 The adoption of container orchestration platforms is accelerating at a rate as fast or faster than any category in enterprise IT. Survey data from enterprise technology research shows Kubernetes specifically leads the pack in both spending velocity and market share. Now like virtualization in its early days, containers bring many new performance and tuning challenges, in particular ensuring consistent and predictable application performance is tricky, especially because containers are so flexible and the enable portability, things are constantly changing. DevOps pros have to wade through a sea of observability data and tuning the environment becomes a continuous exercise of trial and error. This endless cycle taxes resources and kills operational efficiency, so teams often just capitulate and simply dial up and throw unnecessary resources at the problem. StormForge is a company founded mid-last decade that is attacking these issues with a combination of machine learning and data analysis. And with me to talk about a new offering that directly addresses these concerns is Matt Provo, founder and CEO of StormForge Matt. Welcome to theCUBE. Good to see you. Good to see you. Thanks for having me. Yeah, so we saw you guys at a KubeCon, sort of first introduce you to our community, but add a little color to my intro there. Yeah. Well, you semi stole my thunder, but I'm okay with that. I absolutely agree with everything you said in the intro. You know, the problem that we have set out to solve, which is tailor made for the use of real machine learning, not machine learning kind of as a marketing tag is connected to how workloads on Kubernetes are really managed from a resource efficiency standpoint. And so the number of years ago we built the core machine learning engine and have now turned that into a platform around how Kubernetes resources are managed at scale. And so organizations today as they're moving more workloads over sort of drink the Kool-Aid of the flexibility that comes with Kubernetes and how many knobs you can turn and developers in many ways love it. Once they start to operationalize the use of Kubernetes and move workloads from pre-production into production, they run into a pretty significant complexity wall. And this is where StormForge comes in to try to help them manage those resources more effectively in ensuring and implementing the right kind of automation that empowers developers into the process ultimately does not automate them out of it. So you've got news, hard launch coming and to further address these problems. Tell us about that. Yeah. So historically, you know, like any machine learning engine, we think about data inputs and what kind of data is going to feed our system to be able to draw the appropriate insights out for the user. And so historically we've kind of been single threaded on load and performance tests in a pre-production environment. And there's been a lot of adoption of that, a lot of excitement around it and frankly amazing results. My vision has been for us to be able to close the loop however between data coming out of pre-production and the associated optimizations and data coming out of production, a production environment and our ability to optimize that. A lot of our users along the way have said these results in pre-production are fantastic. How do I know they reflect reality of what my application is going to experience in a production environment? And so we're super excited to announce kind of the second core module for our platform called Optimize Live. The data input for that is observability and telemetry data coming out of APM platforms and other data sources. So this is like Nirvana. So I wonder if we could talk a little bit more about the challenges that this addresses. I mean, I've been around a while and it really have observed and I used to ask, you know, technology companies all the time, okay, so you're telling me beforehand what the optimal configuration should be and resource allocation, what happens if something changes and then it's always a pause. And Kubernetes is more of a rapidly changing environment than anything we've ever seen. So this is specifically the problem you're addressing. Maybe talk about that later. Yeah, so we view what happens in pre-production as sort of the experimentation phase and our machine learning is allowing the user to experiment and design and scenario plan. What we're doing with Optimize Live and adding the production piece is what we kind of also call kind of our observation phase. And so you need to be able to run the appropriate checks and balances between those two environments to ensure that what you're actually deploying and monitoring from an application performance from a cost standpoint is aligning with your SLOs and your SLAs as well as your business objectives. And so that's the entire point of this addition is to allow our users to experience, hopefully, the nirvana associated with that because it's an exciting opportunity for them and really something that nobody else is doing from the standpoint of closing that loop. So you set up front machine learning not as a marketing tag. So I want you to sort of double click on that. What's different than how other companies approach this problem? Yeah, I mean, part of it is a bias for me and a frustration as a founder of the reason I started the company in the first place. I think machine learning or AI gets tagged to a lot of stuff. It's very