>> Narrator: Live from San Diego, California. It's theCube! Covering KubeCon and CloudNativeCon, brought to you by Red Hat. A cloud native computing foundation. >> Welcome back. 12 thousand here in attendance for KubeCon CloudNativeCon 2019 in San Diego. I am Stu Miniman, my cohost for this afternoon is John troyer. And happy to welcome to the program, recently out of Stealth, two gentlemen from Chronosphere, Austin. To my right is Martin Mao who is the co-founder and CEO and his co-founder Rob Skillington, who's also the CTO, we've stated on theCUBE actually, you understand where this conference is, where co-founder and CTO is like you know, the most prominent title that we've seen to get on here, because that's the type of geeks we love on the program and in this community. So first of all, congratulations on the launch >> Thank you so much >> And thank you so much for joining us. >> No worries. >> All right, when I've got the founders on, I'm going to start with the whys. How was kind of the problem statement, where you were coming from, and what led to the creation of Chronosphere. >> For sure for sure. So with Chronosphere we found a actual gap in the monitoring market, and a very crowded monitoring market, we found a gap, and the gap exists when companies with very large complex technology stacks, or large enterprises, move on to Cloud Native Technology and Kubernetes. So with this migration, what we've found was there's actually a lot more monitoring data being produced, because there's a lot more pieces now, we're moving from monoliths microservices, we're moving from like physical machines to VMs, to containers and pods. And that generates a lot more things that you need to monitor and track. And not only a lot more things, but you generally monitoring the relationship between these things. So as the number of things increases, the number of relationships exponentially increases. So yeah, that's the sort of problem we're solving, it's like monitoring all of these things at large scale, and when we couldn't find anything, and I could even store all of theses things, so that's it sort of. >> All right, so what is the background of the team that made you into position to work on this problem? >> Yeah great question. I mean me and Martin go back quite a few years. I officiated his wedding, only very very recently actually. And I, yeah we basically work together at several different companies. You know, I think both of us are entrepreneurial at heart. I'll let Martin talk a little bit more about the last few years. >> Yeah, so like you know, a few years ago we started working at Uber. And at Uber, we went through this migrations to our native communities and through that migration that's when we sort of had to solve the problem ourselves. And we solved the problem at Uber, with an open-source project called M3. That's really where this whole thing started. And Chronosphere sort of you know, building on top of M3, and now providing a product on top of the open-source platform that we created. >> Can we talk a little bit about the business? I noticed that you know, there are many ways of approaching open-source, in 2019, you know open core and but also as a service. So can you talk a little bit about how you've approached your business model. >> Yeah for sure. So we're very much in the position or in the camp of as a service, right, because you know a lot of companies do do open core, and they're sort of going into the enterprise support model, we sort of didn't want to go down that route. And also with our open-source product, it's not really an end to end solution in itself, like you use an open-source M3, but you still need to plug it together with other things yourself. So what we really wanted to do was to give customers, and end to end solution, and that was built on top of the great technology, we built with M3, but really it solves the problem sort of end to end, and we do that best as a service. >> Rob maybe you can help explain M3 a little bit for us as to how that fits in the landscape, but what it works with and the like. >> Yeah of course. Yeah it's basically at it's heart a metrics platform, that is built on, at first the lower layer in 3DB, which is a distributive time series database. And then on top of that, we have basically an aggregation platform, that is actually aggregating a lot of the samples, and metrics that we're, collecting. So we can really do some transformations on the data, as it comes in, before it's stored in the database itself. And this let's us do a lot of like smart processing, of what signals actually matter, what signals don't matter, kind of like storing them in a way that can be accessed, much faster than like, other typical systems that don't really do any aggregation before it gets stored. And then, you know we have of course like a query engine that works with this distributed set of data, and so, you know, it's really a database that was designed from day one, to be a metric store. You know, it's not built on Cassandra, it doesn't use Rocks DB, at