FinOps X 2026 | Omer Müller, Adaptive6

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Omer Müller

Co-Founder & Chief Technology Officer

Adaptive6

play_circle_outline FinOps X 2026 event focusing on AI cost and governance

play_circle_outline Adaptiv6: AI-native company using AI across engineering and product

play_circle_outline Sensor-Driven Runtime Visibility for Cloud Cost Governance and Optimization

play_circle_outline Product features: 400+ detections, infra-as-code fixes, automated workflows, AI remediations

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Omer Müller, Adaptive6

Omer Müller

Co-Founder & Chief Technology Officer Adaptive6

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John Furrier

>> Welcome back, we're at theCUBE's livestream here at FinOps X 2026.

Paul Nashawaty

>> It's a hard name to say.

John Furrier

>> Boy, long day of interviews. It's getting to us, but we're doing our part, do whatever it takes here in San Diego. A lot of action. This is a show that's highly compelling because it's bringing together a lot of communities around the idea of putting cost and the value to the AI. And of course, a lot of deep tech is coming to the surface. Governance, identity, attribution. This is the top . Of course, the main theme is tokens, and how much they cost. Omer Müller is here, co-founder and CTO of Adaptiv6. Omer, great to see you. Thanks for coming on theCUBE.

Omer Müller

>> Great meeting you. Thank you.

John Furrier

>> Well, we're pumping on all cylinders here in theCUBE. We'll drop dead and get every interview we can. Thank you for coming on.

Omer Müller

>> Thank you.

John Furrier

>> I really appreciate it. I mean, one of the things that I'm intrigued by is that I love the deep tech. Linux Foundation, open source, deep tech, but the conversation's bubbling up to, it feels like platform re-engineering in the enterprise, and the hyperscalers, because the money's on the table. It's the costs. That's the focus.

Omer Müller

>> Of course.

John Furrier

>> Of course, generative AI is driving productivity and revenue, which has got everyone's attention. So you have, this isn't just an IT thing, this is a wave. What is your reaction? You guys are an AI-native company.

Omer Müller

>> Yeah. So we are an AI-native company. Everything we do internally is driven by AI. Obviously, our engineering is entirely 100% done by AI, obviously with supervision of people. But with AI, our product efforts are combining AI technologies, sales, solution engineering, really try to kind of combine AI in anything we do inside the company, and obviously inside the product as well.

John Furrier

>> Talk about when you guys started the company, what is your core value proposition?

Omer Müller

>> So our core value proposition is cloud cost governance and optimization. We actually help very, very large organizations reduce costs end-to-end.

Paul Nashawaty

>> Yeah. So this is an important topic. I mean, we hear this quite a bit. I've been to about four or five European shows. Governance, data sovereignty, it's a big part of the focus, and largely due to the fact that there's a lot of builders that are building applications now, and access to information that they historically haven't had access to. So when we think about that, what is your thoughts around the AI tools that allows for these, just call it the builders for the lines of business, the citizen developers, and the professional developers, how is this all kind of coming together in your world when it comes to governance?

Omer Müller

>> Yeah, that's a good question. So what we see is obviously AI is taking over everything. We're obviously just in the beginning, and everything I'll say now will be deprecated in probably a month from now. But even now we see our finance practitioners using AI in many, many cases, like across the board, from just connecting their favorite tools through MCP, and like, whether interactively asking questions, or connecting them to the workflows, to, I don't know, fetch everything from that tool, compare it to that tool, create summarization of things. Another thing is they actually, some platforms, ours included, we allow our users to actually create new features using AI. So something that would take engineers and product efforts not that while ago could now be done using a simple prompt. For example, I would like to see all my open issues with the most expensive violations, or most resources that they have open tickets in that status. I want to see them every day, so I'm just creating this kind of component for that.

Paul Nashawaty

>> So the productivity that happens, it's definitely we're seeing that, we see an acceleration of workloads and making things easier to use. But there's a cost associated with that. Is the juice worth the squeeze?

Omer Müller

>> It's a really good question, and yes and no. AI is not just worth the squeeze, sometimes it's mandatory. Or if it's not mandatory, it shouldn't be AI. What do I mean by that? Sometimes people use AI to build things that can be perfectly be done with just regular technologies. So we have very good programming languages, and we can ... The advantage of them is that they are deterministic, they're fast, and they're efficient in terms of cost, but they can't do anything. If I want to summarize a ticket to get all the important information into a summarization, then send it to my engineer, that summarization part is very hard to develop. So for that part, you would definitely want AI, and yeah, it's worth the cost. But if I just want to do, I don't know, maintain things with AI, just opening the ticket with a constant field from a database, I shouldn't use AI for that.