buzzwordy. It looks good. I'm fortunate to have found a number of folks from the outset of the company with PhDs and applied mathematics and a focus on actually building real AI at the core that is connected to solving the right kind of actual business problems. And so for the first three or four years of the company's history, we really operated as a lab and that was our focus. We then decided we're trying to connect a fantastic team with differentiated technology to the right market timing. And when we saw all these pain points around how fast the adoption of containers and Kubernetes have taken place, but the pain that the developers are running into, we found it, we actually found for ourselves that this was the perfect use case. So how specifically does optimized live work? Can you add a little detail on that? Yeah. So many organizations today have an existing monitoring APM observability suite really in place. They've also got a metric source. So this could be something like Datadog or Prometheus. And once that data starts flowing, there's an out-of-the-box or kind of piece of Kubernetes that ships with it called the VPA or the vertical pod autoscaler. And really less than 1% of Kubernetes users take advantage of the VPA, mostly because it's really challenging to configure. And it's not super compatible with the tool set or the ecosystem of tools in a Kubernetes environment. And so our biggest competitor is the VPA. And what's happening in this environment or in this world for developers is they're having to make decisions on a number of different metrics or resource elements, typically things like memory and CPU. And they have to decide what are the requests I'm going to allow for this application and what are the limits? So what are those thresholds that I'm going to be okay with so that I can again try to hit my business objectives and keep in line with my SLAs. And to your earlier point in the intro, it's often guesswork. They either have to rely on out-of-the-box recommendations that ship with the databases and other services that they are using or it's a super manual process to go through and try to configure and tune this. And so with Optimize Live, we're making that one click. And so we're continuously and consistently observing and watching the data that's flowing through these tools. And we're serving back recommendations for the user. They can choose to let those recommendations automatically patch and deploy or they can retain some semblance of control over the recommendations and manually deploy them into their environment themselves. And we again really believe that the user knows their application. They know the goals that they have. We don't. But we have a system that's smart enough to align with the business objectives and ultimately provide the relevant recommendations at that point. So the business objectives are an input from the application team. And then your system is smart enough to adapt and address those. Application over application. And so the thresholds in any given organization across their different ecosystem of apps or environment could be different. The business objectives could be different. And so we don't want to pre-define that for people. We want to give them the opportunity to build those thresholds in and then allow the machine learning to learn and to send recommendations within those bounds. And we're going to hear later from a customer who's hosting a Drupal, one of the largest Drupal hosts. So it's all do-it-yourself across thousands of customers. So it's very unpredictable. I want to make something clear though as to where you fit in the ecosystem. You're not an observability platform. You leverage observability platforms. So talk about that and where you fit into the ecosystem. Yeah. So that's a great point. We're also a Series B startup and growing where we've made the choice to be very intentionally focused on the problems that we've solved. And we've chosen to partner or integrate otherwise. And so we do get put into the APM category from time to time. We're really an intelligence platform. And that intelligence and insights that we're able to draw is because of the core machine learning we've built over the years. And we also don't want organizations or users to have to switch from tools and investments that they've already made. And so we were never going to catch up to Datadog or Dynatrace or Splunk or AppDynamics or some of the other. And we're totally fine with that. They've got great market share and penetration. They do solve real problems. Instead, we felt like users would want a seamless integration into the tools they're already using. And so we view ourselves as kind of the intel inside for that kind of a scenario. And it takes observability and APM data and insights that were somewhat reactive. They're visualized and somewhat reactive. And we add that proactive nature onto it, the insights, and ultimately the appropriate level of automation. So when I think about cloud native, and I go back to the origins of CNCF, handful of companies, and now you look at the participants that make your eyes bleed, how do you address dealing with all those companies? And what's the partnership strategy? Yeah, it's so interesting because even that CNCF landscape has exploded. It was not too long ago where it was as small or smaller than the FinOps landscape today, which by the way, the FinOps piece is also on a neck breaking growth curve. I do see, although