the lower layers, it literarily every part of it, was built for this purpose. >> Can you talk a little bit about dimensionality and cardinality? Because as I look at this observability monitoring space, I see a lot of current discussion about that and frankly a little bit of fighting, and I'm not always, I can kind of see it, why it's important, but what are some of the reasons and what do people do where you know by having it, and what is it actually, let's start with that. >> Yeah for sure. So you know, with this hot topic of like high cardinality and high dimensionality is, what I was talking about earlier, where as you move into cloud native world, you're now monitoring things at like a pod level. So it's like instead of tracking things on like a per host level, you're now tracking things on like a per pod level now, and that is at >> (interjects) You're tracking more things per pod. >> More things per pod and like every pod unit, these are ephemeral pods now, so they don't live for very long. So you end up having more pieces of data and they're kept around for shorter period of time. And now you need a system that can store all of these pieces of data, because you want to see them uniquely. So you want to monitor each individual pod to see exactly what is running at the finest levels. Right, so you actually need technology that can store a lot more data than you could before. >> And I you know, adding to that, there's a lot more people running with like mobile applications, they use you know that are running in markets all round the world, using different cell providers, and different backend services. You may deploy your backend services multiple times, a week or even a day, and if you want to tag you know, the meta data on and slice and dice by that metadata, with your business and with your applications and your system, that requires you know, adding yet another dimension on your data, which adds to that cardinality. Every time you add a dimension, you know that just multiplies the cardinality of your existing data set of monitoring data. >> And it quickly adds up a lot right, so. >> All right Martin, maybe, since you're just out of Stealth, give us some of the speeds and feeds you know, the product GA, is it globally available? Series A funding, who's behind that? >> Yeah so we just kind of still two weeks ago, we closed up Series A a few months ago actually. It was led by Great Luck, we raised 11 million dollars, and our partner at Great Luck is Gary, and we like him very much. And you know the state of the companies that we are currently in private beta right now. So with our hosted platform, we are onboarding to customers into a private offering right now. And early next year, we'll sort of open that up for more public beta. Yeah. >> And the way folks would use this. You'll be using Prometheus or Graphite or something, and you'd be, so you'd have tracing, you'd have logs, you'd have other things and you would be plugging all of them into, into your services. >> Yeah it's a great question. So you mentioned two of the technologies. So if you're Prometheus or Graphite like to try find metrics, both of those can be pushed into the M3 system for sure. We actually just announced a trace integration, this week a KubeCon actually, Rob David spoke about that integration earlier this week at KubeCon. We haven't moved into the logs yet because the way we look at the problem is not from like a sort of like providing a one-stop shop for all observability solutions, we actually look at it from a use case perspective. So the use case we're looking at is like, realtime monitoring and remediation. So tracing is a part of that stroy, it's a critical part of that story, and now to add additional context, when you get to load it based on your metrics, but, we haven't quite moved into logging yet. >> Yeah, and we don't really want to solve any of these problems without knowing it'll work at scale, you know like a fundamental reason we even built the open-source project in the first place, was we were dealing with cardinality in the tens of billions of unique time series, and so, we don't want to just kind of like roll into any, every single feature under the sun, we really want to solve it once correctly and be able to systematically roll that out to enterprises at scale. >> Without, I mean without talking too much about Uber and any Uber secrets, I mean it seems like the game has changed with that kind of a scale of, you could not have done, you can't run Uber if you're tracking all those cars like literarily without some sort of a tracing like high cardinality sort of a system right? Because you're literarily tracking cars all over the world people all over the world, routes all over the world. >> Exactly, well uniquely positioned, we had the requirements to solve it at such a scale, and that's why we had to build this technology to solve it for that unique situation, because you know technologies ahead of time, did not really have this use case to solve. So that's why we had to sort of, we couldn't