Paul Nashawaty

>> Yeah. There's also external pressures too, though. So market pressures out there are saying, "If you're not using AI, then your organization's going to be boxed out." So there's that kind of factor, too.

Omer Müller

>> You definitely need to use AI and you need to use AI for everything that AI is very good for. It's very good for things that cannot be done well with deterministic code. It's very good for building that deterministic code, or building that product feature, or building that product design, or kind of sketching and designing. It's amazing for that. Inside the actual production workload, I would say it's better for tasks that are hard to engineer, to actually write code for.

John Furrier

>> What's interesting about this show, and I'd love to get your reaction to this, is that it's not a CFO show. It's not about finance. This is an engineering show.

Omer Müller

>> Of course.

John Furrier

>> Okay? It's the best time to be an engineer right now. If you look at what's happening, it's like, awesome. However, the finance roles are leaning in, versus monitoring, "Here's your budget." So it reminds me of security days. When we had to shift left in security, that changed the CI/CD pipelining, that created DevSecOps. We seem to be in kind of a similar movement with all the growth in AI. A lot of engineering going on, a lot of infrastructure's code-like things, but it's still now a finance application, because money's involved.

Omer Müller

>> Yeah, you're completely right.

John Furrier

>> What's your reaction to that? Because engineers have still got to be managing the code, managing agents, and designing the system. What's your reaction?

Omer Müller

>> Yeah, so you're completely right. Everything was with shift left in cybersecurity, and the same thing is happening with cost and AI. Everything need to be shifted left. You need to not deploy things or you need to kind of block them during the coding phase. Nothing that wastes money should be deployed to production if you can prevent it beforehand in your CI/CD pipeline.

John Furrier

>> So we're going to change the new, we're going to create a new category, Paul. DevSec FinOps.

Omer Müller

>> Of course.

John Furrier

>> Okay? That's what's actually happening.

Omer Müller

>> I'm not even 100% sure you just created it. I think some people are already discussing that.

John Furrier

>> I thought about it. They copied my brain. But all joking aside, that's where ... The convergence of Sec and Ops was DevSecOps. FinOps stands alone at , I guess. But together, they're managed as one unified thing.

Omer Müller

>> Yeah. It's a good question. Well, security is usually, you have the security team, but the actual work is many times being done by the platform teams and the infrastructure teams. So I think the same thing will happen with FinOps. So FinOps team is a bit like the security ... I come from security in my background. So FinOps team is a bit like the CISO, but many times or most of the times the work that is actually being done are done by engineers in the platform teams. And those teams are exactly the teams which are the DevSecOps, and which will now become the DevSec FinOps teams.

John Furrier

>> Okay. So what's the evolution? Because let's just take this to the next level. If you believe what we just said to be true, which I think we do, then the automation kicks in. Now you've got agentic, you got code generation. There's a different role of the engineer. They may not be writing code, but they're curating code, they're managing. They're essentially the responsible, they're accountable for their agents, their fleets of agents, their cattle, their pets, all that will be on them. What do they do? They just sit back and go, "Okay."

Omer Müller

>> What do managers do? I think every engineer now, instead of being the leaf of the tree, or most engineers would kind of become a mini-manager. Manager, not of humans, but of AI agents that would write code, and obviously that engineer, that manager would need to be accountable for that code.

John Furrier

>> When I was in my youth, when I got my degree, actually, I graduated as a software engineer. That was the title in the '80s. Then it became developer. Coder, vibe coder. So what does an engineer do? And what's the new normal of an engineer, and what do they do? What's the makeup of a successful-

Omer Müller

>> That's a good question. Well, I think the essence of engineering is still understanding problems, understanding how to solve problems, understanding when a solutions for a problem is not working. And I think this is going to stay. You had the Assembly, I don't know, 40 years ago, or maybe more.

John Furrier

>> 30.

Omer Müller

>> Then you had C and C++, then you had Java and Python, and each time it felt like engineering is different. But each time, engineering kind of stayed the same. It's understanding problems, understanding solutions, understanding when things are not going well. Even now with AI agents, they are good, AI agents, but they make many mistakes. So you need to kind of see where things are going.

