago at its Genesis, we saw just a bunch of opportunity for machine learning, to play the right kind of role if we could build it correctly. And so at the outset it was what's going on. Why are people are people moving content workloads over to containers in the first place? And, you know, because of the flexibility and the portability around Kubernetes, we then ran into quickly its complexity. And within that complexity was really the foundation to set up the company and the solution for prob a set of problems uniquely and most beneficially solved by using machine learning. And so when we sort of brought that together and designed out some ideas, we, we did what any, any founder with a product background would do. We went and talked to a bunch of potential users and kind of tried to validate the problems themselves and, and got a really positive response. So. >> So Tom, from a business perspective, what, what attracted you to this? >> Well, initially I wasn't attracted just, I'll say that just from a startup standpoint. So I've been in the industry for 30 years, I've done six or seven pre IPO companies. I was exiting a private company. I did not want to go do another startup company, but being in the largest enterprise companies for the last 20 years, you see Kubernetes like wildfire in these places. And you knew there was huge amount of complexity and sophistication when they deployed it. So I started talking to Matt early on. He explained what they were doing and how unique the offer was around machine learning. I already knew the problems that customers had at scale with Kubernetes. So it was for me, I said, all right, I'm going to take one more run at this with Matt. I think we're, we're in a great position to differentiate ourselves. So that was really the launch pad for me, was really the technology and the market space. Those, those two things in combination are very exciting for us as a business. >> And, you know, a couple of bottles of amazing wine and a number of dinners that. >> Helps as well. >> That definitely helped twist his arm? >> Now tell us, just really kind of get into the technology. What does it do? How does it help facilitate the Kubernetes environment? >> Yeah, absolutely. So when organizations start moving workloads over to Kubernetes and get their applications up and running, there's a number of amazing organizations, whether it's through cloud providers or otherwise that that sort of solved that day one problem, those challenges. And as I was mentioning, you know, they moved because of flexibility and so developers love it and it starts to create a great experience, but there's these set of expectations. >> Where, where typically are these moving from? What you, what, what are the, what are the top three environments these are, that these are moving out of? >> Yeah. I mean, of course, non containerized environments, more generally. They could be coming from, you know, bare metal environment and it could be coming from kind of a VM driven environment. >> Okay. >> So when you look back at kind of the, the growth and Genesis and of VMs, you see a lot of parallels to what we're seeing now with, with containerization. And so as you move, it's, it's exciting. And then you get smacked in the face with the complexity, for all of the knobs that are able to be turned within a Kubernetes environment. It gives developers a lot of flexibility. These knobs, as you turn them, you have no visibility into how into the impact on the application itself. And so often organizations are become, you know, becoming more agile shipping, you know, shipping code more quickly, but then all of a sudden the, the cloud bill comes and they've, over-provisioned by 80, 90%, the, they didn't need nearly as many resources. And so what we do is we help understand the unique goals and requirements for each of the applications that are running in Kubernetes. And we have machine learning capabilities that can predict very accurately what organizations will need from a resource standpoint, in order to meet their goals, not just from a cost standpoint, but also from a performance standpoint. And so we allow organizations to typically save usually between 40 and 60% off their cloud bill and usually increased performance between 30 and 50%. Historically developers had to choose between cost and performance and their worldview on the application environment was very limited to a small set of what we would call parameters or metrics that they could choose from. And machine learning allows that world to just be blown open and not many humans are, are sophisticated in the way we think about multidimensional math to be able to make those kinds of predictions. You're talking about billions and billions of combinations, not just in a static environment, but an ongoing basis. So our technology sits in the middle of all that chaos and, and allows it to allows organizations just to re reap a whole lot of benefits that they otherwise may not ever find. >> Those numbers that you mentioned were, were big from a cost savings perspective than a performance increased perspective, which is so critical these days is in the last 18 months, we've seen so much change. We've seen massive pivots from companies in every industry to survive first of all, and then to be able to thrive and be able to iterate quickly enough to develop new products and services and get them to market to be competitive. >> Yeah. >> Yeah. Sorry. I mean, the thing that's interesting, there was an article by Andreessen Horowitz. I don't know if you've taken to the cloud paradox. So we actually, if you start looking at that great example would be some of these cloud companies that are growing like astronomical rates, snowflakes, like phenomenal what they're doing, but go look at their cogs and what it's doing. Also, it's growing almost proportionately as the revenues growing. So you need to be able to solve that problem in a way that is sophisticated enough with machine learning algorithms, that people don't have to be in the loop to do it. And that the math can prove out the solution as you go out and scale your environments. And a lot of companies now are all transitioning over SAS based platforms, and they're going to start running into these problems that they go as they go to scale. And those are the areas that we're really focused and concentrating on as an organization. >> As the leader of sales, talk to me about the voice of the customer. What are some- you've been there six months or so we heard, we heard about the wine and the dinners is obvious. >> We haven't done a lot of that over the last 18 months. >> You'll have to make for lost time then >> As soon as he closes more business. >> Oh, oh there we go, we got that on camera! >> There's, there's been three, a market spaces that we've had some really good success in that. So we talked about a SAS marketplace. So there's a company that does Drupal and Matt knows very well up in Boston, Aquia. And they have every customer is a unique snowflake customer. So they need to optimize each of their customers in order to ensure the cost as well as performance for that customer on their site works appropriately. So that's one example of a SAS based company that where we can go in and help them optimize without humans doing the optimization and the math and the machine learning from storm forge doing that. So that's an area, the other area that we've seen some really good traction Cantonese with GSI. So part of our go to market model is with GSI. So if you think about what a GSI does, a lot of times customers are struggling either initially deploying Kubernetes or putting it in for 12, 18 months and realizing we're starting to scale, we got all kinds of performance issues. How do I solve it? A lot of these people go to the Accentures, the cognizance and other ones, and start flying their ninjas into kind of solve the problem. So we're getting a lot of traction with them because they're using our tool as a way to help solve the customer's problems. And they're in the largest enterprise customers as possible. >> So if I'm hearing what you're saying correctly, you're saying that when I deploy server less applications, I may in fact, get a bill for servers that are being used? Is it, is that what you're telling us? >> They're there in fact may be a bill for what was coined as server less. That is very difficult to understand, by the way, >> That's crazy talk, Matt. >> And connect back. >> Yeah. But absolutely we deal with that all the time. It's a, it's a painful process from time to time. >> Have you, have you, have you seen the statistics that's going on with how people, I mean, there was huge inertia from every CIO that you had have a cloud strategy in place. Everyone ran out and had a cloud strategy in place. And then they started deploying on Kubernetes. Now they're realizing, oh wow, we can run it, but it's costing us more than it ever costs us on prem and the operational complexity associated with that. So there's not enough people in the industry to help solve that problem, especially at the grass roots, that's where you need sophisticated solutions like storm forge and machine learning to help