>>Set restaurants. And who says TEUs had got a little ass more skin in the game for us, in charge of his destiny? You guys are excited. Robert Worship is Chief Alumni. >>My name is Dave Ante, and I'm a long time industry analyst. So when you're as old as I am, you've seen a lot of transitions. Everybody talks about industry cycles and waves. I've seen many, many waves. Met a lot of industry executives and of a little bit of a, an industry historian. When you interview many thousands of people, probably five or 6,000 people as I have over the last half of a decade, you get to interact with a lot of people's knowledge and you begin to develop patterns. And so that's sort of what I bring is, is an ability to catalyze the conversation and, you know, share that knowledge with others in the community. Our philosophy is everybody's expert at something. Everybody's passionate about something and has real deep knowledge about that's something well, we wanna focus in on that area and extract that knowledge and share it with our communities. This is Dave Ante. Thanks for watching the Cube. >>Hello everyone and welcome back to the Cube where we are streaming live this week from CubeCon. I am Savannah Peterson and I am joined by an absolutely stellar lineup of cube brilliance this afternoon. To my left, a familiar face, Lisa Martin. Lisa, how you feeling? End of day two. >>Excellent. It was so much fun today. The buzz started yesterday, the momentum, the swell, and we only heard even more greatness today. >>Yeah, yeah, abs, absolutely. You know, I, I sometimes think we've hit an energy cliff, but it feels like the energy is just >>Continuous. Well, I think we're gonna, we're gonna slide right into tomorrow. >>Yeah, me too. I love it. And we've got two fantastic analysts with us today, Sarge and Keith. Thank you both for joining us. We feel so lucky today. >>Great being back on. >>Thanks for having us. Yeah, Yeah. It's nice to have you back on the show. We were, had you yesterday, but I miss hosting with you. It's been a while. >>It has been a while. We haven't done anything in since, Since pre >>Pandemic, right? Yeah, I think you're >>Right. Four times there >>Be four times back in the day. >>We, I always enjoy whole thing, Lisa, cuz she's so well prepared. I don't have to do any research when I come >>Home. >>Lisa will bring up some, Oh, sorry. Jeep, I see that in 2008 you won this award for Yeah. Being just excellent and I, I'm like, Oh >>Yeah. All right Keith. So, >>So did you do his analysis? >>Yeah, it's all done. Yeah. Great. He only part, he's not sitting next to me too. We can't see it, so it's gonna be like a magic crystal bell. Right. So a lot of people here. You got some stats in terms of the attendees compared >>To last year? Yeah, Priyanka told us we were double last year up to 8,000. We also got the scoop earlier that 2023 is gonna be in Chicago, which is very exciting. >>Oh, that is, is nice. Yeah, >>We got to break that here. >>Excellent. Keith, talk to us about what some of the things are that you've seen the last couple of days. The momentum. What's the vibe? I saw your tweet about the top three things you were being asked. Kubernetes was not one of them. >>Kubernetes were, was not one of 'em. This conference is starting to, it, it still feels very different than a vendor conference. The keynote is kind of, you know, kind of all over the place talking about projects, but the hallway track has been, you know, I've, this is maybe my fifth or sixth CU con in person. And the hallway track is different. It's less about projects and more about how, how do we adjust to the enterprise? How do we Yes. Actually do enterprise things. And it has been amazing watching this community grow. I'm gonna say grow up and mature. Yes. You know, you know, they're not wearing ties yet, but they are definitely understanding kind of the, the friction of implementing new technology in, in an enterprise. >>Yeah. So ge what's your, what's been your take, We were with you yesterday. What's been the take today to take aways? >>NOMA has changed since yesterday, but a few things I think I, I missed talking about that yesterday were that, first of all, let's just talk about Amazon. Amazon earnings came out, it spooked the market and I think it's relevant in this context as well, because they're number one cloud provider. Yeah. And all, I mean, almost all of these technologies on the back of us here, they are related to cloud, right? So it will have some impact on these. Like we have to analyze that. Like will it make the open source go faster or slower in, in lieu of the fact that the, the cloud growth is slowing. Right? So that's, that's one thing that's put that's put that aside. I've been thinking about the, the future of Kubernetes. What is the future of Kubernetes? And in that context, I was thinking like, you know, I think in, when I put a pointer there, I think in tangents, like, what else is around this thing? So I think CN CNCF has been writing the success of Kubernetes. They are, that was their number one flagship project, if you will. And it was mature enough to stand on its own. It it was Google, it's Google's Borg dub da Kubernetes. It's a genericized version of that. Right? So folks who do tech deep down, they know that, Right. So I think it's easier to stand with a solid, you know, project. But when the newer projects come in, then your medal will get tested at cncf. Right. >>And cncf, I mean they've got over 140 projects Yeah. Right now. So there's definitely much beyond >>Kubernetes. Yeah. So they, I have numbers there. 18 graduated, right, 37 in incubation and then 81 in Sandbox stage. They have three stages, right. So it's, they have a lot to chew on and the more they take on, the less, you know, quality you get goes into it. Who is, who's putting the money behind it? Which vendors are sponsoring like cncf, like how they're getting funded up. I think it >>Something I pay attention to as well. Yeah. Yeah. Lisa, I know you've got >>Some insight. Those are the things I was thinking about today. >>I gotta ask you, what's your take on what Keith said? Are you also seeing the maturation of the enterprise here at at coupon? >>Yes, I am actually, when you say enterprise versus what's the other side? Startups, right? Yeah. So startups start using open source a lot more earlier or lot more than enterprises. The enterprise is what they need. Number one thing is the, for their production workloads, they want a vendor sporting them. I said that yesterday as well, right? So it depend depending on the size of the enterprise. If you're a big shop, definitely if you have one of the 500 or Fortune five hundreds and your tech savvy shop, then you can absorb the open source directly coming from the open source sort of universe right. Coming to you. But if you are the second tier of enterprise, you want to go to a provider which is managed service provider, or it can be cloud service provider in this case. Yep. Most of the cloud service providers have multiple versions of Kubernetes, for example. >>I'm not talking about Kubernetes only, but like, but that is one example, right? So at Amazon you can get five different flavors of Kubernetes, right? Fully manage, have, manage all kind of stuff. So people don't have bandwidth to manage that stuff locally. You have to patch it, you have to roll in the new, you know, updates and all that stuff. Like, it's a lot of work for many. So CNCF actually is formed for that reason. Like the, the charter is to bring the quality to open source. Like in other companies they have the release process and they, the stringent guidelines and QA and all that stuff. So is is something ready for production? That's the question when it comes to any software, right? So they do that kind of work and, and, and they have these buckets defined at high level, but it needs more >>Work. Yeah. So one of the things that, you know, kind of stood out to me, I have good friend in the community, Alex Ellis, who does open Fast. It's a serverless platform, great platform. Two years ago or in 2019, there was a serverless day date. And in serverless day you had K Native, you had Open Pass, you had Ws, which is supported by IBM completely, not CNCF platforms. K native came into the CNCF full when Google donated the project a few months ago or a couple of years ago, now all of a sudden there's a K native day. Yes. Not a serverless day, it's a K native day. And I asked the, the CNCF event folks like, what happened to Serverless Day? I missed having open at serverless day. And you know, they, they came out and said, you know what, K native got big enough. >>They came in and I think Red Hat and Google wanted to sponsor a K native day. So serverless day went away. So I think what what I'm interested in and over the next couple of years is, is they're gonna be pushback from the C against the cncf. Is the CNCF now too big? Is it now the gatekeeper for do I have to be one of those 147 projects, right? In order enough to get my project noticed the open, fast, great project. I don't think Al Alex has any desire to have his project hosted by cncf, but it probably deserves, you know, shoulder left recognition with that. So I'm pushing to happen to say, okay, if this is open community, this is open source. If CNC is the place to have the cloud native conversation, what about the projects that's not cncf? Like how do we have that conversation when we don't have the power of a Google right. Or a, or a Lenox, et cetera, or a Lenox Foundation. So GE what, >>What are your thoughts on that? Is, is CNC too big? >>I don't think it's too big. I think it's too small to handle the, what we are doing in open source, right? So it's a bottle. It can become a bottleneck. Okay. I think too big in a way that yeah, it has, it has, it has power from that point of view. It has that cloud, if you will. The people listen to it. If it's CNCF project or this must be good, it's like in, in incubators. Like if you are y white Combinator, you know, company, it must be good. You know, I mean, may not be >>True, but, >>Oh, I think there's a bold assumption there though. I mean, I think everyone's just trying to do the best they can. And when we're evaluating projects, a very different origin and background, it's incredibly hard. Very c and staff is a staff of 30 people. They've got 180,000 people that are contributing to these projects and a thousand maintainers that they're trying to uphold. I think the challenge is actually really great. And to me, I actually look at events as an illustration of, you know, what's the culture and the health of an organization. If