John Furrier

>> Yeah. I think one common thread, I would say, is, I've heard the word operating model, Jensen Huang says operating system on stage all the time. The systems element of FinOps and all DevOps is like, it's systematic. If I spend 50K here, I can get a million dollars of value here, but the silos we are seeing are breaking down in FinOps. Because I might not have the budget for 50K. We heard that earlier, but that little budget, over budget, would have saved over ... So there's this subsystem consequence reaction. This is the new thing.

Omer Müller

>> Yeah. I think the center of that, again, we get back to, I think SRAs and platform and infrastructure teams. I think they are kind of the center point of security, performance, reliability and FinOps costs. Like not the FinOps people, but in terms of the engineering, the engineering counterpart of these people. So yeah.

Paul Nashawaty

>> Yeah. Omer, you mentioned that, I like where you were going. You're right that the evolution of developers has changed, and AI is just one more evolution. So I get that.

Omer Müller

>> It's a huge evolution.

Paul Nashawaty

>> It's a huge amount.

Omer Müller

>> I'm not saying it's a regular evolution. It's a huge evolution.

Paul Nashawaty

>> Right, it's huge. But the fact is there's still ... We talked about this earlier. There's FinOps tasks that are best suited for AI, right? There's also FinOps tasks that are not suited for AI. What would that be?

Omer Müller

>> Good question. So the two questions that you need to ask yourself, in my opinion, is how well would an AI perform the task, and what happens if it doesn't perform the task well? So for example, if you have a task that 99% AI will do well, but for that 1%, I don't know, you'll be in a catastrophe, so that's not a good task for AI. So what are good tasks for AI? One good task is kind of research and content creation. You want to look at all the issues of some kind, and get the gist of them, and then creating ticket, again, automatically, with all the relevant context, or business context even, technical context, of course. If you want to create a newsletter, that's a really good thing. Now, almost anything that is kind of read-only is a very good use case. Things that are not read-only should probably be treated into two categories. The non-sensitive or non-catastrophic resources or environments, which can still probably be done by AI, and maybe you want even AI guardrail or something like that, but you say, "Okay, it would work for 95% of the time," and for the 5% that it doesn't work, it would still be okay. But sensitive environments, environments under compliance, those kind of places is where you probably don't want AI to be completely autonomous. You would want AI to maybe perform the research, maybe perform the summarization, maybe get all the data, but at the end of the day, you would want an engineer to be accountable for changing these environments, or making, performing change in these environments.

Paul Nashawaty

>> That makes sense.

John Furrier

>> What's the big blockers in FinOps to go the next level in your opinion? What's the blockers that need to be removed, innovation that needs to be happening?

Omer Müller

>> It's a good question. So in our opinion, one of the thing is actually looking more at the applicative level. So we are just launched recently, we have a cloud cost governance and optimization platform that is based on four pillars. The cloud, which most vendors are looking at, the code, which helps you get more code context to understand where resource came from, especially in infrastructure's code. And we just added the runtime aspect. And the runtime aspect is a simple optional runtime sensor that allows us to see into the applicative context. So for example, we can see bad code being run that can be optimized to save costs. So this is something that usually FinOps people don't talk about, but it's huge. Because imagine that now using AI, you would be able, theoretically, to write the perfect code. The perfect code would be 90% cheaper than what's usually being written.

John Furrier

>> Omer, well, I want to thank you. Put a plug in for your company. What are you optimizing for? What are you focused on?

Omer Müller

>> So we are focusing on the entire cloud, cloud workloads, AI workloads, data platform workloads, really everything you have. We are kind of across the board. We have more than 400 types of detections related to waste and shadow waste that we find. We connect to your code in order to provide infrastructure code fixes for those problems. We have automated workflows in order to drive engineering action. We have AI remediations. We really kind of try to encompass everything, in order to do A to Z cloud cost dominant -

John Furrier

>> You're targeting engineers. You're targeting engineers.

Omer Müller

>> Our customer is the FinOps person, but we help that FinOps person drive engineering action using our workflows, for example.

John Furrier

>> Awesome. Well, thank you for sharing on theCUBE. Thanks for coming on.

Omer Müller

>> Thank you very much.