find anything out in the market because to solve it at that scale, that's why we sort of had to build our own, to uniquely solve it for this use case. >> And yeah, I would add to that, that typically engineers you know, at larger organizations, tend to want to organize everything very nicely, and split it up, and really control how they're monitoring that data, but we've noticed actually, definitely over the last few years, more and more people are open to letting people just start collecting you know, random data, that is relevant to the systems that they're building as they're rolling it out, even as they're experimenting with it, and you know systems today that are built from scratch, to deal with, to be as efficient as possible, with very unstructured data is becoming wildly popular because that's how developers want to develop software. You know, they don't want to have to have to like slice and dice it neatly and package it up and pass it on to others to run. They want to basically slice and dice however they want to, and dynamically , and as they scale up. >> I've always enjoyed every sequel skimmer I've had two, or change oh, yeah. (laughter) >> All right, how have you found the show? How's the reception been? Give us a little bit of the vibe of the show and how it's been going for you. >> Yeah it's been fantastic for us actually. So we just came in at silk so like the name is still quite new, but yeah, we've had a bunch of folks set up with the whole day, we've been giving a demo on the product, so a lot of companies are getting excited about it. I think a we're solving at a scale and that really resonates with, you know, a lot of the people here at the show, we're still solving at a scope, we're solving at a scale that's also in a cost efficient way as well. So that's really been our, we sleep quite well so far. >> Yeah Rob, you gave some sessions. What kind of feedback are you getting from people? Is the problem statement that we talked about at the beginning you know resonating with people that you talk to. >> I mean, I was really, yeah pleased to hear that after my session today, that a lot of people came up to me and said you know, I've never really seen metrics been linked to tracers, the way that we're doing it, in fact that's the first time they'd ever seen a demo, that can do, what we're kind of trying to upstream, we're actually you know, up-streaming a lot of those changes in the open-source well, as well at the same time. And so, you know we've found especially in a lot of the companies today that are pushing everything forward with development wise and how they are running operations is that they using a lot of pages in open source, and then those pages are battle tested in open-source, generally it becomes abstracted, to the point where we're actually a very large amount of people, but then when they need to scale it up, that's when it becomes difficult. So, no I think that you know, a lot of people have been very positive with basically us being able to also push forward the feature on >> Back upstream into the M3 project. >> And also into Prometheus. So I, you know I'm an open metrics, contributor and that's essentially, an exposition format that's built on the Prometheus, exposition format. So it's kind of become a standard way of exchanging metrics, from one system to another. And that's kind of like, basically commoditized and democratize the exchange of metrics to make a lot more systems, interoperable with each another. Which we fundamentally believe in as well, of course we're developing in open-source, and we believe that this systems need to play nicely together. So we can build you know, have building blocks that large companies and organizations can all share and build better things on top of. >> All right, so looking to go to public beta early 2020s, what we said, when we come back in 2020, what kind of the, some of the key KPIs and metrics that you'll be looking at to be successfull in your first year out of Stealth. >> Yeah it's a great question. So you know, since some of the KPIs you guys were looking at doing is coming at the public beta, making it available to a large range of companies, because right now we're sort of onboarding companies sort of one or two at a time, so yeah it's seeing how many companies adopt the product and also, we're again adding more features over time, for that particular use case of like you know, monitoring your technology just like in your business in real time. So it'll be a lot more features coming down the pipeline, and a lot more customer adoption along with that. >> And I would also say you know, our hosted platform is really about offering like deep isolation, between our tenants as well, so basically when we you know, in the next few months to come, we want to make sure that it works basically like clockwork, and everyone can, we can roll out and scale that highly isolated platform for you know tens and hundreds of organizations, and thousands eventually. And so, and doing that at scale is hard. So I think yeah, we'll see how we're doing with that. >> Yeah for sure. >> All right. Rob, Martin congratulations on coming out of Stealth, look forward to hearing more and thank you so much for joining us. >> Glad, thank you so much. >> All right, for John Troyer I'm Stu Miniman, we'll be back getting towards the end of three days, want to walk over here KubeCon, CloudNativeCon thanks for watching. (upbeat music)