there are a lot of companies and a lot of tools, we're starting to see a significant amount of consistency or hardening of the tool chain with our customers and users. And so we've made strategic and intentional decisions on deep partnerships in some cases like OEM uses of our technology and certainly intelligent and seamless integrations into a few. So we'll be announcing a really exciting partnership with AWS and specifically what they're doing with EKS, their Kubernetes distribution and services. We've got a deep partnership and integration with Datadog and then with Prometheus and specifically a few other cloud providers that are operating managed Prometheus environments. Okay, so where do you want to take this thing? You're not taking the observability guys head on, smart move, so many of those, even entering the market now, but what is the vision? Yeah, so we've had this debate a lot as well because it's super difficult to create a category. On one hand, I have a lot of respect for founders and companies that do that. On the other hand, from a market timing standpoint, we fit into AI ops. That's really where we fit. We've made a bet on the future of Kubernetes and what that's going to look like. From a containers and Kubernetes standpoint, that's our bet. But we're an AI ops platform. We'll continue getting better at the problems we solve with machine learning and we'll continue adding data inputs. So we'll go beyond the application layer, which is really where we play now. We'll add kind of whole cluster optimization capabilities across the full stack. And the way we'll get there is by continuing to add different data inputs that make sense across the different layers of the stack. It's exciting. We can stay vertically oriented on the problems that we're really good at solving, but we can become more applicable and compatible over time. So that's your next concentric circle. As the observability vendors expand their observation space, you can just play right into that. Yeah, more data you get because you're purpose built to solving these types of problems. Yeah, so you can imagine a world right now out of observability, we're taking things like telemetry data. Pretty quickly, you can imagine a world where we take traces and logs and other data inputs as that ecosystem continues to grow. It just feeds our own, we are reliant on data. Excellent. Matt, thank you so much. Thanks for having on. Okay, keep it right there. In a moment, we're going to hear from a customer with a highly diverse and constantly changing environment that I mentioned earlier. They went through a major replatforming with Kubernetes on AWS. You're watching theCUBE. You're a leader in enterprise tech coverage. Getting started with Kubernetes may be straightforward, but as you ramp up for day two operations, you're forced to make a choice. Either over-provision cloud resources by 50% or more driving costs through the roof, risk business impacting application performance and availability issues, or slow down time to market to spend time manually tuning your complex Kubernetes environment. It's a terrible choice to have to make, especially given how important cloud native transformation is to your company's success. What if you could ensure your cloud native environment was never over-provisioned while still meeting SLAs and SLOs and do it automatically so your dev team could focus on innovating? Stormforge automates the process of achieving and maintaining Kubernetes resource efficiency at scale. We provide a platform for continuous scenario planning using machine learning to provide actionable insights both in production and pre-production. In production, Stormforge leverages the wealth of data you're already collecting from observability solutions like Prometheus or Datadog. With minimal time and effort, we start providing recommendations for configuration changes that will boost efficiency. Recommendations can be implemented automatically or with your approval. You're always in full control. While production optimization provides fast and easy efficiency gains, pre-production optimization lets you go deep to fully understand system behavior and achieve peak efficiency. Stormforge performs experiments in your dev cluster using appropriate workloads to simulate a wide range of scenarios. Our machine learning analyzes the essentially infinite number of possible configurations to minimize cost, ensure performance and find insights and patterns to drive key architectural improvements. Stormforge closes the gaps organizations experience as they reach day two Kubernetes operations. The complexity gap, where effectively managing your Kubernetes environment becomes overwhelming. The data gap, where the amount of data you're collecting has grown exponentially, but you're left without actionable insights. And finally, the skills gap, where you need to do more with less and Kubernetes experts are in short supply. Getting started with Stormforge is as easy as one, two, three. Talk to one of our Kubernetes experts, see a demo and take it for a test drive. Start today to reduce cloud costs, improve application performance, and get your dev teams focused on innovating. Okay, we're back with Charlie Dublin. He's the Vice President of Product Management at Acquia. Great to see you, Charlie. Welcome to the queue. Thank you, Dave. So Acquia, tell us about the company. Sure. So Acquia is the largest and best provider of Drupal hosting capabilities. We