solve this at scale problem in a way that humans could never solve. >> And I would, I would just add to that, that the, the same humans managing the Kubernetes application environments today are likely the same humans that we're managing it in a, in a BM world. So there's a huge skills gap. I love what Castin announced at KU KU con this year around their learning environment where it's free. Come learn Kubernetes and this, and we need more of that. There's an enormous skills gap and, and the problems are complex enough in and of themselves. But when we have, when you add that to the skills gap, it it's, it presents a lot of challenges for organizations. >> What are some the ways in which you think that gap can start to be made smaller. >> Yeah. I mean, I think as more workloads get moved over, over, you know, over time, you see, you see more and more people becoming comfortable in an environment where scale is a part of what they have to manage and take care of. I love what the Linux foundation and the CNCF are doing around Kubernetes certifications, you know, more and more training. I think you're going to see training, you know, availability for more and more developers and practitioners be adopted more widely. You know, and I think that, you know, as the tool chain itself hardens within a CCD world in a containerized world, as that hardens, you're going to, you're going to start seeing more and more individuals who are comfortable across all these different tools. If you look at the CNCF landscape, I mean, today compared to four or five years ago, it's growing like crazy. And so, but, but there's also consolidation taking place within the tools. And people have an opportunity to, to learn and gain expertise within us. Which is very marketable by the way, >> Absolutely >> My employees often show me their LinkedIn profiles and remind me of how , how much they're getting recruited, but they've been loyal. So it's been a fantastic. >> Are there are so many parallels when you look at a VM in virtualization and what's happening with covers, obviously all the abstractions and stuff, but there was this whole concept of VM sprawl, you know, maybe 10 years in, if you think about the Kubernetes environment, that is exponentially bigger problem because of how many they're spitting up versus how, how many you spun up in VM. So those things ultimately need to be solved. It's not just going to be solved with people. It needs to be solved with sophisticated software. That's the only way you're going to solve a problem at scale like that. No matter how many people you have in the industry, it's just never going to solve the problem. >> So when you're in customer conversations, Tom, what are you say are like the top three differentiators that really set storm forage apart? >> Well, so the first one is we're very focused on Kubernetes only. So that's all we do is just Kubernetes environment. So we understand not just the applications that run in Kubernetes, but we understand the underlying architectures and techniques, which we think is really important. From a solution standpoint, >> So you're specialists? >> We are absolutely specialists. The other areas obviously are machine learning and the sophistication of our machine learning. And Matt said this really well, early on, I mean, the buzzwords are all out there. You can read them all up, all over the place for the last five to seven year AI and ML. And a lot of them are very hollow, but our whole foundation was based on machine learning and PhDs from Harvard. That's where we came out of from a technology background. So we were solving more, we weren't just solving the Kubernetes problems. We were solving machine learning problems. And so that's another really big area of differential for us. And I think the ability to actually scale and not just deal with small problems, but very large problems, because our focus is the fortune 2000 companies. And most of them have been deploying like financial services and stuff, Kubernetes for three, four or five years. And so they have had scale challenges that they're trying to solve. >> Yeah. It's Lisa and I talk about this concept of machine learning and looking under the covers and trying to find out is the machine really learning? Is it really learning or is it people are telling the machine, you need to do this. If you see that Where's the machine actually making those correlations and doing something intelligently. So can you give us an example of something that is actually happening that's intelligent? >> Well, so the, the, if this, then that problem is actually a huge source of my original frustration for starting the company, because you, you, you tag AI as a buzzword onto a lot of stuff. And we see that growing like crazy. And so I literally at the beginning said, if we can't actually build something real, that solves problems, like we're going to hang it up. And, you know, as Tom said, we came out of Harvard and, you know, there was a challenge initially of, are we just going to build like a really amazing algorithm? That's so heavy, it can never be productized or commercialized and it really should have just stayed in academia. And, you know, I the I, I will say a couple of things. One is I do not believe that that black box AI is a thing. We believe in what we would call human, augmented AI. So we want to empower practitioners and developers into the process instead of automate them out. We just want to give them the information and we want to save time for them and make their lives easier. But there's a kill switch on the technology. They can intervene at any point in time. They can direct the technology as they see fit. And what's really, really interesting is because their worldview of this application environment gets opened up by all the predictions and all of the learning that actually is taking place and, you know, give it because that worldview is open, they then get into a kind of a tinkering or experimental mindset with the technology. And they start thinking about all these other scenarios that they never were able to explore previously with the application. And, and so the machine learning itself is on an ongoing basis. Understanding changes in traffic, understanding and changes, changes in workloads for the application or demand. If you thought about like surge pricing for Uber, you know, because of a, a big game that took place. And you know, that, that change in peaks and valleys in demand, our, our technology not only understands those reactively, but it starts to build models and predict proactively in advance of the events that are going to take place on, on what ne- what kind of resources need to be allocated. And why that's the other piece around it is often solutions are giving you a little bit of a what, but they certainly are not giving you any explanation of the why. So the holy grail really like in our world is kind of truly explainable AI, which we're not there yet. Nobody's there yet. But human augmented AI with, with actual intelligence that's taking place that also is relevant to business outcomes is, is pretty exciting. So that's why where try to operate. >> Very exciting guys. Thanks for joining us, talking to us about storm forage, to feel like we need some store in forge. T-shirts what do you think? >> (unintelligible) >> See, I'm not even asking for the bottle of wine. I liked that idea. I thank Matt and Tom, thank you so much for joining us exciting company. Congratulations on your success. And we look forward to seeing what great things are to come from storm forage. >> Thanks so much for the time. >> Our pleasure. For Dave Nicholson. I'm Lisa Martin. We are alive in Los Angeles, the cube covering Kube con and cloud native con 21 stick around. Dave and I will be right back with our next guest.

(upbeat music) >> Hello, welcome back to this cube conversation, on cube on cloud startups. I'm John Furrier host of theCUBE. We're wrapping up the closing keynote fireside chat of the AWS showcase, the hottest startups in data and cloud. We've got some great guests here to eluminate what's happened and why it's important. And Michael Skok who's the founding partner, Michael Skok founding partner of Underscore VC, Mike Feinstein, principal business development manager, and the best Ben Haynes CIO advisor Lincoln Center for the Performing Arts. Gentlemen, thank you for joining me for this closing keynote for the AWS showcase. >> Pleasure to be here. >> So, first of all-- >> Happy to be here >> Guys, do you guys have a unique background from startup funding, growing companies, managing these partners at AWS and being a practitioner with Ben here. The first question I have is, what is the real market opportunity? We've heard from McKinsey that there's a trillion dollars of unlocked value in cloud and that really is going to come from all enterprises big and small. So the question is that that's what every wants to know. What's the secret answer key to the to the test if you are a business. 