>>Hey, welcome back everyone to cube Cod's coverage and cloud native con the I'm John for your husband, David Nicholson cube analyst, cloud analyst. Co-host you got two great guests, KIPP alumni, Jerry Chen needs no introduction partner at Greylock ventures have been on the case many times, almost like an analyst chair. It's great to see you. I got guest analyst and Martin mal who's the CEO co-founder of Chronosphere just closed a whopping $200 million series C round businesses. Booming. Great to see you. Thanks for coming on. Thank you. Hey, first of all, congratulations on the business translations, who would have known that observability and distributed tracing would be a big deal. Jerry, you predicted that in 2013, >>I think we predicted jointly cloud was going to be a big deal with 2013, right? And I think the rise of cloud creates all these markets behind it, right. This, you know, I always say you got to ride a wave bigger than you. And, uh, and so this ride on cloud and scale is the macro wave and, you know, Marty and Robin cryosphere, they're just drafted behind that wave, bigger scale, high cardinality, more data, more apps. I mean, that's, that's where the fuck. >>Yeah. Martin, all kidding aside. You know, we joke about this because we've had conversations where the philosophy of you pick the trend is your friend that you know, is going to be happening. So you can kind of see the big waves coming, but you got to stay true to it. And one of the things that we talk about is what's the next Amazon impact gonna look like? And we were watching the rise of Amazon. You go, if this continues a new way to do things is going to be upon us. Okay, you've got dev ops now, cloud native, but observability became really a key part of that. It's like almost the, I call it the network management in the cloud. It's like in a new way, you guys have been very successful. There's a lot of solutions out there. What's different. >>Yeah. I'd say for Kearney sphere, there's really three big differences. The first thing is that we're a platform. So we're still an observability platform. And by that, I mean, we solved the problem end to end. If thinking about observability and monitoring, you want to know when something's wrong, you want to be able to see how bad it is. And then you want to able to figure out what the root cause is. Often. There are solutions that do a part of that, that that problem might solve a part of the problem really well for a platform that does the whole thing. And 10 that's that's really the first thing. Second thing is we're really built for not just the cloud, but cloud native environments. So a microservices architecture on container-based infrastructure. And that is something that, uh, we, we have saw coming maybe 20 17, 20 18, but luckily for us, we were already solving this problem at Uber. That's where myself, my co-founder were back in 20 14, 20 15. So we already had the sort of perfect technology to solve this problem ahead of where the, the trend was going in the industry and therefore a purpose-built solution for this type of environment, a lot more effective than a lot of the existing. >>It's interesting, Jerry, you know, the view investing companies that have their problem, that they have to solve themselves as the new thing, versus someone says, Hey, there's a market. Let's build a solution for something. I don't really know. Well, that's kind of what's going on here. Right? It's >>That's why we love founders. Like Martin Marna, rod that come out with these hyperscale comes Uber's like we say, they've seen the future. You know, like there were Uber, they looked at the existing solutions out there trying to scale Promethease or you know, data dogs and the vendors. And it didn't work. It fell over, was too expensive. And so Martin Rob saw solid future. Like, this is where the world's going. We're going to solve it. They built MP3. It became cryosphere. And um, so I don't take any credit for that. You know, I just look fine folks that can see the future. >>Yeah. But they were solving their problem. No one else had anything. There's no general purpose software that managed servers you could buy, you guys were cutting your teeth into solving the pain. You had Uber. When did you guys figure out like, oh, well this is pretty big. >>Uh, probably about 20 17, 20 18 with a rise in popularity of Kubernetes. That's when we knew, oh wait, the whole world is shifting to this. It's not, no one could really it to just goober and the big tech giants of the world. And that's when we really knew, okay. The whole, the whole whole world is shifting here. And again, it's, it's sheer blind luck that