>>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.