rank number two in the digital experience platform space just behind Adobe. So very strong business growing well and innovating every day. Yeah, Drupal, open source, super deep high quality content management system and more experience. What do you call it, an experience platform? Experience platform, open, flexible. We want our customers to have choice, the ability to solve their problems, how they want leveraging the power of the open source community. So what were the big challenges? Just describe your kind of the business drivers. We're going to talk about Stormforge, but the things that you were facing, some of the challenges that led you to Stormforge. Sure. So our objective first is to provide the best experience with Drupal. So that entails lots of capabilities around ease of use for Drupal itself, but that has to run on the world, a world class platform. It has to be the most performance. It has to be the most secure. It needs to be flexible to enable customers to run Drupal however they want to run Drupal. And so that involves the ability to support thousands of different kinds of modules that come out of the community. We want our customers to have choice with Drupal and be able to support those choices on our platform. So optionality is key. You know, sometimes that creates other challenges like you've got one of everything. So how do you deal with that challenge? Yeah, that's a great question. Every strength is a form of weakness. And so our objective is really first to provide that choice, but to do it in a cost-efficient way. So we try to provide reference architectures for customers, opinionation for our customers to standardize, take out some of the complexity that they might have if everything were a snowflake. But our objective is really to support their needs and err on the side of that flexibility. All right, so you guys had to go through a major replatforming effort around containers and Kubernetes. Can you talk about that and what role Stormforged played? Sure. So tied to the last point, our objective is to provide customers the highest performance and most secure platform. The entire industry, of course, is moving to Kubernetes and leveraging containers. We are a large consumer of AWS services and are undergoing a major replatforming away from legacy AWS towards Kubernetes and containers. And so that major replatforming effort is intending to enable customers to run applications how they want to. And the power of Kubernetes and containers is to support that. And so we looked at Stormforged as a way for us to right-size resource capacity to support our customers' applications. I love it. AWS is now legacy. Andy Jassy, one time, said that if they had to redo Amazon, they'd do it in Lambda and using Serverless. So yeah, it's been around a long time now. Okay, so what were the outcomes that you were seeking? Was it better management, cost reduction, and how'd that go? Sure. So our customers run a wide range of applications. We support customers leveraging Drupal in every industry. Globally, we do business in 30 different countries. And so what you have is a very wide range of applications and consumer and consumption models. And so we felt that leveraging Stormforged would put us in a position where we'd be able to right-size resource to those different kinds of applications. Essentially, let the platform align to how customers wanted to operate their applications. And so Stormforged's capability in conjunction with Kubernetes and containers really puts us in a position where customers are able to get the performance that they want and when they need it on demand. A lot of the auto-scaling capabilities that you get from Kubernetes and containers supports that. And so it really enables customers to run their applications how they want to functionally, as well as from a performance perspective. So this move toward containers and microservices, sort of modern application development coincides with a modern platform like Stormforged. And so I'm sure there are alternatives out there. Why Stormforged? Maybe you could explain a little bit more about why, from your perspective, what it does and why you chose them. Sure. So we leverage AWS in many respects in terms of the underlying platform, but we are a very strong DIY for how that platform supports Drupal applications. We view our expertise as being the best at Drupal. And so we felt like for us to truly maximize Kubernetes and containers and the power of those underlying technologies on the one hand allows us to automate more and do more for customers. On the other side of it, it puts a tremendous burden on the level of expertise in order to do that well for every customer every day at scale. And so that at scale part of that was the challenge. And so we leverage Stormforged to enable us to write size applications for performance, provide us cost benefits, allocate what you need when you need it for customers. And that at scale piece is a critical part. We could do elements of it internally. We tried to do elements of that internally. But as you start getting to scale from a few apps to hundreds of apps to certainly across our fleet of tens of thousands of applications, you really need something that leverages machine learning. You really need a technology that's integrated well within AWS. And Stormforged provided that solution. So make sure I got this right. So it sounds like you sort of from a skill standpoint transitioned or