I were to evaluate CNCF based on that, I'd say we're very healthy right now. I would say that we're in a good spot. There's a lot of momentum. >>Yeah. I, I think CNCF is very healthy. I'm, I'm appreciative for it being here. I love coupon. It's becoming the, the facto conference to have this conversation has >>A totally >>Different vibe to other, It's a totally different vibe. Yeah. There needs to be a conduit and truth be told, enterprise buyers, to subject's point, this is something that we do absolutely agree on, on enterprise buyers. We want someone to pick winners and losers. We do, we, we don't want a box of Lego dumped on our, the middle of our table. We want somebody to have sorted that out. So while there may be five or six different service mesh solutions, at least the cncf, I can go there and say, Oh, I'll pick between the three or four that are most popular. And it, it's a place to curate. But I think with that curation comes the other side of it. Of how do we, how, you know, without the big corporate sponsor, how do I get my project pushed up? Right? Elevated. Elevated, Yep. And, and put onto the show floor. You know, another way that projects get noticed is that startups will adopt them, Push them. They may not even be, I don't, my CNCF project may not, my product may not even be based on the CNCF product. But the new stack has a booth, Ford has a booth. Nothing to do with a individual prod up, but promoting open source. What happens when you're not sponsored? >>I gotta ask you guys, what do you disagree on? >>Oh, so what, what do we disagree on? So I'm of the mindset, I can, I can say this, I I believe hybrid infrastructure is the future of it. Bar none. If I built my infrastructure, if I built my application in the cloud 10 years ago and I'm still building net new applications, I have stuff that I built 10 years ago that looks a lot like on-prem, what do I do with it? I can't modernize it cuz I don't have the developers to do it. I need to stick that somewhere. And where I'm going to stick that at is probably a hybrid infrastructure. So colo, I'm not gonna go back to the data center, but I'm, I'm gonna look, pick up something that looks very much like the data center and I'm saying embrace that it's the future. And if you're Boeing and you have, and Boeing is a member, cncf, that's a whole nother topic. If you have as 400 s, hpu X, et cetera, stick that stuff. Colo, build new stuff, but, and, and continue to support OpenStack, et cetera, et cetera. Because that's the future. Hybrid is the future. >>And sub g agree, disagree. >>I okay. Hybrid. Nobody can deny that the hybrid is the reality, not the future. It's a reality right now. It's, it's a necessity right now you can't do without it. Right. And okay, hybrid is very relative term. You can be like 10% here, 90% still hybrid, right? So the data center is shrinking and it will keep shrinking. Right? And >>So if by whole is the data center shrinking? >>This is where >>Quick one quick getting guys for it. How is growing by a clip? Yeah, but there's no data supporting. David Lym just came out for a report I think last year that showed that the data center is holding steady, holding steady, not growing, but not shrinking. >>Who sponsored that study? Wait, hold on. So the, that's a question, right? So more than 1 million data centers have been closed. I have, I can dig that through number through somebody like some organizations we published that maybe they're cloud, you know, people only. So the, when you get these kind of statements like it, it can be very skewed statements, right. But if you have seen the, the scene out there, which you have, I know, but I have also seen a lot of data centers walk the floor of, you know, a hundred thousand servers in a data center. I cannot imagine us consuming the infrastructure the way we were going into the future of co Okay. With, with one caveat actually. I am not big fan of like broad strokes. Like make a blanket statement. Oh no, data center's dead. Or if you are, >>That's how you get those esty headlines now. Yeah, I know. >>I'm all about to >>Put a stake in the ground. >>Actually. The, I think that you get more intelligence from the new end, right? A small little details if you will. If you're golden gold manak or Bank of America, you have so many data centers and you will still have data centers because performance matters to you, right? Your late latency matters for applications. But if you are even a Fortune 500 company on the lower end and or a healthcare vertical, right? That your situation is different. If you are a high, you know, growth startup, your situation is different, right? You will be a hundred percent cloud. So cloud gives you velocity, the, the, the pace of change, the pace of experimentation that actually you are buying innovation through cloud. It's proxy for innovation. And that's how I see it. But if you have, if you're stuck with older applications, I totally understand. >>Yeah. So the >>We need that OnPrem. Yeah, >>Well I think the, the bring your fuel sober, what we agree is that cloud is the place where innovation happens. Okay? At some point innovation becomes legacy debt and you have thus hybrid, you are not going to keep your old applications up to date forever. The, the, the math just doesn't add up. And where I differ in opinion is that not everyone needs innovation to keep moving. They need innovation for a period of time and then they need steady state. So Sergeant, we >>Argue about this. I have a, I >>Love this debate though. I say it's efficiency and stability also plays an important role. I see exactly what you're talking about. No, it's >>Great. I have a counter to that. Let me tell you >>Why. Let's >>Hear it. Because if you look at the storage only, right? Just storage. Just take storage computer network for, for a minute. There three cost reps in, in infrastructure, right? So storage earlier, early on there was one tier of storage. You say pay the same price, then now there are like five storage tiers, right? What I'm trying to say is the market sets the price, the market will tell you where this whole thing will go, but I know their margins are high in cloud, 20 plus percent and margin will shrink as, as we go forward. That means the, the cloud will become cheaper relative to on-prem. It, it, in some cases it's already cheaper. But even if it's a stable workload, even in that case, we will have a lower tier of service. I mean, you, you can't argue with me that the cloud versus your data center, they are on the same tier of services. Like cloud is a better, you know, product than your data center. Hands off. >>I love it. We, we are gonna relish in the debates between the two of you. Mic drops. The energy is great. I love it. Perspective. It's not like any of us can quite see through the crystal ball that we have very informed opinions, which is super exciting. Yeah. Lisa, any last thoughts today? >>Just love, I love the debate as well. That, and that's, that's part of what being in this community is all about. So sharing about, sharing opinions, expressing opinions. That's how it grows. That's how, that's how we innovate. Yeah. Obviously we need the cloud, but that's how we innovate. That's how we grow. Yeah. And we've seen that demonstrated the last couple days and I and your, your takes here on the Cuban on Twitter. Brilliant. >>Thank you. I absolutely love it. I'm gonna close this out with a really important analysis on the swag of the show. Yes. And if you know, yesterday we were looking at what is the weirdest swag or most unique swag We had that bucket hat that took the grand prize. Today we're gonna focus on something that's actually quite cool. A lot of the vendors here have really dedicated their swag to being local to Detroit. Very specific in their sourcing. Sonotype here has COOs. They're beautiful. You can't quite feel this flannel, but it's very legit hand sound here in Michigan. I can't say that I've been to too many conferences, if any, where there was this kind of commitment to localizing and sourcing swag from around the corner. We also see this with the Intel booth. They've got screen printers out here doing custom hoodies on spot. >>Oh fun. They're even like appropriately sized. They had local artists do these designs and if you're like me and you care about what's on your wrist, you're familiar with Shinola. This is one of my favorite swags that's available. There is a contest. Oh going on. Hello here. Yeah, so if you are Atan, make sure that you go and check this out. The we, I talked about this on the show. We've had the founder on the show or the CEO and yeah, I mean Shine is just full of class as since we are in Detroit as well. One of the fun themes is cars. >>Yes. >>And Storm Forge, who are also on the show, is actually giving away an Aston Martin, which is very exciting. Not exactly manufactured in Detroit. However, still very cool on the car front and >>The double oh seven version named the best I >>Know in the sixties. It's love it. It's very cool. Two quick last things. We talk about it a lot on the show. Every company now wants to be a software company. Yep. On that vein, and keeping up with my hat theme, the Home Depot is here because they want everybody to know that they in fact are a technology company, which is very cool. They have over 500,000 employees. You can imagine there's a lot of technology that has to go into keeping Napa. Absolutely. Yep. Wild to think about. And then last, but not at least very quick, rapid fire, best t-shirt contest. If you've ever ran to one of these events, there are a ton of T-shirts out there. I rate them on two things. Wittiest line and softness. If you combine the two, you'll really be our grand champion for the year. I'm just gonna hold these up and set them down for your laughs. Not afraid to commit, which is pretty great. This is another one designed by locals here. Detroit Code City. Oh, love it. This one made me chuckle the most. Kiss my cash. >>Oh, that's >>Good. These are also really nice and soft, which is fantastic. Also high on the softness category is this Op Sarah one. I also like their bird logo. These guys, there's just, you know, just real nice touch. So unfortunately, if you have the fumble, you're not here with us, live in Detroit. At least you're gonna get taste of the swag. I taste of the stories and some smiles hear from those of us on the cube. Thank you both so much for being here with us. Lisa, thanks for another fabulous day. Got it, girl. My name's Savannah Peterson. Thank you for joining us from Detroit. We're the cube and we can't wait to see you tomorrow.