'Cause you don't want to be on the wrong side of cloud history here. There is a playbook, there's some formation of patterns and there's some playbook things happening out there. How do you guys see this? >> Well, I can try to take a crack at that. First of all I think, there's not only one playbook, you know, only one recipe. If it's a trillion dollar opportunity, that's in the aggregate. There's many different types of opportunities. I think you could have existing companies that are maybe older line companies that need to change the way they're doing things. You can have the younger companies that are trying to take advantage of all the data they've already collected and try to get more value out of it. There could be some radically different types of opportunities with newer technology. I think, you know, for each company just like each of the companies here at the showcase today, they are targeting some, you know, segment of this. Each of those segments is already large. And I think you're going to see a wide range of solutions taking hold here. >> Yeah, cloud drives a lot of value. Michael, I want to get your thoughts. You know, you've seen the software revolution you know, over the years. This time it seems to be accelerated, the time to value, if you're a startup. I mean, you couldn't ask for the perfect storm for our innovation if you're coming out of MIT, Stanford, any college. If you're not even going to school you can get in cloud, do anything. Starting software now is not as hard as it was or its different. What's your perspective because you know, these companies are adding treated value and they're going into an enterprise market that wants scale, they want the reliability. How do you see this evolving? >> You know, the very first time I saw Bezos get on stage and pitch AWS he said one thing which is, "We take away all the hard stuff about starting a software business and let you focus on the innovation." And I think that's still applies. So you're dead right John. And honestly, most founders don't want to spend any time on anything other than unique piece of innovation that they're going to deliver for their customers. So, I think that is fabulous news. I'm going to joke for a second, so I think we're all under shooting on this number. I mean, the reality is that every part of compute infrastructure that we talk about today was built from an infrastructure that's you know, decades old. By which I mean 30 to 50 decades in some 30 to 50 years in some cases. And we look forward in 30 to 50 years, we won't be talking about cloud or everything else. We'll be just talking about computing or whatever it is that we want to talk about at the edge. Or the application of data that you know, in a car and an ARVR heads up display that's helping surgeons work across the world. The fact is the only way this is really going to work is on the cloud. So I think it's a multi-trillion dollar opportunity, we're just taking a snapshot of it right now. And we're in an interesting point because of course digital transformation has been rapidly accelerated. I mean, there's all these jokes about you know, we've had five years of transformation in five months. I don't really care what the number is but what is obvious is that we couldn't have gone off to work and to play and to teach and all these other things without the cloud. And we just took it for granted but a year ago, that's what we all did and look, they're thriving. This whole thing is that, you know, a live broadcast that we're doing on the cloud. So yeah, I think it's a very big opportunity and whatever sector I think to Mike's point, that you look at and all the companies that you've seen this morning prove that, if you want to innovate today, you start on the cloud. Your cloud native as I would say. And as you grow, you will be a cloud assumed. It will be the basis on which everybody wants to access your products and services. So I'm excited about the future if you can't tell. >> I totally subscribe to that. Ben, I want to get your take as the CIO, now advisor to companies. If you're going to look at what Michael's laying out, which is born in the cloud native, they have an advantage, an inherent advantage right out of the gate. They have speed agility and scale. If you're an existing business you say, "Wait a minute I'm going to be competed against these hot startups." There's some serious fear of missing out and fear of getting screwed, right? I mean, you might go out of business. So this is the real threat. This is not just talked about, there's real examples now playing out. So as a practitioner, thinking about re-architecting or rejuvenating or pivoting or just being competitive. It's really the pressure's there. How do you see this? >> Yeah I know it really is. And every enterprise company and through every decade is it's a buyer versus build conversation. And with the cloud opportunities, you can actually build a lot quicker or you can leverage companies that can even go quicker than you that have a focus on innovation. 'Cause sometimes enterprise companies, it's hard to focus on the really cool stuff and that's going to bring value but maybe it won't. So if you can partner with someone and some of these companies that you just showcase, start doing some amazing things. That can actually help accelerate your own internal innovation a lot quicker than trying to spool up your own team. >> We heard some companies talking about day two operations lift and shift, not a layup either. I mean, lift and shift if not done properly as it's well discussed. And McKinsey actually puts that in their report as there's other point outs. It's not a no brainer. I mean, it's a no brainer to go to the cloud but if you lift and shift without really thinking it through or remediating anything, it could be, it could cost more. And you got the CAPEX and OPEX dynamics. So, certainly cloud is happening and this kind of gives a great segue into our next topic that I'd love to get you guys to weigh in on. And that is the business model, the business structure, business organization. Michael you brought up some interesting topics around, some of the new ideas that could be, you know, decentralized or just different consumption capabilities on both sides of the equation. So, the market's there, trillions and trillions of dollars are shifting and the spoils will go to the ones who are smart and agile and fast. But the business model, you could have it, you could be in the right market, but the wrong business model. Who wants to take the first cut at that? >> Mike do you want to go? >> Sure, I'd be happy to. I think that, you know, I mean again, there's not there only going to be one answer but I think one of the things that really make sense is that the business models can be much more consumption-based. You're certainly not going to see annual software licenses that you saw in the old world. Things are going to be much more consumption-based obviously software is a service type of models. And you're going to see, I think lots of different innovations. I've also seen a lot of companies that are starting up kind of based on open source as like a first foray. So there's an open source project that really catches hold. And then a company comes up behind it to both enhance it and to also provide support and to make it a real enterprise offering. But they get there early quick adoption of the frontline engineers by starting off with an open source project. And that's a model that I've seen work quite well. And I think it's a very interesting one. So, you know, the most important thing is that the business model has to be one that's as flexible as what the solutions are that you're trying to get the customers to adopt. The old way of everything being kind of locked in and rigid isn't going to work in this world 'cause you have to just really be agile. >> I want to come back to you Mike in a second on this 'cause I know Amazon's got some innovative go to market stuff. Michael you've written about this, I've read many blog posts on your side about SaaS piece. What's your take on business structure. I mean, obviously with remote, it's clear people are recognizing virtual companies are available. You mentioned you know, edge and compute, and these new app, these emerging technologies. Does the business structure and models shift? Do you have to be on certain side of this business model innovation? How do you view? 