FinOps X 2026 | Tim Cassell, nOps

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Tim Cassell, nOps

Tim Cassell

Head of Product nOps

In this interview from FinOps X 2026, Tim Cassell, head of product and strategy at nOps, joins theCUBE's John Furrier and theCUBE Research's Paul Nashawaty to discuss how FinOps is shifting from cloud cost bookkeeping into an advisory discipline for AI value management. Cassell draws on decades of industry experience — from the dot-com era and PayPal to the early days of cloud at Workday — to argue that today's AI wave is structurally similar to prior technology shifts, only dramatically faster. He notes that while the "easy" cloud optimization wins are large... Read more

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How is nOps responding to the rise of AI/GenAI, and what impact has that had on product development and its role in the FinOps market? add

Has cloud optimization plateaued, especially given AI-driven changes and organizations capturing the obvious cloud savings? add

How does an AI optimization strategy differ from a cloud optimization strategy, and what challenges (including organizational stakeholders and measurement of results) do enterprises face when adopting AI? add

What is the future of FinOps? add

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Tim Cassell, nOps

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John Furrier

>> Welcome back onto theCUBE's live stream here in San Diego for FinOps X 2026. I'm John Furrier, your host of theCUBE with my coach Paul Nashawaty from theCUBE Research. Breaking down all the FinOps action as tokenomics comes into the equation, you're starting to see cloud spend on premise activity in the data center, so much activity. Tim Cassell's here, head of products for nOps. You've got the keys to the kingdom and the product group. Tim, great to see you. You got a job to do. You got a changing market, growing market. Relevance is going through the roof. Money's being instrumented. It's not just cost control, cost takeout. There's revenue on the table with GenAI and AI agents driving value, and so, revenue, cost, profit, it's a money game.

Tim Cassell

>> Yeah. It's interesting. I'm probably someone who's seen a lot of this throughout the history, man. I came to the Bay Area in the '90s as some young kid, and saw the first boom and thought that was crazy. Moved over to... I was working at Accenture, and it was just... I always tell people the '90s was unbelievable. Then had a kid, went over to PayPal and being able to see crazy innovation over there. Then eventually, found where we were focusing on one hour of the year. Building all this capacity for one out of year for a data center. After that, moved over to a startup called Workday. That's where I discovered Amazon. It was just this newer thing, and I thought that was the biggest thing, and it reminds me of why we're going now with AI. But what's going on now? It is unbelievably how different this one is. We're really trying to embrace it here at nOps, and it's one of the greatest tools.

Paul Nashawaty

>> Yeah. The exponential-

John Furrier

>> It's like all those waves combined into one times hundreds. The speed is insane.

Paul Nashawaty

>> Exponentially.

John Furrier

>> I'll just use a simple example at nOps. I tie it to a little bit over a year ago where we embraced it. I'm an idea guy. Then you have engineers and trying to translate engineer... Taking what's crazy in my mind, and then an engineer and then they're trying to understand me and next thing I know it's rinse and repeat and three months have gone by. Now, I have the power of taking those ideas and being able to build something very, very fast and get feedback very quickly. We really embrace. We even rewrote our entire application.

Paul Nashawaty

>> Well, you have to. You take this show, for example, this show has evolved over the years. Cloud optimization was really the main focus. But when we look at things with AI and play and how organizations are really capturing the obvious savings around cloud, has optimization plateaued?

Tim Cassell

>> No, the easy stuff has. The easy stuff is the way I look at it is, the typical and we focus a lot on the commitment side of the fence and not just doing these big bang, like I'm going to just put a big bet on the table and hope and pray for three years, and really lock you in. We do a lot of things that optimize that throughout to really reduce the risk. I look at that more of... I follow one of these podcasts, Acquired. I don't know if you're familiar with it. When FinOps started several years ago, it was something that people could latch onto. You didn't really have to think about architecture and stuff like that. Most people were coming up from more of a finance procurement background, and not this engineering focus. Yes, they spent a lot of time on that. That's great. But to really make an impact is like, "Why am I getting discounts on..." I'm sorry, I will say-

Paul Nashawaty

>> You can say shit.

Tim Cassell

>> Why are you getting discount on a bunch of shit you don't need? That really requires more engineering focus and being able to match supply and demand. That's the hard stuff. It hasn't plateaued. It's transitioning into more of the... That's where AI is coming into play. I think that's why we're starting to see a little bit more angst around that.

Paul Nashawaty

>> Yeah. I would probably call it more of an operational optimization maturity. It's like there's different levels and different organizations have different maturity in their journey. If they're in the image or early stages of it, they might be doing the low hanging fruit, trying to figure that out. But then as all that gets optimized, then it's like now you have to move up the stack.

Tim Cassell

>> You got to move up the stack. That's where it gets a little bit more uncomfortable.