>>Good morning and welcome back to the Cube where we are excited to be broadcasting live all week from Detroit to Michigan at Cuban slash cloud Native con. Depending on who you're asking, Lisa, it's day two things are buzzing. How are you feeling? >>Good, excited. Ready for day two, ready to have more great conversations to see how this community is expanding, how it's evolving, and how it's really supporting it itself. >>Yeah, Yeah. This is a very supportive community. Something we talked a lot about. And speaking of community, we've got some very bold and brave folks over here. We've got this CTO and the head of product from Storm Forge, and they are on a mission to automate Kubernetes. Now automatic and Kubernetes are not words that go in the same sentence very often, so please welcome Patrick and Yasmin. Thank you both for being here. Hello. How you doing? >>Thanks for having us. >>Thanks for having us. >>Talk about what you guys are doing. Cause as you said, Kubernetes auto spelling is anything but auto. >>Yeah. >>The, what are some of the challenges? How do you help >>Eliminate this? Yeah, so the mission at Storm Forge is primarily automatic resource configuration and optimization essentially. So we started as a machine learning company first. And it's kind of an interesting story cuz we're one of those startups that has pivoted a few times. And so we were running our machine learning workloads. Most >>Have, I think, >>Right? Yeah. Yeah. We were, we started out running our machine learning workloads and moving them into Kubernetes. And then we weren't quite sure how to correctly adjust and size our containers. And so our ML team, we've got three PhDs and applied mathematics. They said, Well, hang on, we could write an algorithm for that. And so they did. And then, Oh, I love this. Yeah. And then we said, Well holy cow, that's actually really useful. I wonder if other people would like that. And that's kind of where we got our start. >>You solved your own problem and then you built a business >>Around it. Yeah, exactly. >>That is fantastic. Is, is that driving product development at Storm Forge still? That kind of attitude? >>I mean that kind of attitude definitely drives product development, but we're, you know, balancing that with what the users are, the challenges that they have, especially at large scale. We deal with a lot of large enterprises and for us as a startup, we can relate to the problems that come with Kubernetes when you're trying to scale it. But when you're talking about the scale of some of these larger enterprises, it's just a different mentality. So we're trying to balance that of how we take that input into how we build our product. Talk >>About that, like the, the end user input and how you're taking that in, because of course it's only going to be a, you know, more of a symbiotic relationship when that customer feedback is taken and >>Acted on. Yeah, totally. And for us, because we use machine learning, it's a lot of building confidence with our users. So making sure that they understand how we look at the data, how we come up with the recommendations, and actually deploy those changes in their environment. There's a lot of trust that needs to be built there. So being able to go back to our users and say, Okay, we're presenting you this type of data, give us your feedback and building it alongside them has helped a lot in these >>Relationships. Absolutely. You said the word trust, and that's something that we talk about at every >>Show. I was gonna jump on that too. It's >>Not, Yeah, it's not a buzzword. It's not, It shouldn't be. Yeah. It really should be, I wanna say lived and breathed, but that's probably grammatically incorrect. >>We're not a gram show. It's okay darling. Yeah, thank >>You. It should be truly embodied. >>Yeah. And I, I think it's, it's not even unique to just what we do, but across tech in general, right? Like when I talk about SRE and building SRE teams, one of the things I mentioned is you have to build that trust first. And with machine learning, I think it can be really difficult too for a couple different reasons. Like one, it tends to be a black box if it's actually true machine learning. Totally. Which ours is. But the other piece that we run into. Yeah. And the other piece we run into though is, is what I was an executive at United Health Group before I joined Storm Forge. And I would get companies that would come to me and try to sell me machine learning and I would kind of look at it and say, Well no, that's just a basic decision tree. Or like, that's a super basic whole winter forecast, right? Like that's not actually machine learning. And that's one of the things that we actually find ourselves kind of battling a little bit when we talk about what we do in building that trust. >>Talk a little bit about the latest release as you guys had a very active September. Here we are. And towards the, I think end of October. Yeah. What are some of the, the new things that have come out? New integrations, new partnerships. Give us a scoop on that. >>Yeah, well I guess I'll start and then I'll probably hand it over to you. But like the, the big thing for us is we talked about automating Kubernetes in the very beginning, right? Like Kubernetes has got a vpa it's >>A wild sentence anyway. Yeah, yeah. >>It it >>Has. We're not gonna get over at the whole show. Yeah. >>It as a VPA built in, it has an HPA built in and, and when you look at the data and even when you read the documentation from Google, it explicitly says never the two should meet. Right. Because you'll end up thrashing and they'll fight each other. Well the big release we just announced is with our machine learning, we can now do both. And so we vertically scale your pods to the correct up. Yeah. >>Follow status. I love that. >>Yeah, we can, we can scale your pods to the correct size and still allow you to enable the HPA and we'll make recommendations for your scaling points and your thresholds on the HPA as well so that they can work together to really truly maximize your efficiency that without sacrificing your performance and your reliability of the applications that you're running. That >>Sounds like a massive differentiator for >>Storm launch, which I would say it is. Yeah. I think as far as I know, we're the first in the industry that can do this. Yeah. >>And >>From very singularity vibes too. You know, the machines are learning, teaching themselves and doing it all automatically. Yep. Gets me very >>Excited. >>Yeah, absolutely. And from a customer demand perspective, what's the feedback been? Yeah, it's been a few >>Weeks. Yeah, it's been really great actually. And a lot of why we went down this path was user driven because they're doing horizontal scale and they want to be able to vertically size as they're scaling. So if you put yourself in the shoes of someone that's configuring Kubernetes, you're usually guessing on what you're setting your CPU requests and limits do. But horizontal scale makes sense. You're either adding more things or removing more things. And so once they actually are scaled out as a large environment and they have to rethink, how am I gonna resize this now? It's just not possible. It's so many thousands of settings across all the different environments and you're only thinking about CPU memory, You're not thinking about a lot of things. It's just, but once you scale that out, it's a big challenge. So they came to us and said, Okay, you're doing, cuz we were doing vertical scaling before and now we enable vertical and horizontal. And so they came to us and said, I love what you're doing about right sizing, but we wanna be able to do this while also horizontally scaling. And so the way that our software works is we give you the recommendations for what the setting should be and then allow Kubernetes to continue to add and remove replicas as needed. So it's not like we're going in and making changes to Kubernetes, but we make changes to the configuration settings so that it's the most optimal from a resource perspective. >>Efficiency has been a real big theme of the show. Yeah. And it's clear that that's a focus for you. Everyone here wants to do more faster Of course. And innovation, that's the thing to do that sometimes we need partners. You just announced an integration with Datadog. Tell us about that. Yeah, >>Absolutely. Yeah. So the way our platform works is we need data of course, right? So they're, they're a great partner for us and we use them both as an input and an output. So we pull in metrics from Datadog to provide recommendations and we'll actually display all those within the Datadog portal. Cause we have a lot of users that are like, Look, Datadog's my single pane of glass and I hate using that word, but they get all their insights there. They can see their recommendations and then actually go deploy those. Whether they wanna automatically have the recommendations deployed or go in and actually push a button. >>So give me an example of a customer that is using the, the new release and some of the business outcomes they're achieving. I imagine one of the things that you're enabling is just closing that ES skills gap. But from a business level perspective, how are they gaining like competitive advantages to be able to get products to market faster, for example? >>Yeah, so one of the customers that was actually part of our press release and launch and spoke about us at a webinar, they are a SaaS product and deal with really bursty workloads. And so their cloud costs have been growing 