we already had the ideal solution for this particular environment. It wasn't planned it. Wasn't what we were planning for back then. But, um, yeah. Get everything. >>It makes a lot of difference. When you walk into a customer and say, we had this problem, I can empathize with you. Not just say we've got solved. Exactly. Jerry, how do they compete in the cloud? We always talk about how Amazon and Azure want to eat up anything that they see that might, you know, something on AWS. Um, this castle in the cloud opportunity here. Okay. >>In the cloud. I mean, you know, we talked last time about how to fight the big three, uh, Amazon Azure and, uh, and Google. And I think for sure they have basic offerings, right. You know, Google Stackdriver years ago, they've done basically for Pete's offerings, basic modern offerings. I think you have like basic, simple needs. It's a great way to get started, but customers don't want kind of a piecemeal solution all the time. They want a full product. Like Datadog shows a better user experience, but full product is going to, you know, the better mousetrap the world will beat a path to your door. So first you can build a better product versus these point solutions. Number two is at some scale and some level complexity, those guys can handle like the demanding users that current affairs handling right now, right? The door dash, the world. >>And finally don't want the Fox guarding the hen house. You know, you don't want to say like Amazon monitoring, you can't depend on Amazon service monitoring your Amazon apps or Google service monitor your Google apps, having something that is independent and multi-cloud, that can dual things, Marta said, you know, see a triage, fixed your issues is kind of what you want. And, um, that's where the market's skilling. So I do believe that cloud guys will have an offering the space, but in our castle and cloud research, we saw that, yeah, there's a plenty of startups being funded. There's plenty of opportunity. And that the scoreboard between Splunk Datadog and all these other companies, that there's a huge amount of market and value to be created in this piece. So, >>So with, at, at the time, when you, you know, uh, uh, necessity is the mother of invention, you're an Uber, you have a practical problem to solve and use you look around you and you see that you're not the only entity out there that has this problem. Where are we in that wave? So not everyone is at, cloud-scale not everyone has adopted completely Kubernetes and cloud native for everything. Are we just at the beginning of this wave? How far from the >>Beach are we, we think we're just at the beginning of this wave right now. Um, and if you think about most enterprises today, they're still using on, and they're not even in perhaps in the cloud at all right. Are you still using perhaps APM and solutions, uh, on premise? So, um, if you look at that wave, we're just at the beginning of it. But when, but when we talked to a lot of these companies and you ask them for their three year vision, Kubernetes is a huge piece of that because everyone wants to be multi-cloud everyone to be hybrid eventually. And that's going to be the enabler of that. So, uh, we're just in the beginning now, but it seems like an inevitable wave that is coming. >>So obviously people evaluated that exactly the way you're evaluating that. Right. Thus the funding, right. Because no one makes that kind of investment without thinking that there is a multiplier on that over time. So that's pretty, that's a pretty exciting place. >>Yeah. I think to your point, a lot of companies are running into that situation right now, and they're looking at existing solutions there for us. It was necessity because there wasn't anything out there now that there is a lot of companies are not using their sort of precious engineering resources to build their own there. They would prefer to buy a solution because this is something that we can offer to all the companies. And it's not necessarily a business differentiating technology for the businesses themselves >>Distributed tracing in that really platform. That's the news. Um, and you mentioned you've got this, a good bid. You do some good business. Is scale the big differentiator for you guys? Or is it the functionality? Because it sounds like with clients like door dash, and it looks a lot like Uber, they're doing a lot of stuff too, and I'm sure everyone needs the card. Other people doing the same kind of thing, that scale, massive amount of consumer data coming in on the edge. Yeah. Is that the differentiation or do you work for the old one, you know, main street enterprise, right. >>Um, that is a good part of the differentiation and for our product thus far before we had a distributor tracing for monitoring and metric data, that was the main differentiation is the sheer volume of data that gets produced so much higher, really excited about distributor tracing because that's actually not just a scale problem. It's, it's a space that everybody can see the potential distributor