 cloud-native community and welcome back to Chicago. We're here at KubeCon, cloud-native con, CNCF's largest North American event. My name is Savannah Peterson, joined here by my fabulous co-host Rob, how you doing? I'm doing awesome. Are you, do you, have you, have you known, have people commented on how well you matched the branding for the event today? I, you know what, you were the first. Well, I mean, that doesn't surprise me, but yeah. I'm excited for our segment this afternoon. We have a Kube alumni. He's been on the show four different times and coincidentally also has had four funding rounds to compliment that. Most recently raising $115 million series C up-round in January, which in this ecosystem landscape and economy is a serious accomplishment. Martin is so great to have you back on the show. Thank you so much, and looking forward to our conversation soon. Hopefully we can keep that run going, four for four right now. I know, I love that. Hopefully, hopefully we didn't jinx it, five-folded rings. We'll see you in Paris. Maybe that'll be the case. Hopefully, and we can talk about it then. So, yeah, there's a lot going on with Chronosphere. Let's start with the big product announcement that you had today. What's going on? Yeah, so we just announced Chronosphere Lens. And what Chronosphere Lens is, it's a new and more effective way for developers to interact with observability data, right? So, you've heard of the phrase single pane of glass, and that's been the way observability vendors have approached a problem for a while now. But the problem with the single pane of glass is there's so much data these days, just seeing all of it in one place is actually not that useful. It's almost too much data, and the developers are drowning in it. So what Chronosphere Lens does is it actually analyzes all of the raw observability data underneath the covers, and then extracts insights and knowledge from that raw data, and presents that information to the developer in a context that they want to solve their problems. So we found it to be far more effective. And in fact, for Chronosphere customers, they're finding that they're reducing their SEDB zero several incidents by about 75% or so. So it's a lot more effective approach to solving the problem than the standard. Yeah. That's a considerable impact. Thank you, yeah. And I imagine it would have taken some calibration to, upon intended, figure out how to hone that lens to serve the developers the information they need in that moment. Exactly, exactly, right. So yeah, now you know why we called it Chronosphere Lens, right? Because it's all about focusing into the data and the insights that's really relevant for an individual developer, right? Because remember, these systems we've built are really complex these days. And as a developer, I only own one part of the system. I don't actually want to see what's going wrong because there's a lot of things going wrong with the whole system, right? I don't actually want to see all of it. I just want to hone in on what I care about and my dependencies. And that's what Lens does, it helps you hone in on that piece there. It helps them focus. And I think you also announced that there was new change event tracking as well as part of that. And I think it's to your point, bringing it all together and then being able to coordinate that information. 100%, so great point there. So with change event tracking, we're tracking every change that happens to a system, a deployment, a new piece of infrastructure. Those changes there, to your point, for the whole system, there are a lot of changes, right? So what I really want to see is not just all the changes. What are the changes relevant to me as a developer? What are the changes that I need to know about that again in context helps me solve my problem? So we did add event tracking, but the trick with Lens is that we scope down and focus on just the events that are relevant for that particular incident there. Yeah, because a lot of times it's garbage in, garbage out, and you're drowning in data and things of that nature. So this really helps them focus in on what they're trying to achieve for their piece of an application, be it a microservice or a container or hey, if they're more of the platform engineering type people. 100%, it's what they own, to your point, it's a microservice or a piece of infrastructure there, but it's also the dependencies, right? Because it's not just what I own, it's what I'm dependent on, it's what's dependent on me. So it's more than just their tiny piece of the world, but it's not the whole thing, all right? But yeah, that's the focus there. And then to help with the drowning in data, you can imagine the drowning in data is causing a whole bunch of cost problems for the industry right now. That's where I was going to drive this next. We're trying to solve, well we're not trying, we are solving that problem at the same time as well there, right? Tell us a little bit more about that. Yeah, so what we find is that when a company adopts cloud native, what they see is on average a 12.4x increase in the volume of data that's being produced, right? That's an order of magnitude more data. And the reason why people spend so money on observability tooling, it's not because the vendors have been greedy, it's actually because there's so much more data, these systems cost a lot more because of that, right? And it's one of these unintended side effects. When you adopt cloud