40% year over year. And their platform engineering team is basically enabled to provide the automation for developers and in their environment, but also to reduce those costs. So they want to, it's that trade off of resiliency and cost performance. And so they came to us and said, Look, we know we're over provisioned, but we don't know how to tackle that problem without throwing tons of humans at the problem. And so we worked with them and just on a single app found 60% savings and we're working now to kind of deploy that across their entire production workload. But that allows them to then go back and get more out of the, the budget that they already have and they can kind of reallocate that in other areas, >>Right? So there can be chop line and bottom >>Line impact. Yeah. And I, I think there's some really direct impact to the carbon emissions of an organization as well. That's a good point. When you can reduce your compute consumption by 60%. >>I love this. We haven't talked about this at all during the show. Yeah. And I'm really glad that you brought this up. All of the things that power this use energy. Yeah. >>What is it like seven to 8% of all electricity in the world is consumed by data centers. Like it's crazy. Yeah. Yeah. And so like that's wild. Yeah. Yeah. So being able to make a reduction in impact there too, especially with organizations that are trying to sign green pledges and everything else. >>It's hard. Yeah. ESG initiatives are huge. >>Absolut, >>It's >>A whole lot. A lot of companies have ESG initiatives where they can't even go out and do an RFP with a business, Right. If they don't have an actual active starting, impactful ESG program. Yes. Yeah. >>And the RFPs that we have to fill out, we have to tell them how they'll help. >>Yeah. Yes. It's so, yeah, I mean I was really struck when I looked on your website and I saw 54% average cost reduction for Yeah. For your cloud operations. I hadn't even thought about it from a power perspective. Yeah. I mean, imagine if we cut that to 3% of the world's power grid. That is just, that is very compelling. Speaking of compelling and exciting future things, talk to us about what's next? What's got you pumped for 2023 and and what lies >>Ahead? Oh man. Well that seems like a product conversation for sure. >>Well, we're super excited about extending what we do to other platforms, other metrics. So we optimize a lot right now around CPU and memory, but we can also give people insights into, you know, limiting kills, limiting CPU throttling, so extending the metrics. And when you look at hba and horizontal scale today, most of it is done with cpu, but there are some organizations out there that are scaling on custom metrics. So being able to take in more data to provide more recommendations and kind of extend what we can do from an optimization standpoint. >>That's, yeah, that's cool. And what house you most excited on the show floor? Anything? Anything that you've seen? Any keynotes? >>There's, Well, I haven't had a lot of time to go to the keynotes unfortunately, but it's, >>Well, I'm shock you've been busy or something, right? Much your time here. >>I can't imagine why. But no, there's, it's really interesting to see all the vendors that are popping up around Kubernetes focus specifically with security is always something that's really interesting to me. And automating CICD and how they continue to dive into that automation devs, SEC ops continues to be a big thing for a lot of organizations. Yeah. Yeah. >>I I do, I think it's interesting when we marry, Were you guys here last year? >>I was not here. >>No. So at, at the smaller version of this in Los Angeles. Yeah. I, I was really struck because there was still a conversation of whether or not we were all in on Kubernetes as, as kind of a community and a society this year. And I'm curious if you feel this way too. Everyone feels committed. Yeah. Yeah. I I I feel like there's no question that Kubernetes is the tool that we are gonna be using. >>Yeah. I I think so. And I think a lot of that is actually being unlocked by some of these vendors that are being partners and helping people get the most outta Kubernetes, you know, especially at the larger enterprise organizations. Like they want to do it, but the skills gap is a very real problem. Right. And so figuring out, like Jasmine talked about figuring out how do we, you know, optimize or set up the correct settings without throwing thousands of humans at it. Never mind the fact you'll never find a thousand people that wanna do that all day every day. >>I was gonna, It's a fold endeavor for those >>People study, right? Yeah. And, and being able to close some of those gaps, whether it's optimization, security, DevOps, C I C D. As we get more of those partners like I just talked about on the floor, then you see more and more enterprises being more open to leaning into Kubernetes a little bit. >>Yeah. Yeah. We've seen, we've had some great conversations the last day and, and today as well with organizations that are history companies like Ford Motor Companies for >>Example. Yeah. Right. >>Just right behind us. One of their EVs and, and it's, they're becoming technology companies that happen to do cars or home >>Here. I had a nice job with 'em this morning. Yes. With that storyline, honestly. >>Yes. That when we now have such a different lens into these organizations, how they're using technologies, advanced technologies, Kubernetes, et cetera, to really become data companies. Yeah. Because they have to be, well the consumers on the other end expect a Home Depot or a Ford or whomever or your bank Yeah. To know who you are. I want the information right here whenever I need it so I can do the transaction I need and I want you to also deliver me information that is relevant to me. Yeah. Because there, there's no patience anymore. Yeah. >>And we partner with a lot of big FinTech companies and it's, it's very much that. It's like how do we continue to optimize? But then as they look at transitioning off of older organizations and capabilities, whether that's, they have a physical data center that's racked to the gills and they can't do anything about that, so they wanna move to cloud or they're just dipping their toe into even private cloud with Kubernetes in their own instances. A lot of it is how do we do this right? Like how do we lean in and, Yeah. >>Yeah. Well I think you said it really well that the debate seems to be over in terms of do we go in on Kubernetes? That that was a theme that I think we felt that yesterday, even on on day one of the keynotes. The community seems to be just craving more. I think that was another thing that we felt yesterday was all of the contributors and the collaborators, people want to be able to help drive this community forward because it's, it's a flywheel of symbiosis for all of the vendors here. The maintainers and, and really businesses in any industry can benefit. >>Yeah. It's super validating. I mean if you just look at the floor, there's like 20 different booths that talk about cost reporting for Kubernetes. So not only have people moved, but now they're dealing with those challenges at scale. And I think for us it's very validating because there's so many vendors that are looking into the reporting of this and showing you the problem that you have. And then where we can help is, okay, now you know, you have a problem, here's how we can fix it for you. >>Yeah. Yeah. That, that sort of dealing with challenges at scale that you set, I think that's also what we're hearing. Yeah. And seeing and feeling on the show floor. >>Yeah, absolutely. >>What can folks see and, and touch and feel in your booth? >>We have some demos there you can play around with the product. We're giving away a Lego set so we've let >>Gotta gets >>Are right now we're gonna have to get some Lego, We do a swag segment at the end of the day every day. Now we've >>Some cool socks. >>Yep. Socks are hot. Let's, let's actually talk about scale internally as our closing question. What's going on at Storm Forge? If someone's watching right now, they're excited. Are you hiring? We are hiring. Yeah. How can they stalk you? What's the >>School? Absolutely. So you can check us out on Storm forge.io. We're certainly hiring across the engineering organization. We're hiring across the UX a product organization. We're dealing, like I said, we've got some really big customers that we're, we're working through with some really fun challenges. And we're looking to continue to build on what we do and do new innovative things like especially cuz like I said, we are a machine learning organization first. And so for me it's like how do I collect all the data that I can and then let's find out what's interesting in there that we can help people with. Whether that's cpu, memory, custom metrics, like as said, preventing kills, driving availability, reliability, What can we do to, to kind of make a little bit more transparent the stuff that's going on underneath the covers in Kubernetes for the decision makers in these organizations. >>Yes. Transparency is a goal of >>Many. >>Yeah, absolutely. Well, and you mentioned fun. If this conversation is any representation, it would be very fun to be working on both of your teams. We, we have a lot of fun Ya. Patrick, thank you so much for joining. Thanks for having us, Lisa, As usual, thanks for being here with me. My pleasure. And thank you to all of you for turning into the Cubes live show from Detroit. My name's Savannah Peterson and we'll be back in a few.