tracing yet. No one has really realized that potential. So our offering right now is fairly unique. It does things that no other vendors out there can do. And we're really excited about that because we think that that fundamentally solves the problem differently, not just at a larger scale, >>Because you're an expert, what is distributed tracing. >>Yeah. Uh, it's, it's, it's a great question. So really, if you look at this retracing, it captures the details of a particular request. So a particular customer interaction with your business and it captures how that request flows through your complex architecture, right? So you have every detail of that at every step of the way. And you can imagine this data is extremely rich and extremely useful to figure out what the underlying root causes of issues are. The problem with that is it's very bit boast. It's a lot of data gets produced. A ton of data gets produced, every interaction, every request. So one of the main issues are in this space is that people can't afford cost effectively to store all of this data. Right? So one of the main differentiators for our product is we made it cost efficient enough to store everything. And when you have all the data, you have far better analytics and you have >>Machine learning is better. Everything's better with data. That's right. Yeah. Great. What's the blind spot out. Different customers, as cybersecurity is always looking for corners and threats that some people say it's not what you want. It's what you don't see that kills you. That's, that's a tracing issue. That's a data problem. How do you see that evolving in your customer base clients, trying to get a handle of the visibility into the data? >>Yeah. Um, I think right now, again, it's, it's very early in this space of people are just getting started here and you're completely correct where, you know, you need that visibility. And again, this is why it's such a differentiator to have all the data. If you can imagine with only 10% of the data or 1% of data, how can you actually detect any of these particular issues? Right. So, uh, uh, data is key to solving that >>Feel great to have you guys on expert and congratulations on the funding, Jerry. Good to see you take a minute to give a plug for the company. What do you guys do? And actually close around the funding, told you a million dollars. Congratulations. What are you looking for for hiring? What are your milestones? What's on your plan plan. >>Yeah. Uh, so with the spanning, it's really to, to, uh, continue to grow the company, right? So we're sort of hiring, as I told you earlier, we are, uh, we grew our revenue this year by, by 10 X in the sense of the 10 months of this year, thus far. So our team hasn't really grown 10 X. So, so we, we need to keep up with that grid. So hiring across the board on engineering side, on the go to market side, and I just continue to >>Beat that. The headquarters, your virtual, if you don't mind, we've gone >>Completely distributed. Now we're mostly in the U S have a bunch of folks in Seattle and in New York, however, we going completely remote. So hiring anyone in the U S anywhere in Europe, uh, >>Oh, I got you here. What's your investment thesis. Now you got castles in the cloud, by the way, if you haven't seen the research from Greylock, Jerry and the team called castles in the cloud, you can Google it. What's your thesis now? What are you investing in? >>Yeah, it is. It is hard to always predict the next wave. I mean, my job is to find the right founders, but I'd say the three core areas are still the same one is this cloud disruption to Martin's point we're. So early days, the wave, I say, number two, uh, there's vertical apps, different SAS applications be finance, healthcare construction, all are changing. I think healthcare, especially the past couple of years through COVID, we've seen that's a market that needs to be digitized. And finally, FinTech, we talked about this before everything becomes a payments company, right? And that's why Stripe is such a huge juggernaut. You know, I don't think the world's all Stripe, but be it insurance payments, um, you know, stuff in crypto, whatever. I think fintechs still has a lot of, a lot of market to grow. >>It's making things easier. It's a good formula right now. If you can reduce complexity, it makes things easy in every market. You're going to seems to be the formula. >>And like the next great thing is making today's crappy thing better. Right? So the next, the next brace shows making this cube crappy thing. Yeah, >>We're getting better every day on our 11th season or year, I'm calling things seasons now, episodes and season for streaming, >>All the seasons drop a Netflix binge, watch them all the >>Cube plus and NFTs for our early videos. There'll be worth something because they're not that good, Jerry. How, of course you're great. Thank you. Thanks guys. Thanks for coming on it. Cubes coverage here in a physical event, 2021 cloud being the con CubeCon I'm John farrier and Dave Nicholson. Thanks for watching.