applied your skills from turning knobs, if you will, to automation and scale. Correct. And what was that like? Was it, I mean, was the team leaning into that, you know, loving it? Was it a, was it a, you know, a challenging thing for you guys to get there? Yeah, it's a good question. So the benefit and the way that Stormforged applies it, so they leverage machine learning to enable us to make better decisions. So we still have the control elements, but we have much greater insight into what that would mean ahead of time before customers would be affected. So we still have the knobs we need, but we're able to do it at scale. And then from the automation point, it allows us to focus our deep expertise on making Drupal and the core hosting platform capabilities awesome, sort of the stuff and resource allocation, resource consumption, that's an enabler, we can outsource that to Stormforged. So this is, this is not batch, it's, you're basically doing this in sort of near real time, optimize live, right? Is the, is the, is the capability, maybe you can describe what it is. Sure. Yeah. So optimize live is, is new. We're in testing with that. We've, we've done extensive testing with Stormforged on the core call it decision making logic that allows for the right sizing of consumption and resources for customer applications. So that has already been tested. So the core engines have been tested. Optimize live allows us to do that in real time to make policy decisions across our fleet on what's the right trade off between performance, cost, other parameters. Again, it informs our decision making and our management of our platform that would be very, very difficult. Otherwise, without Stormforged, we'd have to do massive data aggregation. We'd have to have machine learning and additional infrastructure to manage to derive this information and, and, and that is not our core business. We don't want to be doing that. We want insights to manage our platform to enable customers and Stormforged provides that. So, okay. So it's kind of human in the loop thing. Hey, here's what, like our recommendation or here's some options that you might want to, is a path that you want to go down, but it's not taking that action for you necessarily, right? You don't want that. You want to make sure that the, the experts are, have a hand in it still, is that correct? Correct. You still want the experts to have a hand in it, but you don't want them to have a hand in it on each individual app. You need that, that machine learning capability, that insight that allows you to do that at scale. So if you had a step back and think about your relationship with Stormforged, what was the business impact of, you know, bringing them in? Yeah, first, from a time to market perspective, we're able to get to market with a higher performance, more cost effective solution earlier. So there's that benefit. Second benefit to the earlier point is that we're able to make resource allocation decisions focused on where our core competency is, not into the guts of Kubernetes containers and the like. Third is that the machine learning talent that Stormforged brings to the table is world-class. I've run machine learning teams, data science teams, and we put them in the top 1% of any team that I've worked with in terms of their expertise. So the logic and decision making and insights is outstanding. So we can get to the best decision, the optimal decision much more quickly. And then when you accompany that with the newer product and Optimize Live with that automation component you mentioned, you know, all the better. So we're able to make decisions quicker, get it implemented in our platform and realize the benefits. What customers get from that is much better performance of their applications, more real time, higher, able to scale more dynamically. What we get is resource efficiency and our network and platform efficiency. We're not over allocating capacity that costs us more money than we should. We're under allocating capacity that could have a lower performance solution for our customers. So that puts money in your pocket and your customers are happier. So they're higher renewal rates, less churn. Correct. Higher prices over time as you add more capabilities. That's correct. What's it like? Well, you know, new application approach, Kubernetes, containers, fine, okay, I need a modern platform, but it's a relatively new company, Stormforge, right? What's it like working with them? Sure. Their talent level is world-class. And, you know, I wasn't familiar with them when I joined Aquia. I came to know them and then very impressed. There's many other providers in the market that will speak to some similar capabilities and would make many claims. But from our assessment, our view is that they're the right partner for us or the right size. They're flexible, excellent team. They've evolved their technology roadmap very quickly. They deliver on their promises and commits. They're a very good team to work with. So I've been very impressed for such an early stage company to deliver and to support our business so rapidly. So I think that's a strength. And then I think, again, the quality of the people that's been manifested in the product itself. It's a high-quality product. I think it's unique to the market. So Napoleon Hill, famous writer, thinker, he wrote Think and Grow Rich. If you haven't read it, check it out. But one of his concepts is a lever, small lever can move