>>The cube presents, Coon and cloud native con Europe 22, brought to you by the cloud native computing foundation. >>Welcome to Melissa Spain. And we're at cuon cloud native con Europe, 2022. I'm Keith Townsend. And my co-host en Rico senior Etti en Rico's really proud of me. I've called him en Rico and said IK, every session, senior it analyst giga, O we're talking to fantastic builders at Cuban cloud native con about the projects and the efforts en Rico up to this point, it's been all about provisioning insecurity. What, what conversation have we been missing? >>Well, I mean, I, I think, I think that, uh, uh, we passed the point of having the conversation of deployment of provisioning. You know, everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem. And in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their classroom, what, why it is happening and all the, the questions that come with it. I mean, and, uh, the more I talk with, uh, people in the, in the show floor here, or even in the, you know, in the various sessions is about, you know, we are growing, the, our clusters are becoming bigger and bigger. Uh, applications are becoming, you know, bigger as well. So we need to know, understand better what is happening. It's not only, you know, about cost it's about everything at the >>End. So I think that's a great set up for our guests, max, Provo, founder, and CEO of storm for forge and Patrick Britton, Bergstrom, Brookstone. Yeah, I spelled it right. I didn't say it right. Berg storm CTO. We're at Q con cloud native con we're projects are discussed, built and storm forge. I I've heard the pitch before, so forgive me. And I'm, I'm, I'm, I'm, I'm, I'm kind of torn. I have service mesh. What do I need more like, what problem is storm for solving? >>You wanna take it? >>Sure, absolutely. So it it's interesting because, uh, my background is in the enterprise, right? I was an executive at United health group. Um, before that I worked at best buy. Um, and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky Dory. Right. Uh, but then we run into the issue like you and I were just talking about where it gets very, very expensive, very quickly. Uh, and so my first conversations with Matt and the storm forge group, and they were telling me about the product and, and what we're dealing with. I said, that is the problem statement that I have always struggled with. And I wish this existed 10 years ago when I was dealing with EC two costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically, what it is is we take your raw telemetry data and we essentially monitor the performance of your application. And then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over provisioning. So we reduce your consumption of CPU of memory and production, which ultimately nine times outta 10, actually I would say 10 out of 10 reduces your cost significantly without sacrificing reliability. >>So can your solution also help to optimize the application in the long run? Because yes, of course, yep. You know, the lowing fluid is, you know, optimize the deployment. Yeah. But actually the long term is optimizing the application. Yes. Which is the real problem. >>Yep. So we actually, um, we're fine with the, the former of what you just said, but we exist to do the latter. And so we're squarely and completely focused at the application layer. Um, we are, uh, as long as you can track or understand the metrics you care about for your application, uh, we can optimize against it. Um, we love that we don't know your application. We don't know what the SLA and SLO requirements are for your app. You do. And so in, in our world, it's about empowering the developer into the process, not automating them out of it. And I think sometimes AI and machine learning sort of gets a bad wrap from that standpoint. And so, uh, we've at this point, the company's been around, you know, since 2016, uh, kind of from the very early days of Kubernetes, we've always been, you know, squarely focused on Kubernetes using our core machine learning, uh, engine to optimize metrics at the application layer, uh, that people care about and, and need to need to go after. And the truth of the matter is today. And over time, you know, setting a cluster up on Kubernetes has largely been solved. Um, and yet the promise of, of Kubernetes around portability and flexibility, uh, downstream when you operationalize the complexity, smacks you in the face. And, uh, and that's where, where storm forge comes in. And so we're a vertical, you know, kind of vertically oriented solution. Um, that's, that's absolutely focused on solving that problem. >>Well, I don't want to play, actually. I want to play the, uh, devils advocate here and, you know, >>You wouldn't be a good analyst if you didn't. >>So the, the problem is when you talk with clients, users, they, there are many of them still working with Java with, you know, something that is really tough. Mm-hmm <affirmative>, I mean, we loved all of us loved Java. Yeah, absolutely. Maybe 20 years ago. Yeah. But not anymore, but still they have developers. They are porting applications, microservices. Yes. But not very optimized, etcetera. C cetera. So it's becoming tough. So how you can interact with these kind of yeah. Old hybrid or anyway, not well in generic applications. >>Yeah. We, we do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage. And we like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So the perfect example is Java, you know, you have to worry about your heap size, your garbage collection tuning. Um, and one of the things that really struck, struck me very early on about the storm forage product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and, and performance tuning, we were only as good as our humans were because of what they knew. And so we were, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that, the machine will recommend things you never would've dreamed of. And you get amazing results out of >>That. So both me and an Rico have been doing this for a long time. Like I have battled to my last breath, the, the argument when it's a bare metal or a VM. Yeah. Look, I cannot give you any more memory. Yeah. And the, the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources expensive, my bigger box. Yep. Uh, buying a bigger box in the cloud to your point is no longer a option because it's just expensive. Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? So is it, that is that if it, is it the shift in responsibility? >>I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially, um, especially as the development of applications becomes more and more rapid. And the management of them, our, our charge and our belief wholeheartedly is that you shouldn't have to choose, you should not have to choose between costs or performance. You should not have to choose where your, you know, your applications live, uh, in a public private or, or hybrid cloud environment. And so we want to empower people to be able to sit in the middle of all of that chaos and for those trade-offs and those difficult interactions to no, no longer be a thing. You know, we're at, we're at a place now where we've done, you know, hundreds of deployments and never once have we met a developer who said, I'm really excited to get outta bed and come to work every day and manually tune my application. <laugh> One side, secondly, we've never met, uh, you know, uh, a manager or someone with budget that said, uh, please don't, you know, increase the value of my investment that I've made to lift and shift us over mm-hmm <affirmative>, you know, to the cloud or to Kubernetes or, or some combination of both. And so what we're seeing is the converging of these groups, um, at, you know, their happy place is the lack of needing to be able to, uh, make those trade offs. And that's been exciting for us. So, >>You know, I'm listening and looks like that your solution is right in the middle in application per performance management, observability. Yeah. And, uh, and monitoring. So it's a little bit of all of this. >>So we, we, we, we want to be, you know, the Intel inside of all of that, mm-hmm, <affirmative>, we don't, you know, we often get lumped into one of those categories. It used to be APM a lot. We sometimes get a, are you observability or, and we're really not any of those things in and of themselves, but we, instead of invested in deep integrations and partnerships with a lot of those, uh, with a lot of that tooling, cuz in a lot of ways, the, the tool chain is hardening, uh, in a cloud native and, and Kubernetes world. And so, you know, integrating in intelligently staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for, for our users who have already invested likely in a APM or observability. >>So to go a little bit deeper. Sure. What does it mean integration? I mean, do you provide data to this, you know, other applications in, in the environment or are they supporting you in the work that you >>Yeah, we're, we're a data consumer for the most part. Um, in fact, one of our big taglines is take your observability and turn it into actionability, right? Like how do you take the it's one thing to collect all of the data, but then how do you know what to do with it? Right. So to Matt's point, um, we integrate with folks like Datadog. Um, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >>But, but also we want Datadog customers. For example, we have a very close partnership with, with Datadog, so that in your existing data dog dashboard, now you have yeah. This, the storm for capability showing up in the same location. Yep. And so you don't have to switch out. >>So I was just gonna ask, is it a push pull? What is the developer experience? When you say you provide developer, this resolve ML, uh, learnings about performance mm-hmm <affirmative> how do they receive it? Like what, yeah, what's the, what's the, what's the developer experience >>They can receive it. So we have our own, we used to for a while we were CLI only like any good developer tool. Right. Uh, and you know, we have our own UI. And so it is a push in that, in, in a lot of cases where I can come to one spot, um, I've got my applications and every time I'm going to release or plan for a release or I have released, and I want to take, pull in, uh, observability data from a production standpoint, I can visualize all of that within the storm for UI