John Furrier

>> All right. I'm John Furrier with Paul Nashawaty here at FinOps, breaking it down. As the equation gets focused on, the cost piece is going to drive the value. And of course the ROI is the revenue, and the value creation with AI is coming. It's in the cloud, it's on premises, it's at the edge. It's everywhere, and they're working hard to get it done. We're doing our part. Thanks for watching.

Omer Müller, Adaptive6

search

John Furrier

>> Welcome back, we're at theCUBE's livestream here at FinOps X 2026.

Paul Nashawaty

>> It's a hard name to say.

John Furrier

>> Boy, long day of interviews. It's getting to us, but we're doing our part, do whatever it takes here in San Diego. A lot of action. This is a show that's highly compelling because it's bringing together a lot of communities around the idea of putting cost and the value to the AI. And of course, a lot of deep tech is coming to the surface. Governance, identity, attribution. This is the top . Of course, the main theme is tokens, and how much they cost. Omer Müller is here, co-founder and CTO of Adaptiv6. Omer, great to see you. Thanks for coming on theCUBE.

Omer Müller

>> Great meeting you. Thank you.

John Furrier

>> Well, we're pumping on all cylinders here in theCUBE. We'll drop dead and get every interview we can. Thank you for coming on.

Omer Müller

>> Thank you.

John Furrier

>> I really appreciate it. I mean, one of the things that I'm intrigued by is that I love the deep tech. Linux Foundation, open source, deep tech, but the conversation's bubbling up to, it feels like platform re-engineering in the enterprise, and the hyperscalers, because the money's on the table. It's the costs. That's the focus.

Omer Müller

>> Of course.

John Furrier

>> Of course, generative AI is driving productivity and revenue, which has got everyone's attention. So you have, this isn't just an IT thing, this is a wave. What is your reaction? You guys are an AI-native company.

Omer Müller

>> Yeah. So we are an AI-native company. Everything we do internally is driven by AI. Obviously, our engineering is entirely 100% done by AI, obviously with supervision of people. But with AI, our product efforts are combining AI technologies, sales, solution engineering, really try to kind of combine AI in anything we do inside the company, and obviously inside the product as well.

John Furrier

>> Talk about when you guys started the company, what is your core value proposition?

Omer Müller

>> So our core value proposition is cloud cost governance and optimization. We actually help very, very large organizations reduce costs end-to-end.

Paul Nashawaty

>> Yeah. So this is an important topic. I mean, we hear this quite a bit. I've been to about four or five European shows. Governance, data sovereignty, it's a big part of the focus, and largely due to the fact that there's a lot of builders that are building applications now, and access to information that they historically haven't had access to. So when we think about that, what is your thoughts around the AI tools that allows for these, just call it the builders for the lines of business, the citizen developers, and the professional developers, how is this all kind of coming together in your world when it comes to governance?

Omer Müller

>> Yeah, that's a good question. So what we see is obviously AI is taking over everything. We're obviously just in the beginning, and everything I'll say now will be deprecated in probably a month from now. But even now we see our finance practitioners using AI in many, many cases, like across the board, from just connecting their favorite tools through MCP, and like, whether interactively asking questions, or connecting them to the workflows, to, I don't know, fetch everything from that tool, compare it to that tool, create summarization of things. Another thing is they actually, some platforms, ours included, we allow our users to actually create new features using AI. So something that would take engineers and product efforts not that while ago could now be done using a simple prompt. For example, I would like to see all my open issues with the most expensive violations, or most resources that they have open tickets in that status. I want to see them every day, so I'm just creating this kind of component for that.

Paul Nashawaty

>> So the productivity that happens, it's definitely we're seeing that, we see an acceleration of workloads and making things easier to use. But there's a cost associated with that. Is the juice worth the squeeze?

Omer Müller

>> It's a really good question, and yes and no. AI is not just worth the squeeze, sometimes it's mandatory. Or if it's not mandatory, it shouldn't be AI. What do I mean by that? Sometimes people use AI to build things that can be perfectly be done with just regular technologies. So we have very good programming languages, and we can ... The advantage of them is that they are deterministic, they're fast, and they're efficient in terms of cost, but they can't do anything. If I want to summarize a ticket to get all the important information into a summarization, then send it to my engineer, that summarization part is very hard to develop. So for that part, you would definitely want AI, and yeah, it's worth the cost. But if I just want to do, I don't know, maintain things with AI, just opening the ticket with a constant field from a database, I shouldn't use AI for that.