native, you know, you can imagine you're not really paying for the cloud providers running Kubernetes for you, you're really paying for the VMs and the hardware underneath the covers there. So the cost of the infrastructure is the same and yet you run a different architecture. The workload you can put through it is roughly the same, but your observability bill grows potentially 12.4x. So that's a huge problem that the industry is facing right now. And what we're trying to do with that is actually help companies control the growth of data there. And one of the tricks we learned is we can't just make it cheaper, unfortunately, because there's diminishing returns on how efficient you can get the back ends out there and the data is exploding in an exponential format. So it's not just about making it cheaper. It's actually the trick is to understand and show the companies and the customers what is costing you all of your expensive bill, what is causing all of that cost and out of the data what is valuable and what isn't valuable. And we're really trying to get people to match, okay, I want to spend money only on my valuable use cases. So how do I go and match those two things together and make sure I only spend money on my valuable use cases? And we're able to do that through a feature called the control plane in Chronosphere. And on average, our customers are optimizing their data about 60% and you can imagine therefore saving at least 60% on their observability bills there. And I think you've always been known for no overage contracts and things of that nature. How has that really been helping you in the past year since we talked last? Yeah, it's been helping. You can imagine the economy hasn't been great in the past year, right? So a lot of companies have been pressured. They don't have extra budget. So the concept of an overage is really painful for a lot of companies because you didn't budget for it internally originally. And now you have to go find discretionary budget in order to cover the overage. We hate that model and we don't believe in that model at all. In terms of our product, there is no concept of an overage. So what we get companies to do is we use this control plane in order to control that growth of data and avoid overages there. And the behavior we actually saw in our, with the companies that we worked with this year, is that they actually went back and used the tooling to go and optimize the data further as their budgets weren't expanded. So that's the behavior we saw this year and we're really happy that we're able to help customers control both the data volume growth as well as their cost there. And it makes sense that it's built in when you're using your own tool to help them navigate what that even looks like. 100%, 100%. It makes perfect sense, very synergistic. When we chatted last year, you mentioned that observability was just beginning. Where are we at a year later here in Chicago? I would say it's definitely advanced a lot since last year, right? And I think the overall cloud native journey has advanced a lot. Like you look at this show, this reminds me of maybe San Diego 2019. Like it feels like the buzz rank cloud native is back. And we see it in the industry, especially around the enterprises, right? Like the enterprises are really, they've been thinking about this for years. And in the last year, we've really seen that the enterprise actually starts to take action and actually shift workloads over to cloud native architecture. So that trend has continued. And along with that, you really need observability and cloud native observability are there. So that trend just continues on. So what are we going to say a year from now? I think that trend will only continue for from a year from now. I do think a lot more companies are going to run into a lot of these cost challenges. Because again, as you make this transition, the data loads are going to grow and there'll be even more pressure and costs. I don't think we turn back to a 2021 economy. I think there's a new, as a new level of efficiency expectation around. So I think that trend will continue. And I think there's going to be a need for tools that are much better at focusing on the problem and being more effective at solving the problem because that's just a really bad trend in the industry as well. I mean, it's one of the things that we hear from organizations is, hey, I don't have an observability problem. I have 10 tools in this space. And I think what a lot of them are looking for is really a platform. And it seems like that's really the direction you're going into approach your take. It's both the platform for sure because you need to consolidate those tools down. But even when you do that to perhaps one platform, you don't just want one platform in a single pane of glass. It actually goes a little further than that as well. And hence the chronosphere lens there, right? So we're trying to be two steps ahead of where people ultimately need to go. But yeah. If you're two steps ahead, where are we going with generative AI? That's a great question. I think a lot of buzz in the space. And what we found when we played around with it, just like most other companies there are, the public models are interesting for sure. But the problem with the public models is they're never built on your company's data, right? So if you think about observability, what you want to know is, what are the issues with my system? And the public models are not