and platform, make decisions. We allow you to, to set your, you know, kind of comfort level of automation that you're, you're okay with. You can be completely set and forget, or you can be somewhere along that spectrum. And you can say, as long as it's within, you know, these thresholds, go ahead and release the application or go ahead and apply the configuration. Um, but we also allow you to experience, uh, the same, a lot of the same functionality right now, you know, in Grafana in Datadog, uh, and a bunch of others that are coming. >>So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges, or if not, one of the biggest challenges CIOs are facing are resource constraints. Yeah. They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs? Yeah. >>Development? >>Just take that one. Yeah, absolutely. That's um, so like my background, like I said, at United health group, right. It's not always just about cost savings. In fact, um, the way that I look about at some of these tech challenges, especially when we talk about scalability, there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece, cuz you can only throw money at a problem for so long. And it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small. And so we are absolutely squarely in that footprint of, we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. >>Like we were, you were talking about private cloud for instance. And so having a physical data center, um, I've worked with physical data centers that companies I've worked for have owned where it is literally full wall to wall. You can't rack any more servers in it. And so their biggest option is, well, I could spend 1.2 billion to build a new one if I wanted to. Or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster. Like that's a huge opportunity. >>So either out of question, I mean, may, maybe it, it doesn't sound very intelligent at this point, but so is it an ongoing process or is it something that you do at the very beginning mean you start deploying this. Yeah. And maybe as a service. Yep. Once in a year I say, okay, let's do it again and see if something changes. Sure. So one spot 1, 1, 1 single, you know? >>Yeah. Um, would you recommend somebody performance tests just once a year? >>Like, so that's my thing is, uh, previous at previous roles I had, uh, my role was you performance test, every single release. And that was at a minimum once a week. And if your thing did not get faster, you had to have an executive exception to get it into production. And that's the space that we wanna live in as well as part of your C I C D process. Like this should be continuous verification every time you deploy, we wanna make sure that we're recommending the perfect configuration for your application in the name space that you're deploying >>Into. And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the C I C D process that's connected to optimization and that no application should be released monitored and sort of, uh, analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, yeah. Cost end performance, >>Almost a couple of hundred vendors on this floor. You know, you mentioned some of the big ones, data, dog, et cetera. But what happens when one of the up and comings out of nowhere, completely new data structure, some imaginable way to click to elementry data. Yeah. How do, how do you react to that? >>Yeah. To us it's zeros and ones. Yeah. Uh, and you know, we're, we're, we're really, we really are data agnostic from the standpoint of, um, we're not, we we're fortunate enough to, from the design of our algorithm standpoint, it doesn't get caught up on data structure issues. Um, you know, as long as you can capture it and make it available, uh, through, you know, one of a series of inputs, what one, one would be load or performance tests, uh, could be telemetry, could be observability if we have access to it. Um, honestly the messier, the, the better from time to time, uh, from a machine learning standpoint, um, it, it, it's pretty powerful to see we've, we've never had a deployment where we, uh, where we saved less than 30% while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and, you know, 30 to 40% improvement in performance. >>And what happens if the application is, I, I mean, yes, Kubernetes is the best thing of the world, but sometimes we have to, you know, external data sources or, or, you know, we have to connect with external services anyway. Mm-hmm <affirmative> yeah. So can you, you know, uh, can you provide an indication also on, on, on this particular application, like, you know, where the problem could >>Be? Yeah, yeah. And that, that's absolutely one of the things that we look at too, cuz it's um, especially when you talk about resource consumption, it's never a flat line, right? Like depending on your application, depending on the workloads that you're running, um, it varies from sometimes minute to minute, day to day, or it could be week to week even. Um, and so especially with some of the products that we have coming out with what we want to do, you know, partnering with, uh, you know, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like. Um, or you know, what your disc looks like, right? Like cuz with our, our low environment testing, any metric you throw at us, we can, we can optimize for. >>So Madden Patrick, thank you for stopping by. Yeah. Yes. We can go all day. Because day two is I think the biggest challenge right now. Yeah. Not just in Kubernetes, but application replatforming and re and transformation. Very, very difficult. Most CTOs and S that I talked to, this is the challenge space from Valencia Spain. I'm Keith Townsend, along with my host en Rico senior. And you're watching the queue, the leader in high tech coverage.

>>Hi everybody. This is Dave Volante. Welcome to this cube conversation where we're going to go back in time a little bit and explore the early days of Kubernetes. Talk about how it formed the improbable events, perhaps that led to it. And maybe how customers are taking advantage of containers and container orchestration today, and maybe where the industry is going. Matt Provo is here. He's the founder and CEO of storm forge and Chandler Huntington hoes. Hoisington is the general manager of EKS edge and hybrid AWS guys. Thanks for coming on. Good to see you. Thanks for having me. Thanks. So, Jenny, you were the vice president of engineering at miso sphere. Is that, is that correct? >>Well, uh, vice-president engineering basis, fear and then I ran product and engineering for DTQ masons. >>Yeah. Okay. Okay. So you were there in the early days of, of container orchestration and Matt, you, you were working at a S a S a Docker swarm shop, right? Yep. Okay. So I mean, a lot of people were, you know, using your platform was pretty novel at the time. Uh, it was, it was more sophisticated than what was happening with, with Kubernetes. Take us back. What was it like then? Did you guys, I mean, everybody was coming out. I remember there was, I think there was one Docker con and everybody was coming, the Kubernetes was announced, and then you guys were there, doc Docker swarm was, was announced and there were probably three or four other startups doing kind of container orchestration. And what, what were those days like? Yeah. >>Yeah. I wasn't actually atmosphere for those days, but I know them well, I know the story as well. Um, uh, I came right as we started to pivot towards Kubernetes there, but, um, it's a really interesting story. I mean, obviously they did a documentary on it and, uh, you know, people can watch that. It's pretty good. But, um, I think that, from my perspective, it was, it was really interesting how this happened. You had basically, uh, con you had this advent of containers coming out, right? So, so there's new novel technology and Solomon, and these folks started saying, Hey, you know, wait a second, wait if I put a UX around these couple of Linux features that got launched a couple of years ago, what does that look like? Oh, this is pretty cool. Um, so you have containers starting to crop up. And at the same time you had folks like ThoughtWorks and other kind of thought leaders in the space, uh, starting to talk about microservices and saying, Hey, monoliths are bad and you should break up these monoliths into smaller pieces. >>And any Greenfield application should be broken up into individuals, scalable units that a team can can own by themselves, and they can scale independent of each other. And you can write tests against them independently of other components. And you should break up these big, big mandalas. And now we are kind of going back to model this, but that's for another day. Um, so, so you had microservices coming out and then you also had containers coming out, same time. So there was like, oh, we need to put these microservices in something perfect. We'll put them in containers. And so at that point, you don't really, before that moment, you didn't really need container orchestration. You could just run a workload in a container and be done with it, right? You didn't need, you don't need Kubernetes to run Docker. Um, but all of a sudden you had tons and tons of containers and you had to manage these in some way. >>And so that's where container orchestration came, came from. And, and Ben Heineman, the founder of Mesa was actually helping schedule spark at the time at Berkeley. Um, and that was one of the first workloads with spark for Macy's. And then his friends at Twitter said, Hey, come over, can you help us do this with containers at Twitter? He said, okay. So when it helped them do it with containers at Twitter, and that's kinda how that branch of the container wars was started. And, um, you know, it was really, really great technology and it actually is still in production in a lot of shops today. Um, uh, more and more people are moving towards Kubernetes and Mesa sphere saw that trend. And at the end of the day, Mesa sphere was less concerned about, even though they named the company Mesa sphere, they were less concerned about helping customers with Mesa specifically. They really want to help customers with these distributed problems. And so it didn't make sense to, to just do Mesa. So they would took on Kubernetes as well. And I hope >>I don't do that. I remember, uh, my, my co-founder John furrier introduced me to Jerry Chen way back when Jerry is his first, uh, uh, VC