'Cause you're seeing the startups who are usually crazy at first, but then they become correct at the end of the day. What's your take? >> Well first of all, I love this debate because it's over. We used to have things that were not successful that would become shelfware. And that just doesn't work in the cloud. There is no shelfware. You're either live and being used or you're dead. So the great news about this is, it's very visible. You know, you can measure every person's connection to you for how long and what they're doing. And so the people that are smart, don't start with this question, the business model. They start with what am I actually doing for my user that's in value them? So I'll give you some examples like build on Mike's team. So, you know, I backed a company called Acquia. But it was based on an open source project called Drupal. Which was initially used for content management. Great, but people started building on it and over time, it became used for everything from the Olympics and hosting, you know, theirs to the Grammy's, to you know, pick your favorite consumer brand that was using it to host all of their different brands and being very particular about giving people the experiences. So, it's now a digital experience platform. But the reason that it grew successfully as a company is because on top of the open source project, we could see what people were doing. And so we built what in effect was the basis for them to get comfortable. By the way, Amazon is very fundamental partner in this was, became an investor extremely helpful. And again, took away all the heavy lifting so we could focus on the innovation. And so that's an example of what's going on. And the model there is very simple. People are paying for what they use to put that digital experience of that, to create a great customer journey. And for people to have the experience that obviously you know, makes the brand look good or makes the audience feel great if it's the Grammy's or whatever it is. So I think that's one example, but I'll give you two others because they are totally different. And one of the most recent investments we made is in a company called Coder. Which is a doc spelled backwards. and it's a new kind of doc that enables people to collaborate and to bring data and graphics and workflow and everything else, all into the simplicity of what's like opening up a doc. And they don't actually charge anybody who uses their docs. They just charge for people who make their docs. So its a make a best pricing, which is very interesting. They've got phenomenal metrics. I mean they're like over 140% net dollar retention, which is astoundingly good. And they grew over three and a half times last year. So that's another model, but it's consumer and it's, you know, as I said, make a price. And then, you know, another company we've been involved with if I look at it way back was Demand Web. It was the first e-commerce on demand company. We didn't charge for the software at all. We didn't charge for anything in fact. what we did was to take a percentage of the sales that went through the platform. And of course everybody loved that because, you know, if we were selling more or getting better uplift then everybody started to do very well. So, you know, the world's biggest brands moved online and started using our platform because they didn't want to create all that infrastructure. Another totally different model. And I could go on but the point is, if you start from the customer viewpoint like what are you doing for the customer? Are you helping them sell more? Or are you helping them build more effective business processes or better experiences? I think you've got a fantastic opportunity to build a great model in the cloud. >> Yeah, it's a great point. I think that's a great highlight also call out for expectations become the experience, as the old saying goes. If a customer sees value in something, you don't have to be tied to old ways of selling or pricing. And this brings up, Ben, I want to tie in you in here and maybe bring Mike back in. As an enterprise, it used to be the old adage of, well startups are unreliable, blah, blah, blah, you know, they got to get certified and enterprise usually do things more complicated than say consumer businesses. But now Amazon has all kinds of go to market. They have the marketplace, they have all kinds of the partner networks. This certification integration is a huge part of this. So back to, you know, Michael's point of, if you're dead you're dead or knows it, but if you're alive you usually have some momentum it's usually well understood, but then you have to integrate. So it has to be consumable for the enterprise. So Ben, how do you see that? Because at the end of the day, there's this desire for the better product and the better use case. That can, how do I procure it? Integration? These used to be really hard problems. Seems to be getting easier or are they? What's your take? >> Not 100%. I mean, even five years ago you would have to ask a lot of startups for a single sign on and as table stakes now. So the smart ones are understanding the enterprise principles that we need and a lot of it is around security. And then, they're building that from the start, from the start of their products. And so if you get out of that security hurdle, the stability so far is a lot more improved because they are, you know, a lot more focused and moving in a really, really quick way which can help companies, you know, move quickly. So definitely seen an improvement and there's still, the major entry point is credit card, small user base, small pricing, so you're not dealing with procurement. And building your way up into the big purchase model, right? And that model hasn't changed except the start is a lot lot quicker and a lot easier to get going. >> You know, I remember the story of the Amazon web stores, how they won the CIA contract is someone put a test on a credit card and IBM had the deal in their back pocket. They had the Ivory Tower sales call, Michael, you know the playbook on enterprise sales, you know, you got the oracles and you guys call it the top golf tournament smoothing and then you got the middle and then you got the bottoms up you got the, you know, the data dogs of the world who can just come in with freemium. So there's different approaches. How do you guys see that? Michael and Mike, I'd love for you to weigh in on this because this is really where there's no one answer, but depending upon the use case, there's certain motions that work better. Can you elaborate on which companies should pay attention to what and how customers should understand how they're buying? >> Yeah, I can go first on that. I think that first of all, with every customer it's going to be a little different situation, depends on the scale of the solution. But I find that, these very large kind of, you know, make a huge decision and buy some really big thing all at once. That's not happening very much anymore. As you said John, people are kind of building up it's either a grassroots adoption that then becomes an enterprise sale, or there is some trials or smaller deployments that then build up at enterprise sales. Companies can't make those huge mistake. So if they're going to make a big commitment it's based on confidence, that's come from earlier success. And one of the things that we do at AWS in addition to kind of helping enterprises choose the right technology partners, such as many of the companies here today. We also have solutions partners that can help them analyze the market and make the choice and help them implement it. So depending on the level of help that they need, there's lots of different resources that are going to be available to help them make the right choice the first time. >> Michael, your thoughts on this, because ecosystems are a part of the entire thing and partnering with Amazon or any cloud player, you need to be secure. You need to have all the certifications. But the end of the day, if it works, it works. And you can consume it whatever way you can. I mean, you can buy download through the marketplace. You can go direct, it's free. What do you see as the best mix of go to market from a cloud standpoint? Given that there's a variety of different use cases. >> Well, I'm going to play off Ben and Mike on this one and say, you know, there's a perfect example of what Ben brought up, which is single sign on. For some companies, if you don't have that you just can't get in the door. And at the other extreme to what Mike is saying, you know, there are reasons why people want to try stuff before they buy it. And so, you've got to find some way in between these two things to either partner with the right people that have the whole product solution to work with you. So, you know, if you don't have single sign on, you know, go work with Okta. And if you don't have all the certification that's needed well, work with AWS and you know, take it on that side of cash and have better security than anybody. So there's all sorts of ways to do this. But the bottom line is I think you got to be able to share value before you charge. And I'll give you two examples that are extreme in our portfolio, because I think it will show the sort of the edge with these two things. You know, the first one is a company called Popcart. It's been featured a lot in the press because when COVID hit, nobody could find whatever it was, that TP or you know, the latest supplies that they wanted. And so Popcart basically made it possible for people to say, "Okay, go track all the favorite suppliers." Whether it's your Walmarts or your Targets or your Amazons, et cetera. And they would come back and show you the best price and (indistinct) it cost you nothing. Once you started buying of course they were getting (indistinct) fees and they're transferring obviously values so everybody's doing well. It's a win-win, doesn't cost the consumer anything. So we love those strategies because, you know, whenever you can make value for people without costing them anything, that is great. The second one is the complete opposite. And