trained on your company data. So therefore they're not quite as effective. So we started down this path and it's actually matches with what we're doing with the chronosphere lens, which is analyze the raw data and build, not quite a vector database, but build a knowledge graph on top of the raw data and have that, which is specific to a particular company, go fuel the insights that you want to go present. And then when we built that piece, we thought about it and we're like, actually, you know what, the interface, the chat interface is interesting for some use cases. For the observability use case, it's actually not the best because you don't actually want to ask it proactively like, what's wrong with my system? It's much better for the tool just to tell you, here's what's wrong with my system. And you want it constantly giving you that information as necessary. Exactly, exactly. And perhaps text interface, not the best. You know, you really want it as a visual thing of like, this is what's wrong and let me show you what's wrong there. So what we found is actually lens is a much more effective way of presenting that information. It's a similar dynamic to what a lot of these AI models are, but we just don't think chat is quite as effective as the main interface to observability. But we did do a lot of playing around there. And I do think, you know, there is a general trend in that direction. And you don't have to do all the prompt engineering as we have our own LLM and I can tell you that I've gotten very good at writing prompts. Exactly. You have to be really good at it. And you have to know. It's an art. And you have to know what questions to ask, right? Whereas like, if we can just tell you what the answers are, you don't need to figure out what questions to ask, right? It's more effective that way. Well, and you want to be anticipating what those questions are going to meet. And eventually meet them. I mean, isn't that actual AI? The whole point of observability is, we want to share what's wrong with the system rather than have you ask, well, is it this? Nope, is it this? Nope, is it this? No, right? And troubleshooting when you're in a moment of panic too and something's not working. The last thing you want to do is be trial and erroring. Exactly, exactly, exactly. So hence, you can imagine an interface where it's like, look, we know who you are. We know why you're here because you're not just randomly perusing this tool. Like, you're here for a reason. We just paged you and here are some of the areas that are wrong, right? Like, proactively presenting that information in a very curated way is what we found to be a more effective approach. And it would seem like there's definitely consolidation in the industry, I mean, going on. And it would seem that that makes a lot of sense because we looked out here a couple of years ago, it was almost all observability companies. Now that's, you know, shrank down a bit, there's still quite a good core. How do you see that part in the openness that's going on here, helping you drive that in your company? Yeah, I think the openness and like industry centers like Open Telemetry, things like Fluent and Fluent Bit and Fluent D, I think that really actually opened up the industry quite a lot and actually made room for new companies like Chronospeed to enter because you are now no longer locked in to these proprietary agents that are producing the data for you. So it's actually better for the world overall because, you know, as a company, I now am not locked into one particular vendor. I can actually instrument and own the data creation myself and I can pick whichever tool I want. It also opened the door for new players like Chronospeed to enter and really reduce the moat of the incumbent players that were here. And then of course earlier this year, a lot of the big companies out there were taking privates. So that does, you know, pave the way for perhaps new companies to go take their spot. So that's something we're very excited about, but yeah. That's great, no, totally makes sense as well. Yeah, yeah, so let's talk, I suspect there's probably a little bit of privacy around this. What are some of the partners and players that you're getting to support on their journey? Yeah, for sure. So, you know, on the partnership side, what we look at is from a problem perspective, what are problems that our customers can solve using our tools? And then what are other problems that they can't solve? Because we can't solve everything under the sun, right? So what are problems? Sure, we're working on why. The fifth appearance maybe? Sixth appearance here on the show, I love it Martin. We'll check back in next year. But there are certain things that we can't do well, right? So we really want to find complimentary partners there. And we found great partnerships with particular companies like CrowdStrike out there, where we have a good partnership with them on some of their use cases there, right? So we do try to partner and again, as a smaller company, it's more effective for us to partner with a best and breed solutions out there, where we focus. I do think the good thing about our partnership is we're also looking for companies that are really aimed at the future at cloud-native environments, as opposed to non-cloud-native environments, right? So there's a lot of good partnerships. A lot of them are on the floor here at the show. A lot of good partnerships for us from that perspective. Yeah. And