investment with Greylock was Docker. And we were talking in these very, obviously very excited about it. And, and his Chandler was just saying, it said Solomon and the team simplified, you know, containers, you know, simple and brilliant. All right. So you guys saw the opportunity where you were Docker swarm shop. Why? Because you needed, you know, more sophisticated capabilities. Yeah. But then you, you switched why the switch, what was happening? What was the mindset back then? We ran >>And into some scale challenges in kind of operationalize or, or productizing our kind of our core machine learning. And, you know, we, we, we saw kind of the, the challenges, luckily a bit ahead of our time. And, um, we happen to have someone on the team that was also kind of moonlighting, uh, as one of the, the original core contributors to Kubernetes. And so as this sort of shift was taking place, um, we, we S we saw the flexibility, uh, of what was becoming Kubernetes. Um, and, uh, I'll never forget. I left on a Friday and came back on a Monday and we had lifted and shifted, uh, to Kubernetes. Uh, the challenge was, um, you know, you, at that time, you, you didn't have what you have today through EKS. And, uh, those kinds of services were, um, just getting that first cluster up and running was, was super, super difficult, even in a small environment. >>And so I remember we, you know, we, we finally got it up and running and it was like, nobody touch it, don't do anything. Uh, but obviously that doesn't, that doesn't scale either. And so that's really, you know, being kind of a data science focused shop at storm forge from the very beginning. And that's where our core IP is. Uh, our, our team looked at that problem. And then we looked at, okay, there are a bunch of parameters and ways that I can tune this application. And, uh, why are the configurations set the way that they are? And, you know, uh, is there room to explore? And that's really where, unfortunately, >>Because Mesa said much greater enterprise capabilities as the Docker swarm, at least they were heading in that direction, but you still saw that Kubernetes was, was attractive because even though it didn't have all the security features and enterprise features, because it was just so simple. I remember Jen Goldberg who was at Google at the time saying, no, we were focused on keeping it simple and we're going from mass adoption, but does that kind of what you said? >>Yeah. And we made a bet, honestly. Uh, we saw that the, uh, you know, the growing community was really starting to, you know, we had a little bit of an inside view because we had, we had someone that was very much in the, in the original part, but you also saw the, the tool chain itself start to, uh, start to come into place right. A little bit. And it's still hardening now, but, um, yeah, we, as any, uh, as any startup does, we, we made a pivot and we made a bet and, uh, this, this one paid off >>Well, it's interesting because, you know, we said at the time, I mean, you had, obviously Amazon invented the modern cloud. You know, Microsoft has the advantage of has got this huge software stays, Hey, just now run it into the cloud. Okay, great. So they had their entry point. Google didn't have an entry point. This is kind of a hail Mary against Amazon. And, and I, I wrote a piece, you know, the improbable, Verizon, who Kubernetes to become the O S you know, the cloud, but, but I asked, did it make sense for Google to do that? And it never made any money off of it, but I would argue they, they were kind of, they'd be irrelevant if they didn't have, they hadn't done that yet, but it didn't really hurt. It certainly didn't hurt Amazon EKS. And you do containers and your customers you've embraced it. Right. I mean, I, I don't know what it was like early days. I remember I've have talked to Amazon people about this. It's like, okay, we saw it and then talk to customers, what are they doing? Right. That's kind of what the mindset is, right? Yeah. >>That's, I, I, you know, I've, I've been at Amazon a couple of years now, and you hear the stories of all we're customer obsessed. We listened to our customers like, okay, okay. We have our company values, too. You get told them. And when you're, uh, when you get first hired in the first day, and you never really think about them again, but Amazon, that really is preached every day. It really is. Um, uh, and that we really do listen to our customers. So when customers start asking for communities, we said, okay, when we built it for them. So, I mean, it's, it's really that simple. Um, and, and we also, it's not as simple as just building them a Kubernetes service. Amazon has a big commitment now to start, you know, getting involved more in the community and working with folks like storm forage and, and really listening to customers and what they want. And they want us working with folks like storm florigen and that, and that's why we're doing things like this. So, well, >>It's interesting, because of course, everybody looks at the ecosystem, says, oh, Amazon's going to kill the ecosystem. And then we saw an article the other day in, um, I think it was CRN, did an article, great job by Amazon PR, but talk about snowflake and Amazon's relationship. And I've said many times snowflake probably drives more than any other ISV out there. And so, yeah, maybe the Redshift guys might not love snowflake, but Amazon in general, you know, they're doing great three things. And I remember Andy Jassy said to me, one time, look, we love the ecosystem. We need the ecosystem. They have to innovate too. If they don't, you know, keep pace, you know, they're going to be in trouble. So that's actually a healthy kind of a dynamic, I mean, as an ecosystem partner, how do you, >>Well, I'll go back to one thing without the work that Google did to open source Kubernetes, a storm forge wouldn't exist, but without the effort that AWS and, and EKS in particular, um, provides and opens up for, for developers to, to innovate and to continue, continue kind of operationalizing the shift to Kubernetes, um, you know, we wouldn't have nearly the opportunity that we do to actually listen to them as well, listen to the users and be able to say, w w w what do you want, right. Our entire reason for existence comes from asking users, like, how painful is this process? Uh, like how much confidence do you have in the, you know, out of the box, defaults that ship with your, you know, with your database or whatever it is. And, uh, and, and how much do you love, uh, manually tuning your application? >>And, and, uh, obviously nobody's said, I love that. And so I think as that ecosystem comes together and continues expanding, um, it's just, it opens up a huge opportunity, uh, not only for existing, you know, EKS and, uh, AWS users to continue innovating, but for companies like storm forge, to be able to provide that opportunity for them as well. And, and that's pretty powerful. So I think without a lot of the moves they've made, um, you know, th the door wouldn't be nearly as open for companies like, who are, you know, growing quickly, but are smaller to be able to, you know, to exist. >>Well, and I was saying earlier that, that you've, you're in, I wrote about this, you're going to get better capabilities. You're clearly seeing that cluster management we've talked about better, better automation, security, the whole shift left movement. Um, so obviously there's a lot of momentum right now for Kubernetes. When you think about bare metal servers and storage, and then you had VM virtualization, VMware really, and then containers, and then Kubernetes as another abstraction, I would expect we're not at the end of the road here. Uh, what's next? Is there another abstraction layer that you would think is coming? Yeah, >>I mean, w for awhile, it looked like, and I remember even with our like board members and some of our investors said, well, you know, well, what about serverless? And, you know, what's the next Kubernetes and nothing, we, as much as I love Kubernetes, um, which I do, and we do, um, nothing about what we particularly do. We are purpose built for Kubernetes, but from a core kind of machine learning and problem solving standpoint, um, we could apply this elsewhere, uh, if we went that direction and so time will tell what will be next, then there will be something, uh, you know, that will end up, you know, expanding beyond Kubernetes at some point. Um, but, you know, I think, um, without knowing what that is, you know, our job is to, to, to serve our, you know, to serve our customers and serve our users in the way that they are asking for that. >>Well, serverless obviously is exploding when you look again, and we tucked the ETR survey data, when you look at, at the services within Amazon and other cloud providers, you know, the functions off, off the charts. Uh, so that's kind of an interesting and notable now, of course, you've got Chandler, you've got edge in your title. You've got hybrid in, in your title. So, you know, this notion of the cloud expanding, it's not just a set of remote services, just only in the public cloud. Now it's, it's coming to on premises. You actually got Andy, Jesse, my head space. He said, one time we just look at it. The data centers is another edge location. Right. Okay. That's a way to look at it and then you've got edge. Um, so that cloud is expanding, isn't it? The definition of cloud is, is, is evolving. >>Yeah, that's right. I mean, customers one-on-one run workloads in lots of places. Um, and that's why we have things like, you know, local zones and wavelengths and outposts and EKS anywhere, um, EKS, distro, and obviously probably lots more things to come. And there's, I always think of like, Amazon's Kubernetes strategy on a manageability scale. We're on one far end of the spectrum, you have EKS distro, which is just a collection of the core Kubernetes packages. And you could, you could take those and stand them up yourself in a broom closet, in a, in a retail shop. And then on the other far in the spectrum, you have EKS far gate where you can just give us your container and we'll handle everything for you. Um, and then we kind of tried to solve everything in between for your data center and for the cloud. And so you can, you can really ask Amazon, I want you to manage my control plane. I want you to manage this much of my worker nodes, et cetera. And