Paul Nashawaty

>> Yeah. There's also external pressures too, though. So market pressures out there are saying, "If you're not using AI, then your organization's going to be boxed out." So there's that kind of factor, too.

Omer Müller

>> You definitely need to use AI and you need to use AI for everything that AI is very good for. It's very good for things that cannot be done well with deterministic code. It's very good for building that deterministic code, or building that product feature, or building that product design, or kind of sketching and designing. It's amazing for that. Inside the actual production workload, I would say it's better for tasks that are hard to engineer, to actually write code for.

John Furrier

>> What's interesting about this show, and I'd love to get your reaction to this, is that it's not a CFO show. It's not about finance. This is an engineering show.

Omer Müller

>> Of course.

John Furrier

>> Okay? It's the best time to be an engineer right now. If you look at what's happening, it's like, awesome. However, the finance roles are leaning in, versus monitoring, "Here's your budget." So it reminds me of security days. When we had to shift left in security, that changed the CI/CD pipelining, that created DevSecOps. We seem to be in kind of a similar movement with all the growth in AI. A lot of engineering going on, a lot of infrastructure's code-like things, but it's still now a finance application, because money's involved.

Omer Müller

>> Yeah, you're completely right.

John Furrier

>> What's your reaction to that? Because engineers have still got to be managing the code, managing agents, and designing the system. What's your reaction?

Omer Müller

>> Yeah, so you're completely right. Everything was with shift left in cybersecurity, and the same thing is happening with cost and AI. Everything need to be shifted left. You need to not deploy things or you need to kind of block them during the coding phase. Nothing that wastes money should be deployed to production if you can prevent it beforehand in your CI/CD pipeline.

John Furrier

>> So we're going to change the new, we're going to create a new category, Paul. DevSec FinOps.

Omer Müller

>> Of course.

John Furrier

>> Okay? That's what's actually happening.

Omer Müller

>> I'm not even 100% sure you just created it. I think some people are already discussing that.

John Furrier

>> I thought about it. They copied my brain. But all joking aside, that's where ... The convergence of Sec and Ops was DevSecOps. FinOps stands alone at , I guess. But together, they're managed as one unified thing.

Omer Müller

>> Yeah. It's a good question. Well, security is usually, you have the security team, but the actual work is many times being done by the platform teams and the infrastructure teams. So I think the same thing will happen with FinOps. So FinOps team is a bit like the security ... I come from security in my background. So FinOps team is a bit like the CISO, but many times or most of the times the work that is actually being done are done by engineers in the platform teams. And those teams are exactly the teams which are the DevSecOps, and which will now become the DevSec FinOps teams.

John Furrier

>> Okay. So what's the evolution? Because let's just take this to the next level. If you believe what we just said to be true, which I think we do, then the automation kicks in. Now you've got agentic, you got code generation. There's a different role of the engineer. They may not be writing code, but they're curating code, they're managing. They're essentially the responsible, they're accountable for their agents, their fleets of agents, their cattle, their pets, all that will be on them. What do they do? They just sit back and go, "Okay."

Omer Müller

>> What do managers do? I think every engineer now, instead of being the leaf of the tree, or most engineers would kind of become a mini-manager. Manager, not of humans, but of AI agents that would write code, and obviously that engineer, that manager would need to be accountable for that code.

John Furrier

>> When I was in my youth, when I got my degree, actually, I graduated as a software engineer. That was the title in the '80s. Then it became developer. Coder, vibe coder. So what does an engineer do? And what's the new normal of an engineer, and what do they do? What's the makeup of a successful-

Omer Müller

>> That's a good question. Well, I think the essence of engineering is still understanding problems, understanding how to solve problems, understanding when a solutions for a problem is not working. And I think this is going to stay. You had the Assembly, I don't know, 40 years ago, or maybe more.

John Furrier

>> 30.

Omer Müller

>> Then you had C and C++, then you had Java and Python, and each time it felt like engineering is different. But each time, engineering kind of stayed the same. It's understanding problems, understanding solutions, understanding when things are not going well. Even now with AI agents, they are good, AI agents, but they make many mistakes. So you need to kind of see where things are going.