again, it's an interesting example, you know, to Ben's point about how you have to work with existing solutions in some cases, or in some cases across more things to the cloud. So it's a company called Cloud Serum. It's also one we've partnered with AWS on. They basically help you save money as you use AWS. And it turns out that's important on the way in because you need to know how much it's going to cost to run what you're already doing off premises, sorry off the cloud, into the cloud. And secondly, when you move it there to optimize that spend so you don't suddenly find yourself in a situation where you can't afford to run the product or service. So simply put, you know, this is the future. We have to find ways to specifically make it easy again from the customer standpoint. The get value as quickly as possible and not to push them into anything that feels like, Oh my God, that's a big elephant of a risk that I don't obviously want to take on. >> Well, I'd like to ask the next question to Michael and Ben. This is about risk management from an enterprise perspective. And the reason Michael we just want to get you in here 'cause you do risk for living. You take risks, you venture out and put bets on horses if you will. You bet on the startups and the growing companies. So if I'm a customer and this is a thing that I'm seeing both in the public and private sector where partnerships are super critical. Especially in public right now. Public private partnerships, cybersecurity and data, huge initiatives. I saw General Keith Alexander talking about this, about his company and a variety of reliance on the private problem. No one winning formula anymore. Now as an enterprise, how do they up level their skill? How do you speak to enterprises who are watching and learning as they're taking the steps to be cloud native. They're training their people, they're trying to get their IT staff to be superpowers. They got to do all these. They got to rejuvenate, they got to innovate. So one of the things that they got to take in is new partnerships. How can an enterprise look at these 10 companies and others as partners? And how should the startups that are growing, become partners for the enterprise? Because if they can crack that code, some say that's the magical formula. Can you guys weigh in on that? (overlapping chatter) >> Look, the unfortunate starting point is that they need to have a serious commitment to wanting to change. And you're seeing a lot of that 'cause it is popping up now and they're all nodding their heads. But this needs people, it needs investment, and it needs to be super important, not just to prior, right? And some urgency. And with that behind you, you can find the right companies and start partnering to move things forward. A lot of companies don't understand their risk profile and we're still stuck in this you know, the old days of global network yet infiltrated, right? And that's sort of that its like, "Oh my God, we're done." And it's a lot more complicated now. And there needs to be a lot of education about the value of privacy and trust to our consumers. And once the executive team understands that then the investments follow. The challenge there is everyone's waiting, hoping that nothing goes wrong. When something goes wrong, oh, we better address that, right? And so how do we get ahead of that? And you need a very proactive CSO and CIO and CTO and all three if you have them really pushing this agenda and explaining what these risks are. >> Michael, your thoughts. Startups can be a great enabler for companies to change. They have their, you know, they're faster. They bring in new tech to the scenario scene. What's your analysis? >> Again, I'll use an example to speak to some of the things that Ben's talking about. Which is, let's say you decide you want to have all of your data analysis in the cloud. It turns out Amazon's got a phenomenal set of services that you can use. Do everything from ingest and then wrangle your data and get it cleaned up, and then build one of the apps to gain insight on it and use AI and ML to make that whole thing work. But even Amazon will be the first to tell you that if you have all their services, you need a team understand the development, the operations and the security, DevSecOps, it's typically what it's referred to. And most people don't have that. If you're sure and then say you're fortune 1000, you'll build that team. You'll have, you know, a hundred people doing that. But once you get below that, even in the mid tier, even in a few billion dollar companies, it's actually very hard to have those skills and keep them up to date. So companies are actually getting built that do all of that for you, that effectively, you know, make your services into a product that can be run end to end. And we've invested in one and again we partnered with Amazon on gold Kazina. They effectively make the data lake as a service. And they're effectively building on top of all the Amazon services in orchestrating and managing all that DevSecOps for you. So you don't need that team. And they do it in, you know, days or weeks, not months or years. And so I think that the point that Ben made is a really good one. Which is, you know, you've got to make it a priority and invest in it. And it doesn't just happen. It's a new set of skills, they're different. They require obviously everything from the very earliest stage of development in the cloud, all the way through to the sort of managing and running a bit. And of course maintaining it all securely and unscalable, et cetera. (overlapping chatter) >> It's interesting you bring up that Amazon's got great security. You mentioned that earlier. Mike, I wanted to bring you in because you guys it's graduating a lot of startups, graduating, it's not like they're in school or anything, but they're really, you're building on top of AWS which is already, you know, all the SOC report, all the infrastructure's there. You guys have a high bar on security. So coming out of the AWS ecosystem is not for the faint of heart. I mean, you got to kind of go through and I've heard from many startups that you know, that's a grueling process. And this is, should be good news for the enterprise. How are you guys seeing that partnership? What's the pattern recognition that we can share with enterprises adopting startups coming on the cloud? What can they expect? What are some best practices? What are the things to look for in adopting startup technologies? >> Yeah, so as you know we have a shared security model where we do the security for the physical infrastructure that we're operating, and then we try to share best practices to our partners who really own the security for their applications. Well, one of the benefits we have particularly with the AWS partner network is that, we will help vet these companies, we will review their security architecture, we'll make recommendations. We have a lot of great building blocks of services they can use to build their applications, so that they have a much better chance of really delivering a more secure total application to the enterprise customer. Now of course the enterprise customers still should be checking this and making sure that all of these products meet their needs because that is their ultimate responsibility. But by leveraging the ecosystem we have, the infrastructure we have and the strength of our partners, they can start off with a much more secure application or use case than they would if they were trying to build it from scratch. >> All right. Also, I want to get these guys out of the way in on this last question, before we jump into the wrap up. products and technologies, what is the most important thing enterprises should be focused on? It could be a list of three or four or five that they should be focused on from emerging technologies or a technology secret sauce perspective. Meaning, I'm going to leverage some new things we're going to build and do or buy from cloud scale. What are the most important product technology issues they need to be paying attention to? >> I think I'll run with that first. There's a major, major opportunity with data. We've gone through this whole cycle of creating data lakes that tended to data's forms and big data was going to solve everything. Enterprises are sitting on an amazing amount of information. And anything that can be done to, I actually get insights out of that, and I don't mean dashboards, PI tools, they're like a dime a dozen. How can we leverage AI and ML to really start getting some insights a lot quicker and a lot more value to the company from the data they owns. Anything around that, to me is a major opportunity. >> Now I'm going to go just a little bit deeper on that 'cause I would agree with all those points that Ben made. I think one of the real key points is to make sure that they're really leveraging the data that they have in kind of in place. Pulling in data from all their disparate apps, not trying to generate some new set of data, but really trying to leverage what they have so they can get live information from the disparate apps. Whether it's Salesforce or other systems they might have. I also think it's