I bet everybody wants to work with you with that $115 million purse. It helps, it helps. What is the capital going to unlock for you in terms of your ability to scale? Big investment in the product. So to your point, we don't do everything yet, but we will soon. All right, set the timer right now, we're in. Exactly, exactly. So a lot more investment in the product. And I think a lot more investment in product because companies are looking for platforms. They're not looking for 10 different solutions. They're looking for fewer and fewer. They're looking for consolidation. So we do want to expand our product suite there. We do want to make sure we stay ahead. So the general conversation was very interesting. We want to make sure we invest enough in the right ways there to stay ahead and not get caught out that way. So a lot of investment there. A lot of investment also on the go-to-market side as well. You can imagine the popularity of the product and the company is growing as well and that has to fuel that growth there. So more capital for growth, I would say. And I think of what I've liked about it and have been briefed by you guys before is that you really focus on confidence, control and context as kind of the three pillars for your roadmap. How does that help you with focus as well? Because you could say, hey, we're going to make bets all over the place with the money, but it would seem like that wouldn't give confidence, control or context. Exactly, and you put it in better words than I can. Those are exactly the three things that I all begin with C, right? So to your point, it's about focus on those three areas, because when we looked at the problem, there wasn't a need for necessarily just another tool. There were enough tools out there. We really looked at what is everyone struggling with and what everyone's struggling with is cost and controlling the data growth. So that's one area we want to focus on, especially right now. So that's one area we've been focusing on for years already. Second one is context, because the effectiveness of these tools just weren't there anymore for the new environment. So that was the second one. And third one is the confidence and the reliability. Observability, this is the thing that's telling you how reliable is your product and service. So if this thing isn't reliable, there's no way you can be reliable as a company, right? So that was a cornerstone of the company. Those are honestly the three main strategies there. So everything we invest in has to form one of those three categories there. And that's what allows us to stay focused. So while we look at other things, and have to be aligned like the Chronosphere lens piece, it's aligned on the context, right? We don't do it if it's not context, if it's not about control, if it's not about confidence, we don't really make those investments. But yeah. Well say it, we got the three Cs now. We're going to have to bring that out in a lot of our- From Chronosphere, right? Yeah, yeah, yeah, yeah. From Chronosphere no less, oh my God, it's cool. Having been a product guy, I appreciate focus and pillars and gives you that north star to aim for. And I think- And alliteration. And alliteration. And alliteration. And alliteration. Or another TLR. Yeah, yeah, yeah. TLA. Don't arc. It's all right, we don't have to kill everyone. We don't have to kill everyone with acronyms. What does it mean for you? I mean, I can feel the energy. It's loud in here right now, it's exciting. What does it mean for you and the team to be here at KubeCon? Look, this is our favorite event all year long, right? We love it. We love it. And again, as I mentioned earlier, this year feels reminiscent of KubeCon San Diego. We're like pre-pandemic. It felt like, and it's not like the trend to cloud native is slowed down at all. But I think the in-person energy from shows like this hasn't quite returned yet. Last year, we're getting close. This year, I really feel it. So I think it's great, I would say, reinforcement of how far we've climbed in the cloud a native space and the fact that this is still the future that everyone in the whole industry is moving towards. So we love it. Our favorite show of the year. And this is probably our favorite show or segment of the favorite show of the year as well. Now you're just flattering us, but we'll take it. We'll take it. So that means we'll see you in Paris then. I'll be there in Paris, so we should definitely ask. Yeah, we'll have to see what the fifth chat means. I look forward to that. That's only a few months from now. Two, five months away, fifth chat. I'm sure we'll have some big news to break by then. Well, that is awesome. Martin, thank you so much for being here with us on the show. We're going to be showing off some of the things that are at your booth a little bit later today on a separate segment. But truly looking forward to number five here. And congratulations again on the funding and the absolutely explosive growth as a company that's inspiring to see. I think a lot of people need to see it, especially right now. Rob, thank you so much for being here, for hanging out, for matching the entire event, which is an accomplishment in itself. And thank you most importantly viewer for tuning in. My name is Savannah Peterson here in the Paris of the Prairie, Chicago, Illinois at KubeCon, CNCF, CloudNativeCon, coming to you live all afternoon. Don't forget to join us on theCUBE, the leading source for emerging tech news.