a big rock, you know, it can be very powerful. Do you see Stormforge as having that kind of effect on your business, that change on your business? I do. Like I said, I think the engagement with them has proven, and this isn't debatable based on the results we've had with them. We ran that team through the ringer to validate the technology. Again, we've heard lots of promises from other companies. Ran that team through the ringer with extensive testing across many customers, large and small, many use cases to really stress test their capabilities. And they came out well ahead of any metric we put forth, even well ahead of claims that they had coming into the engagement. They exceeded that. And so that's why I'm here, why I'm an advocate, why I think they're an outstanding company with a tremendous amount of potential. What can you tell us about where you want to take the company and the partnership with Stormforge? Sure. I think the main next step is for us to engage with Stormforge to drive automation, drive decisioning as we expand and move more and more customers over to our new platform. We're going to uncover use cases, different challenges as we go. So I think it's a learning process for both sides, but I think it's been successful so far and has a lot of potential. Sounds like you had a great business and had a great new partnership. So thanks so much for coming on theCUBE. Thank you very much. Appreciate your time. All right, my pleasure. And thank you for watching theCUBE. You're a global leader in enterprise tech coverage. Stormforge automates Kubernetes resource efficiency at scale. The Stormforge platform provides all the tools you need to run your Kubernetes applications at peak performance and efficiency. In this video, I will walk you through the newest addition to the Stormforge platform. We call it Optimize Life, a revolutionary, simple way to optimize your containers. Optimize Life basically looks at observability data you're already capturing from your production system. Our machine learning is learning from historical usage and trends and then making recommendations for updated CPU and memory settings at whatever frequency you specified when you configured it. After you deployed Optimize Life and connected it to your observability tools, it automatically detects all your apps within your production system. From this list, you can select the applications you want to optimize. Let's take a look at how that works. After selecting an application, you just have to follow a few simple steps. For CPU and memory, you can define upper and lower limits and your risk tolerance. Next, you select the recommendation frequency. This defines how often the machine learning algorithm calculates a new recommendation. And as a last step, you can choose between patching the configuration for this container manually or automatically. Optimize Life also comes with a Grafana dashboard to visualize the results of your optimizations. What you can see here is an example of what the actual results might look like. In this example, the yellow line is actual CPU usage. The blue and red show the CPU request and limits as recommended by machine learning. You can see how closely they track the actual usage. Optimize Life has already a broad ecosystem. It runs on any cloud with any CNCF certified Kubernetes distribution and integrates well with your already existing observability tools. Stormforge optimization has never been easier. Okay, we're set to wrap up this session on solving K8's complexity gap optimizing with machine learning brought to you by Stormforge. You know, containers, they're all about simplifying the packaging of application components. And the world needed an abstraction layer to simplify the management of all these containers that are being created and deployed. Hence the explosive adoption of Kubernetes, which rose from a series of improbable events to take the application development world by storm. We heard today how Stormforge is introducing Optimize Live, marrying data from pre-production environments with telemetry data from observability platforms in production settings and using machine intelligence to accelerate insights on which actions to take to improve application performance. Now being able to correlate what you thought was going to happen and be optimal in a pre-production environment and then iterating on what's actually happening in a real world production setting and bridging the gap between those two worlds. That's new and that's exciting. You know, unlike the days of virtualization where this type of optimization took the better part of a decade and a ton of tribal knowledge, in today's world that time to optimization is being compressed by companies like Stormforge combining data with AI and cloud native APIs to leverage an ecosystem of innovations in observability to accelerate high quality application delivery. Kubernetes is storming the castle and there's no stopping it. Stormforge and a host of companies are stepping up to help customers take advantage of this wave by delivering technologies that help predict and manage customer experiences and accelerate innovation. Remember, all these sessions will be available immediately on demand at thecube.net and at stormforge.io. Thanks for watching Solving the Kubernetes complexity gap by optimizing with machine learning brought to you by Stormforge and thecube, your leader in enterprise tech coverage. We'll see you next time.