important to give users the tools to do a lot of their own analytics. So I think definitely, you know, kind of dashboards are a dime a dozen as Ben said, but the more you can do to make it really easy for users to do their own thing, so they're not relying on some central department to create some kind of report for them, but they can innovate on their own and do their own analytics of the data. I think its really critical to help companies move faster. >> Michael? >> I'll just build on that with an example because I think Ben and Mike gave two very good things, you know, data and making it self service to the users et cetera So, an example is one of our companies called Salsify, which is B2B commerce. So they're enabling brands to get their products out into the various different channels the day that people buy them on. Which by the way, an incredible number of channels have been created, whether it's, you know, Instagram at one extreme or of course you know, traditional commerce sites is another. And it's actually impossible to get all of the different capabilities of your product fully explained in the right format in each of those channels humanly. You actually have to use a computer. So that highlights the first thing I was thinking is very important is, what could you not do before that you can now do in the cloud? And you know, do in a distributed fashion. So that's a good example. The second thing is, and Mike said it very well, you know, if you can give people the data that Ben was referring to in a way that they line a business user, in this case, a brand manager, or for example the merchandiser can actually use, they'll quickly tell you, "Oh, these three channels are really not worth us spending a lot of money on. We need waste promotion on them. But look at this one, this one's really taking up. This TikTok thing is actually worth paying attention to. Why don't we enable people to buy, you know, products there?" And then focus in on it. And Salsify, by the way, is you know, I can give you stats with every different customer they've got, but they've got huge brands. The sort of Nestlés, the L'Oreals et cetera. Where they're measuring in terms of hundreds of percent of sales increase, because of using the data of Ben's point and making itself service to Mike's point. >> Awesome. Thought exercise for this little toss up question, for anyone who wants to grab it. If you had unlimited budget for R&D, and you wanted to play the long game and you wanted to take some territory down in the future. What technology and what area would you start carving out and protecting and owning or thinking about or digging into. There's a variety of great stuff out there and you know, being prepared for potentially any wildcards, what would it be? >> Well, I don't mind jumping in. That's a tough question. Whatever I did, I would start with machine learning. I think we're still just starting to see the benefits of what this can do across all of different applications. You know, if you look at what AWS has been doing, we, you know, we recently, many of our new service offerings are integrating machine learning in order to optimize automatically, to find the right solution automatically, to find errors in code automatically. And I think you're going to see more and more machine learning built into all types of line of business applications. Sales, marketing, finance, customer service. You know, you already see some of it but I think it's going to happen more and more. So if I was going to bet on one core thing, it would be that. >> I'll jump on that just because I-- >> You're VC, do you think about this as an easy one for you. >> Well, yes or no (indistinct) that I've been a VC now for too long. I was you know (indistinct) for 21 years. I could have answered that question pretty well but in the last 19 of becoming a VC, I've become ruined by just capital being put behind things. But in all seriousness, I think Mike is right. I think every single application is going to get not just reinvented completely reimagined by ML. Because there's so much of what we do that there is indeed managing the data to try to understand how to improve the business process. And when you can do that in an automated fashion and with a continuous close loop that improves it, it takes away all the drudgery and things like humans or the other extreme, you know, manufacturing. And in-between anything that goes from border to cash faster is going to be good for business. And that's going to require ML. So it's an exciting time ahead. That's where we're putting our money. >> Ben, are you going to go off the board here or you're going to stay with machine learning and dating, go wild card here. Blockchain? AR? VR? (overlapping chatter) >> Well I'd have to say ML and AI applying to privacy and trust. Privacy and trust is going to be a currency that a lot of companies need to deal with for a long time coming. And anything you can do to speed that up and honestly remove the human element, and like Michael said, there's a lot of, before there's a lot of services on AWS that are very creative. There's a lot of security built-in But it's that one S3 bucket that someone left open on the internet, that causes the breach. So how are we automating that? Like how do we take the humans out of this process? So we don't make human errors to really get some security happening. >> I think trust is an interesting one. Trust is kind of data as well. I mean, communities are, misinformation, we saw that with elections, huge. Again, that's back to data. We're back to data again. >> You know, John if I may, I'd like to add to that though. It's a good example of something that none of us can predict. Which is, what will be a fundamentally new way of doing this that we haven't really thought of? And, you know, the blockchain is effectively created a means for people to do distributed computing and also, you know, sharing of data, et cetera. Without the human being in the middle and getting rid of many of the intermediaries that we thought were necessary. So, I don't know whether it's the next blockchain or there's blockchain itself, but I have a feeling that this whole issue of trust will become very different when we have new infrastructure. >> I think I agree with everyone here. The data's key. I come back down to data whether you're telling the sovereignty misinformation, the data is there. Okay. Final, final question before we wrap up. This has been amazing on a more serious note for the enterprise folks out there and people in general and around the world. If you guys could give a color commentary answer to, what the post COVID world will look like. With respect to technology adoption, societal impact and technology for potentially good and aura for business. Now that we're coming closer to vaccines and real life again, what is the post COVID world going to look like? What do we learn from it? And how does that translate into everyday in real life benefits? >> Well, I think one of the things that we've seen is that people have realized you can do a lot of work without being in the office. You could be anywhere as long as you can access the data and make the insights from it that you need to. And so I think there's going to be an expectation on the part of users, that there'll be able to do that all the time. They'll be able to do analytics on their phone. They'll be able to do it from wherever they are. They'll be able to do it quickly and they'll be able to get access to the information that they need. And that's going to force companies to continue to be responsive to the expectations and the needs of their users, so that they can keep people productive and have happy employees. Otherwise they're going to go work somewhere else. >> Michael, any thoughts? Post COVID, what do we learn? What happens next? >> You said one key thing Mike, expectations. And I think we're going to live in a very difficult world because expectations are completely unclear. And you might think it's based on age, or you might think it's based on industry or geography, etc. The reality is people have such wildly different expectations and you know, we've tried to do surveys and to try and understand, you know, whether there are some patterns here. I think it's going to be one word, hybrid. And how we deal with hybrid is going to be a major leadership challenge. Because it's impossible to predict what people will do and how they will behave and how they want to for example, go to school or to you know, go to work or play, et cetera. And so I think the third word that I would use is flexibility. You know, we just have to be agile and flexible until we figure out, you know, how this is going to settle out, to get the best of both worlds, because there's so much that we've learned that has been to your point, really beneficial. The more productivity taking out the community. But there's also a lot of things that people really want to get back to such as social interaction, you know, connecting with their friends and living their lives. >> Ben, final word. >> So I'll just drill in on that a little bit deeper. The war on talent, if we talk about tech, if we talk a lot about data, AI, ML. That it's going to be a big differentiator for the companies that are willing to maintain a work from home and your top level resources are going to be dictating where they're working from. And they've seen our work now. And you know, if you're not flexible with how you're running your organization, you will start to lose talent. And companies are going to have to get their head around that as we move forward. >> Gentlemen, thank you very much for your time. That's a great wrap up to this cube on cloud, the AWS startup showcase. Thank you very much on behalf of Dave Vellante, myself, the entire cube team and Amazon web services. Thank you very much for closing out the keynote. Thanks for your time. >> Thank you John and thanks Amazon for a great day. >> Yeah, thank you John. >> Okay, that's a wrap for today. Amazing event. Great keynote, great commentary, 10 amazing companies out there growing, great traction. Cloud startup, cloud scale, cloud value for the enterprise. I'm John Furrier on behalf of theCUBE and Dave Vellante, thanks for watching. (bright music)

