 The CUBE presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the CloudNative Computing Foundation and its ecosystem partners. Welcome to, but let's say Spain, and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico, senior at, Enrico's really proud of me. I've called him Enrico, instead of Enrique, every session. Senior IT analyst, GigaOwn. We're talking to fantastic builders at KubeCon, CloudNativeCon, about the projects and the efforts. Enrico, up to this point, it's been all about provisioning insecurity. What conversation have we been missing? Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. You know, everybody is 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 in their classroom. Why it is happening and all the questions that come with it. And the more I talk with people in the show floor here, or even in the various sessions, it's about, you know, we are growing. So our clusters are becoming bigger and bigger. Applications are becoming, you know, bigger as well. So we need to understand better what is happening. This is not only, you know, about cost. It's also about everything at the end. So I think that's a great setup for our guest Max Provo, founder and CEO of StormForge and Patrick Brinkstrom. Berkstrom. Berkstrom. I spelled it right, I didn't say it right, Berkstrom. CTO. We're at QCon, CloudNativeCon, where projects are discussed, built, and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. Why do I need more? Like what problem is StormForge solving? You want to take it? Sure, absolutely. So it's interesting because my background is in the enterprise, right? I was an executive at UnitedHealth Group. Before that I worked at Best Buy. 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? But then we run into the issue, like you and I were just talking about where it gets very, very expensive very quickly. And so my first conversations with Matt and the StormForge group and they were telling me about the product 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 EC2 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 out of 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, the low-hanging fruit is optimize the deployment. But actually the long term is optimizing the application, which is the real problem. So we actually, we're fine with 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. We are, as long as you can track or understand the metrics you care about for your application, we can optimize against it. 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 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 rap from that standpoint. And so we've, at this point, the company's been around since 2016, kind of from the very early days of Kubernetes. We've always been squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today, and over time, setting a cluster up on Kubernetes has largely been solved. And yet, the promise of Kubernetes around portability and flexibility downstream when you operationalize, the complexity smacks you in the face. And that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution that's absolutely focused on solving that problem. Well, I don't want to play, actually, I want to play the devil's advocate here. And, you know... You wouldn't be a good analyst if you didn't. So the problem is, when you talk with clients, users, there are many of them still working with Java, with something that is really tough. I mean, we loved, all of us love Java. Yeah, maybe 20 years ago, yeah, but not anymore. But still, they have developers, they are porting applications, microservices, yes, but not very optimized, et cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid, or anyway, not well engineered applications? Yeah, we do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage. And 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. And one of the things that really struck me very early on about the StormForge 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 performance tuning, we were only as good as our humans were because of what they knew. And so 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 have dreamed of. And you get amazing results out of that. So both me and Enrico have been doing this for a long time. Like I have battled to my last breath, the argument when it's a bare metal or a VM, look, I cannot give you any more memory. And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith, you're cheap, my developer resource is expensive, my are bigger box. 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? 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 on an ongoing way, especially as the development of applications becomes more and more rapid in the management of them, our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between cost or performance. You should not have to choose where your applications live in a public, private, 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 longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, I'm really excited to get out of bed and come to work every day and manually tune my application. One side. Secondly, we've never met a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups at their happy place is the lack of needing to be able to make those trade-offs. And that's been exciting for us. So I'm listening and it looks like your solution is right in the middle in application performance, management, observability, and monitoring. So it's a little bit of all of this. So we want to be the intel inside of all of that. We often get lumped into one of those categories. These used to be APM a lot. We sometimes get, are you observability? And we're really not any of those things in and of themselves, but we instead have invested in deep integrations and partnerships with a lot of that tooling because in a lot of ways the tool chain is hardening in a cloud native and in Kubernetes world. And so integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely in a APM or observability. So to go a little bit deeper, what does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the world that you do? Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into actionability. Like how do you take, it's one thing to collect all of the data, but then how do you know what to do with it? So to Matt's point, we integrate with folks like Datadog. We integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. But also we want Datadog customers, for example. We have a very close partnership with Datadog so that in your existing Datadog dashboard now you have the StormForge capability showing up in the same location. So you don't have to switch out. So I was just going to ask, is it a push pool? What is the developer experience? When you say you provide developer this, resolve ML learnings about performance, how do they receive it? Like 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. And we have our own UI. And so it is a push in a lot of cases where I can come to one spot. 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 observability data from a production standpoint. I can visualize all of that within the StormForge UI and platform, make decisions. We allow you to set your kind of comfort level of automation that 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 these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now in Grafana and Datadog and a bunch of others that are coming. So I 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. 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, developers. Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right? It's not always just about cost savings. In fact, the way that I look 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 because 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 you were talking about private cloud, for instance, and so having a physical data center. 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 namespaces into your cluster, like that's a huge opportunity. So I have another question. I mean, maybe 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? When you start deploying this, maybe as a service, once in a year, I say, okay, let's do it again and see if something changes. So, one spot, one single, you know. Yeah, would you recommend somebody performance test just once a year? Like so that's my thing is at previous roles, my role was to 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 want to live in as well as part of your CICD process. Like this should be continuous verification. Every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the namespace 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 CICD process that's connected to optimization. And that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but from cost and performance. Almost a couple of hundred vendors on this floor. You know, you mentioned some of the big ones, Datadog, et cetera. But what happens when one of the up-and-coming's out of nowhere, completely new data structure, some imaginative way to click, to elementary data, how do you react to that? To us, it's zeros and ones. Yeah. And, you know, we're really, we really are data agnostic from the standpoint of, we're not, we're fortunate enough to, from the design of our algorithm standpoint, it doesn't get caught up on data structure issues. You know, as long as you can capture it and make it available through, you know, one of a series of inputs, one would be load or performance test, could be telemetry, could be observability. If we have access to it, honestly, the messier the better from time to time, from a machine learning standpoint. It's pretty powerful to see. We've never had a deployment 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 hybrid? I mean, yes, Kubernetes is the best thing of the world, but sometimes we have to, you know, external data sources or, you know, we have to connect to external services anyway. So can you, you know, can you provide an indication also on this particular application, like, you know, where the problem could be? Yeah, and that's absolutely one of the things that we look at too, because it's, 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, it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, you know, partnering with, 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, or, you know, what your disk looks like, right? Like, because with our lower environment testing, any metric you throw at us, we can optimize for. So Madden, Patrick, thank you for stopping by. We can go all day because day two is, I think the biggest challenge right now, not just in Kubernetes, but application, replatforming and transformation, very, very difficult. Most CTOs and EAs that I've talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host, Enrico Signoretti, and you're watching the Q, the leader in high-tech coverage.