oh, I actually want help on prem. And so we're just trying to listen to customers and solve their problems where they're asking us to solve them. Cut, >>Go ahead. No, I would just add that in a more vertically focused, uh, kind of orientation for us. Like we, we believe that op you know, optimization capabilities should transcend the location itself. And, and, and so whether that's part public part, private cloud, you know, that's what I love part of what I love about EKS anywhere. Uh, it, you know, you shouldn't, you should still be able to achieve optimal results that connect to your business objectives, uh, wherever those workloads, uh, are, are living >>Well, don't wince. So John and I coined this term called Supercloud and people laugh about it, but it's different. It's, it's, you know, people talk about multi-cloud, but that was just really kind of vendor diversity. Right? I got to running here, I'm running their money anywhere. Uh, but, but individually, and so Supercloud is this concept of this abstraction layer that floats wherever you are, whether it's on prem, across clouds, and you're taking advantage of those native primitives, um, and then hiding that underlying complexity. And that's what, w re-invent the ecosystem was so excited and they didn't call it super cloud. We, we, we called it that, but they're clearly thinking differently about the value that they can add on top of Goldman Sachs. Right. That to me is an example of a Supercloud they're taking their on-prem data and their, their, their software tooling connecting it to AWS. They're running it on AWS, but they're, they're abstracting that complexity. And I think you're going to see a lot, a lot more of that. >>Yeah. So Kubernetes itself, in many cases is being abstracted away. Yeah. There's a disability of a disappearing act for Kubernetes. And I don't mean that in a, you know, in an, a, from an adoption standpoint, but, uh, you know, Kubernetes itself is increasingly being abstracted away, which I think is, is actually super interesting. Yeah. >>Um, communities doesn't really do anything for a company. Like we run Kubernetes, like, how does that help your bottom line? That at the end of the day, like companies don't care that they're running Kubernetes, they're trying to solve a problem, which is the, I need to be able to deploy my applications. I need to be able to scale them easily. I need to be able to update them easily. And those are the things they're trying to solve. So if you can give them some other way to do that, I'm sure you know, that that's what they want. It's not like, uh, you know, uh, a big bank is making more money because they're running Kubernetes. That's not, that's not the current, >>It gets subsumed. It's just become invisible. Right. Exactly. You guys back to the office yet. What's, uh, what's the situation, >>You know, I, I work for my house and I, you know, we go into the office a couple of times a week, so it's, it's, uh, yeah, it's, it's, it's a crazy time. It's a crazy time to be managing and hiring. And, um, you know, it's, it's, it's, it's definitely a challenge, but there's a lot of benefits of working home. I got two young kids, so I get to see them, uh, grow up a little bit more working, working out of my house. So it's >>Nice also. >>So we're in, even as a smaller startup, we're in 26, 27 states, uh, Canada, Germany, we've got a little bit of presence in Japan, so we're very much distributed. Um, we, uh, have not gone back and I'm not sure we will >>Permanently remote potentially. >>Yeah. I mean, w we made a, uh, pretty like for us, the timing of our series B funding, which was where we started hiring a lot, uh, was just before COVID started really picking up. So we, you know, thankfully made a, a pretty good strategic decision to say, we're going to go where the talent is. And yeah, it was harder to find for sure, especially in w we're competing, it's incredibly competitive. Uh, but yeah, we've, it was a good decision for us. Um, we are very about, you know, getting the teams together in person, you know, as often as possible and in the safest way possible, obviously. Um, but you know, it's been a, it's been a pretty interesting, uh, journey for us and something that I'm, I'm not sure I would, I would change to be honest with you. Yeah. >>Well, Frank Slootman, snowflakes HQ to Montana, and then can folks like Michael Dell saying, Hey, same thing as you, wherever they want to work, bring yourself and wherever you are as cool. And do you think that the hybrid mode for your team is kind of the, the, the operating mode for the, for the foreseeable future is a couple of, >>No, I think, I think there's a lot of benefits in both working from the office. I don't think you can deny like the face-to-face interactions. It feels good just doing this interview face to face. Right. And I can see your mouth move. So it's like, there's a lot of benefits to that, um, over a chime call or a zoom call or whatever, you know, that, that also has advantages, right. I mean, you can be more focused at home. And I think some version of hybrid is probably in the industry's future. I don't know what Amazon's exact plans are. That's above my pay grade, but, um, I know that like in general, the industry is definitely moving to some kind of hybrid model. And like Matt said, getting people I'm a big fan at Mesa sphere, we ran a very diverse, like remote workforce. We had a big office in Germany, but we'd get everybody together a couple of times a year for engineering week or, or something like this. And you'd get a hundred people, you know, just dedicated to spending time together at a hotel and, you know, Vegas or Hamburg or wherever. And it's a really good time. And I think that's a good model. >>Yeah. And I think just more ETR data, the current thinking now is that, uh, the hybrid is the number one sort of model, uh, 36% that the CIO is believe 36% of the workforce are going to be hybrid permanently is kind of their, their call a couple of days in a couple of days out. Um, and the, the percentage that is remote is significantly higher. It probably, you know, high twenties, whereas historically it's probably 15%. Yeah. So permanent changes. And that, that changes the infrastructure. You need to support it, the security models and everything, you know, how you communicate. So >>When COVID, you know, really started hitting and in 2020, um, the big banks for example, had to, I mean, you would want to talk about innovation and ability to, to shift quickly. Two of the bigger banks that have in, uh, in fact, adopted Kubernetes, uh, were able to shift pretty quickly, you know, systems and things that were, you know, historically, you know, it was in the office all the time. And some of that's obviously shifted back to a certain degree, but that ability, it was pretty remarkable actually to see that, uh, take place for some of the larger banks and others that are operating in super regulated environments. I mean, we saw that in government agencies and stuff as well. >>Well, without the cloud, no, this never would've happened. Yeah. >>And I think it's funny. I remember some of the more old school manager thing people are, aren't gonna work less when they're working from home, they're gonna be distracted. I think you're seeing the opposite where people are too much, they get burned out because you're just running your computer all day. And so I think that we're learning, I think everyone, the whole industry is learning. Like, what does it mean to work from home really? And, uh, it's, it's a fascinating thing is as a case study, we're all a part of right now. >>I was talking to my wife last night about this, and she's very thoughtful. And she w when she was in the workforce, she was at a PR firm and a guy came in a guest speaker and it might even be in the CEO of the company asking, you know, what, on average, what time who stays at the office until, you know, who leaves by five o'clock, you know, a few hands up, or who stays until like eight o'clock, you know, and enhancement. And then, so he, and he asked those people, like, why, why can't you get your work done in a, in an eight hour Workday? I go home. Why don't you go in? And I sit there. Well, that's interesting, you know, cause he's always looking at me like, why can't you do, you know, get it done? And I'm saying the world has changed. Yeah. It really has where people are just on all the time. I'm not sure it's sustainable, quite frankly. I mean, I think that we have to, you know, as organizations think about, and I see companies doing it, you guys probably do as well, you know, take a four day, you know, a week weekend, um, just for your head. Um, but it's, there's no playbook. >>Yeah. Like I said, we're a part of a case study. It's also hard because people are distributed now. So you have your meetings on the east coast, you can wake up at seven four, and then you have meetings on the west coast. You stay until seven o'clock therefore, so your day just stretches out. So you've got to manage this. And I think we're, I think we'll figure it out. I mean, we're good at figuring this stuff. >>There's a rise in asynchronous communication. So with things like slack and other tools, as, as helpful as they are in many cases, it's a, it, isn't always on mentality. And like, people look for that little green dot and you know, if you're on the you're online. So my kids, uh, you know, we have a term now for me, cause my office at home is upstairs and I'll come down. And if it's, if it's during the day, they'll say, oh dad, you're going for a walk and talk, you know, which is like, it was my way of getting away from the desk, getting away from zoom. And like, you know, even in Boston, uh, you know, getting outside, trying to at least, you know, get a little exercise or walk and get, you know, get my head away from the computer screen. Um, but even then it's often like, oh, I'll get a slack notification on my phone or someone will call me even if it's not a scheduled walk and talk. Um, uh, and so it is an interesting, >>A lot of ways to get in touch or productivity is presumably going to go through the roof. But now, all right, guys, I'll let you go. Thanks so much for coming to the cube. Really appreciate it. And thank you for watching this cube conversation. This is Dave Alante and we'll see you next time.

>> 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.