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat hi I'm Stu min a man and this is the cubes coverage of Red Hat summit 2020 of course this year the event is happening all online and that gives us an opportunity to meet with red hat executives customers partners and practitioners where they are around the globe in this segment one of our favorites ever years we're talking to the women in open source and joining me for this segment first of all we have Elissa and Alexander who is the executive vice president and chief people officer of Red Hat this award fit thunder her domain dallisa it is great to see you again thanks so much for joining us thank you so much for having us all right and we have two of the Award winners so first if you see right next bit Elissa we have an epic Sain who's a doctor and PhD candidate in clinical neuroscience at the University of Gothenburg coming to us from Sweden method great to see you thank you very much all right we also have Megan Burge Sinicki who is a manager of research and operations at the open source program office at Google Megan thank you so much for joining us off though thanks for having me all right so dallisa let me hand it off to you is give our audience a little bit if they're not familiar with whipping an open source what the initiative is the community and you know what might have changed from previous years when we've talked about this sure so we realized that the tech industry is a great industry for diverse populations but a lot of diverse populations don't realize that and so as the open source leader we wanted to shine a light on the contributions that some of our underrepresented populations are making an open source that trying to inspire more people to join communities to participate to contribute we know that more diverse populations help us to innovate more rapidly they help us to solve more problems and so it's really important especially today with what's happening in the world lots of important problems to solve that we really invite more of our other upper sort of populations to join in the communities awesome so absolutely there there are lots of people that volunteer there are lots of people that do it as their day job Megan why don't we fuck you have a roll open source first Google as a strong legacy and open source in general so tell us a little bit about you know what you were working on and what you're being recognized for here yeah well a lot of the recognition comes from my work with the Drupal Association I had been with Drupal for 8 years hoping to build that foundation in supporting that community and lots of different ways from fundraising to community events running sprints and helping with their developer tools and so that was a lot what the award was based on and now I'm at Google and I've been here for about a year and a half and I run their research and operations and so Google is an expression of open source and we have thousands of people using thousands of projects and we want to make sure they do it well they feel supported that we are good citizens in the projects that we participate in and so my group provides the operational support to make sure that happens you know you know what one of the things that's always fascinating when I go to Red Hat there's so many projects there's so many participants from various walks of life last year at the show there was a lot of discussion of you know it was a survey really and said that you know the majority of people that tribute now it's actually part of their job as opposed to when I think back you know you go back a couple of decades ago and it was like oh well in my spare time or down in my basement I'm contributing here so maybe talk a little bit about the communities and you know what what Megan is embodying CSUN she worked on project now she's working for obviously a good partner of Red Hat's that does a lot of open source yeah I love the way she described what her role is at Google and that it's fascinating and Google has been really a huge contributor in the community for in communities for years and years so I think that what we're seeing with the communities and people saying yeah now it's part of my day job is that you know 20 years ago the idea that open-source development would be kind of on par with proprietary development and on par in terms of being used in the enterprise and the data center was something that I think many people questioned proprietary software was the way that most people felt comfortable making sure that their intellectual property is protected and that users could feel comfortable using it within the parameters required so that was the way it was 20 years ago and then now you think about you know most companies there is some form of open source that is part of their infrastructure so now open source is no longer you know that disrupter but it's really a viable alternative and organizations really want to use both they want to have some propriety or they want to have some open sources so that means like every company is going to need to have some need to understand how to participate in communities how to influence communities and Red Hat's a great partner in helping enterprise customers to be able to understand what those red Nets might look like and then helping to kind of harden it make sure things that they need to have application city to have certified or certified and make it really usable in a way they're comfortable with in the enterprise that's kind of special Red Hat place but it's just a tribute to where we come in a world in terms of open source being really accepted and thriving and it helps us to innovate much more rapidly yeah and there's there's no better way to look at not only where we are but where we're going then talk about what's happening in the academic world so that gives it brings us Aneta so you are the academic award winner you're a PhD candidate so tell us a little bit about your participation and open source what it means to be part of this community my PhD project involves using virtual reality to measure the arm movements of people with stroke so we have participants coming in into our lab so they we're these 3d glasses and then they start seeing virtual objects in the 3d space and they use their hands to touch at these targets and make them disappear and we have all these movements data specially interpreters and then we write code and analyze the data and find out how much they have recovered within one year after stroke this is my PhD project but my involvement with open source happens they before like in starting from 2010 I have been editing Wikipedia and I have been writing several articles related to medicine and healthcare so that is where I started with open open knowledge and then I moved on words and after my medical studies I moved to research and worked on this awesome project and so there are multiple ways by which I have engaged with open source that's far that's awesome my understanding is also some of the roots that you had and some of the medical things that you're doing have an impact on what's happening today so obviously we're all dealing with the global pandemic in Koba 19 so I'd like to hear you know what your involvement there you know your data obviously is politically important that we have the right data getting to the right people as fast as possible definitely yes right now I'm working on writing creating content for Wikipedia writing on articles related to Kobe 19 so I mostly work on writing about its socio-economic impact writing about Kobe 19 testing and also about the disease in general mental health issues surrounding that social stigma associated began with it and so forth so I use all these high-quality references from the World Health Organization the United Nations and also from several journals and synthesize them and write articles on Wikipedia so we have a very cool project called wiki project code 19 on Wikipedia where people who are interested in writing articles creating data uploading images related to poet 19 come together and create some good content out of it so