John Furrier

>> Yeah. I think one common thread, I would say, is, I've heard the word operating model, Jensen Huang says operating system on stage all the time. The systems element of FinOps and all DevOps is like, it's systematic. If I spend 50K here, I can get a million dollars of value here, but the silos we are seeing are breaking down in FinOps. Because I might not have the budget for 50K. We heard that earlier, but that little budget, over budget, would have saved over ... So there's this subsystem consequence reaction. This is the new thing.

Omer Müller

>> Yeah. I think the center of that, again, we get back to, I think SRAs and platform and infrastructure teams. I think they are kind of the center point of security, performance, reliability and FinOps costs. Like not the FinOps people, but in terms of the engineering, the engineering counterpart of these people. So yeah.

Paul Nashawaty

>> Yeah. Omer, you mentioned that, I like where you were going. You're right that the evolution of developers has changed, and AI is just one more evolution. So I get that.

Omer Müller

>> It's a huge evolution.

Paul Nashawaty

>> It's a huge amount.

Omer Müller

>> I'm not saying it's a regular evolution. It's a huge evolution.

Paul Nashawaty

>> Right, it's huge. But the fact is there's still ... We talked about this earlier. There's FinOps tasks that are best suited for AI, right? There's also FinOps tasks that are not suited for AI. What would that be?

Omer Müller

>> Good question. So the two questions that you need to ask yourself, in my opinion, is how well would an AI perform the task, and what happens if it doesn't perform the task well? So for example, if you have a task that 99% AI will do well, but for that 1%, I don't know, you'll be in a catastrophe, so that's not a good task for AI. So what are good tasks for AI? One good task is kind of research and content creation. You want to look at all the issues of some kind, and get the gist of them, and then creating ticket, again, automatically, with all the relevant context, or business context even, technical context, of course. If you want to create a newsletter, that's a really good thing. Now, almost anything that is kind of read-only is a very good use case. Things that are not read-only should probably be treated into two categories. The non-sensitive or non-catastrophic resources or environments, which can still probably be done by AI, and maybe you want even AI guardrail or something like that, but you say, "Okay, it would work for 95% of the time," and for the 5% that it doesn't work, it would still be okay. But sensitive environments, environments under compliance, those kind of places is where you probably don't want AI to be completely autonomous. You would want AI to maybe perform the research, maybe perform the summarization, maybe get all the data, but at the end of the day, you would want an engineer to be accountable for changing these environments, or making, performing change in these environments.

Paul Nashawaty

>> That makes sense.

John Furrier

>> What's the big blockers in FinOps to go the next level in your opinion? What's the blockers that need to be removed, innovation that needs to be happening?

Omer Müller

>> It's a good question. So in our opinion, one of the thing is actually looking more at the applicative level. So we are just launched recently, we have a cloud cost governance and optimization platform that is based on four pillars. The cloud, which most vendors are looking at, the code, which helps you get more code context to understand where resource came from, especially in infrastructure's code. And we just added the runtime aspect. And the runtime aspect is a simple optional runtime sensor that allows us to see into the applicative context. So for example, we can see bad code being run that can be optimized to save costs. So this is something that usually FinOps people don't talk about, but it's huge. Because imagine that now using AI, you would be able, theoretically, to write the perfect code. The perfect code would be 90% cheaper than what's usually being written.

John Furrier

>> Omer, well, I want to thank you. Put a plug in for your company. What are you optimizing for? What are you focused on?

Omer Müller

>> So we are focusing on the entire cloud, cloud workloads, AI workloads, data platform workloads, really everything you have. We are kind of across the board. We have more than 400 types of detections related to waste and shadow waste that we find. We connect to your code in order to provide infrastructure code fixes for those problems. We have automated workflows in order to drive engineering action. We have AI remediations. We really kind of try to encompass everything, in order to do A to Z cloud cost dominant -

John Furrier

>> You're targeting engineers. You're targeting engineers.

Omer Müller

>> Our customer is the FinOps person, but we help that FinOps person drive engineering action using our workflows, for example.

John Furrier

>> Awesome. Well, thank you for sharing on theCUBE. Thanks for coming on.

Omer Müller

>> Thank you very much.

John Furrier

>> All right. I'm John Furrier with Paul Nashawaty here at FinOps, breaking it down. As the equation gets focused on, the cost piece is going to drive the value. And of course the ROI is the revenue, and the value creation with AI is coming. It's in the cloud, it's on premises, it's at the edge. It's everywhere, and they're working hard to get it done. We're doing our part. Thanks for watching.