I am a very active participant there alright and making my understanding is you you also have some initiatives related to kovat 19 maybe you can tell us a little bit about those yeah well one I'm loosely affiliated with this kovat act now and that is a combination of developers data scientists epidemiologists and US state government officials and it's looking at how was the curve look like and how does that curve get flattened if governor's made decisions faster or differently than what they're making today and how does it impact the availability of ICU beds and ventilators and so that is a tool that's being used today by many decision-makers here in the US and my contribution to that was they needed some resources I reached into Google and found some smart generous volunteers that are contributing to the dataset and actually I just connected with Neda do this award program and now she's connected and is gonna start working on this as well yes oh that's fantastic yeah I mean dallisa you know we've known for a long time you want to move fast if you want to connect you know lots of diverse groups you know open sources is an important driver there what what else are you seeing in your group you know with your hat is the the people officer you know obviously this is a big impact not only on all of your customers partners but on fun Red Hatters themselves well it is a huge impact we're so fortunate that we have some experience working remotely we have about 25 percent of our population that historically works remotely so we have that as a foundation but certainly the quick move the rapid move to really thinking about our people first and having them work from home across the globe that is unprecedented and at this point we have some individuals who have been working from home for many many many week and others that are really in entering their fourth week so we're starting to have this huge appreciation for what it's like to work remotely and what we can learn about more effective inclusion so I think you know back to the idea of women and open source and diversity inclusion one of the things you may always prided ourself in is we focus on inclusion and we think about things like okay if the person is not in the room with their remote let's make sure for including them let's make sure they get to speak first etcetera well now we're learning what it's really like to be remote and for everyone to be remote and so we're creating this muscle as an organization I think most organizations are doing this right getting a muscle you didn't have before we really really having to think about inclusion in a different way and you're building a capability as an organization that you didn't have to appreciate those that are not in the room and to make sure they are included because no one's in the room you know we're really important pieces and dallisa you know one of the things that that's always great about Red Hat summit is you you bring together all these people as we just heard you know that your two Award winners here you know got connected through the awards so maybe give us a little bit of a peek as to what sort of things the community can still look forward to how they can continue to connect even though we're all going to be remote for this event yeah this event is is it going to be great event and I hope everyone joins us along our journey we are fortunate that Red Hat you know as the open source leader really wants to take a leadership position in thinking about how we can shine a light on opportunities for us to highlight the value of diversity and inclusion and so we've got a number of events not throughout the summit that we'd love people to join in and we're going to be celebrating our women and open-source again at our women's leadership community lunch is now not a lunch it is now a discussion unless you're having your lunch that you can check your desk but we're having a great conversation at that event I mean by people to join in and have a deeper conversation and also another look at our women in open source Award winners but these Award winners are just so amazing every year that applications that are submitted are just more and more inspiring and all the finalists were people that are so impressive so I love the fact that our community continues to grow and that they're more and more impressive people that are joining the community and that they're making those connections so that together we can you know really shine a light on the value that women bring to the communities and continue to inspire other underrepresented groups to join in and participate then a you know research obviously is an area where open-source is pretty well used but just give us a little bit of viewpoint from your standpoint yourself and your peers you know I would think from the outside that you know open sourced is just kind of part of the fabric of the tools that you're using is it something that people think specifically about a course or does it just come naturally that people are you know leveraging using and even contributing what what's available the tool I'm using is called cuteness it's an open source tool written in Python and so that gives me the possibility to have a look in deeper into the code and see what's actually inside for example I would like to know how what is the size of the target that is shown in the virtual space and I can fit know that correctly to the millimeters because it's available to me in open source so I think these are the advantages which researchers see when they have tools open-source tools and at the same time there's also a movement in Sweden and in most of Europe where they want the researchers are asking for publishing their articles in open access journals so they want most of their research be published as transparent as possible and there is also this movement where people want researchers want to have their data put in some open data city so that everybody can have a look at it and do analysis on the data and build up on that data if other people want to so there's a lot going from the open access side and knowledge side and also the open source side in the research community and I'm looking forward to what probably 19 will do to this movement in future and I am sure people will start using more more and more open-source tools because after the Manderly yeah making I'm curious from your standpoint when I think about a lot of these communities you know meetups are just kind of some of the regular fabric of how I get things done as well as you know just lots of events tie into things so when you're talking to your colleagues when you're talking to your peers out there how much is kind of the state of reality today having an impact in any any learnings that you can share with gaudí yeah that is definitely a challenge that we're going to figure out together and I am part of a group called Foss responders we are reaching out to projects and listening to their needs and amplifying their needs and helping to get them connected with resources and one of the top three areas of need include how do I run an online community event how do I replace these meetups and what is wonderful is that groups have been moving in this direction already and so who would release a guide of how they run online events and they provide some tooling as well but so has WordPress put out a guide and other projects that have gone down this path and so in the spirit of open source everyone is sharing their knowledge and Foss responders is trying to aggregate that so that you can go to their site find it and take advantage of it yeah definitely something I've seen one of the silver linings is you know these communities typically have been a lot of sharing but even more so everybody's responding everybody's kind of rallying to the cause don't want to give you the final word obviously you know this is a nice segment piece that we usually expect to see at Red Hat summit so what else do you want to help share where the community is final closing thoughts well I think that you know we're not done yet we have been so fortunate to be able to highlight you know the contributions that women make to open source and that is a honor that we get to take that role but we need to continue to go down this path we are not we're not done we have not made the improvement in terms of the the representative in our communities that will actually foster all of the improvements and all the solutions that need to happen in the world though we're going to keep down this pathway and really encourage everyone to think through how you can have a more inclusive team how you can make someone feel included if you're participating in a community or in an organization so that we really continue to bring in more diversity and have more innovation well excellent thank you so much Alisa for sharing it thank you too - both of you Award winners and really look forward to reading more online definitely checking out some of the initiatives that you've shared valuable pieces that hopefully everybody can leverage all right lots more coverage from Red Hat summit 2020 I'm Stu minimun and as always thank you for watching the cube [Music]