John Furrier

>> I want to ask you a question. You mentioned angst, anxiety, angst, same thing. I remember talking to Andy Jassy at a hockey game one time about, he was talking about the business and how he's forecasting and he said, "We're doing locking in these long-term contracts." The analysts don't understand the discounting and how it relates to the quarters. Different conversation. But my point is that those long-term contracts for AWS are cost opportunities. That's out there. Second thing is that the data centers and the enterprises are back because you're having hybrid distributed computing happen, and all the users that are adopting AI inside the company who work inside the firewall are using tokens, but they're going to the cloud for that so the cloud wins. But now there's talk of putting AI factories in the enterprise like old school days, small little cluster, manage the tokens internally, create the... Now, a whole like networked token economy. What's your reaction? Because now, long-term contrast is... I won't say lock in, but I'll say it, like commitment. Now, you have the shift to on premises, which is not just repatriation, it's integrated. How does FinOps rationalize that in your mind? Because that's a perfect opportunity to come in and slice and dice the levels of granularity to understand that.

Tim Cassell

>> Like I said, I hear a lot of what's going on here and there's, like I said, a lot of angst around this. I keep on going back to when the cloud and Amazon first started. There was tons of that back then too, but the biggest difference right now is we were all solving it by ourselves. We were all doing our own little thing, and trying to solve that. Then a group came together, and then they started to share same size. The advantage that I see the FinOps in doing this right now, with this whole token economics, if I screw up the name or whatever the hell they're calling it.

John Furrier

>> Tokenonomics.

Tim Cassell

>> It's the right move. The point I'm trying to get to is, we've learned a lot. A lot of new folks in FinOps maybe didn't walk that journey. They got it once it was already established. Well, guess what? It's no different to me. It's absolutely no different. It's something new. It's a different language. We always had a cost and a return of investment and all this kind of stuff. It's just different data sets, different ways of thinking that you can do that.

Paul Nashawaty

>> I agree with you. It's the same, but it's different. But the difference I think between a cloud optimization strategy and an AI optimization strategy is... You kind of nailed it. The fact is there's more hands in on AI than we're in with on cloud. Enterprises have this gap now. They may have done AWS or GCP or Azure optimization around the cloud. Great. Fantastic. They've optimized. They know it. But now, there's a gap in enterprises that are saying, "Well, now that they have this multi-cloud reality that's in place." But then on top of that, now you're layering in AI. By the way, it's not just your tech teams, it's your marketing and your sales and your finance and everybody else is touching it. That's a reality too.

Tim Cassell

>> Yeah. It comes down to the same basic blocking and tackling is the way I... Sometimes we just got to get back to the basics and the discipline. You can't solve what you can't see, and it's a different type of data set, a different... What I always find interesting about the AI space is a little bit different than the cloud to throw that out. Yeah. You got a lot more people in there. It's interesting. We were just talking to some of the marketing stuff. I didn't know they were even doing some of this stuff and it was just great to see and embracing the technology. But what's cool about this data set that AI brings that the cloud didn't, is so much richer on what's in there. It's not just what I sent it, but what are the results. I'll be honest with you, once you figure out that data set, you're going to be able to measure the actual results much easier... At least in my opinion, it's going to take some time and wringling, but you'll be able to measure that ROI easier than it was in the cloud because it was separate.

Paul Nashawaty

>> Yeah.

John Furrier

>> Talk about the cloud relationship to AI because we were talking earlier, a bunch of other folks, experts and practitioners. If you were doing the cloud, you had good prep for AI. I think I use the term middle of the fairway. You're ready for the next shot.

Tim Cassell

>> My game my man.

John Furrier

>> If you're in the cloud game, they've done their work, the ball's in the middle of the fairway, now you got the next shot is AI. It's a lot of synergies. How should people think, in your opinion, around how to leverage that experience with AI now? Velocity's higher, more tools, more vendors, more practitioners, more people in the community. How do you go from that middle of the fairway cloud spending journey, and take to the next shot?

Tim Cassell

>> Yeah. To me, and I'm sorry, I'm a simple West Virginia guy. These problems to me have always been the same. It's just different technology and different things. Like I said, it's because it's so unknown or we're not as comfortable with the type of data that we were traditionally getting to help our businesses, I think is driving a lot of that. Once you start to understand that underlying data, it's still the same shit that we've been doing since the beginning of time to me. That's why once again, why I loved... I'll be honest with you, I wasn't so much the biggest fan of it sometimes, the FinOps thing, because I wasn't thinking it was that innovating, but what he did, like being able to break this off and because we don't know all the answers and being able to get it at the beginning versus like in the cloud time, we'd already started and then we're trying to wrangle it in. Here we're at the beginning and being able to define that and maybe have a little bit more... How would I say it? More influence into how all this information can be normalized to help companies. But you brought up the greatest point. It's not just a bunch of engineers using it now. It's everybody in the organization. My favorite new tool is Cowork. I love it. I want it to be my single point for everything I do. Then engineers or Cursor or different tools. Once you are able to start to wrangle that in and be able to realize that you will be able to measure that value, I think it's going to be an exciting time. I'm not as scared about it as others because I just look at it as like, the internet was different.

John Furrier

>> You live through some other waves.

Tim Cassell

>> You lived through a lot of these waves, and it's no different. The only difference is how fast it is. That moves the ball and got the new shiny, this, this, this. It's like just getting back to basis and-

John Furrier

>> It's like pro sports. It's the pace of play is higher and faster game.

Tim Cassell

>> It's a faster game.

John Furrier

>> I guess my final question for you is, what's the future of FinOps? Because remember, you have all these other adjacencies in the technology stack, iMap, SAM, all coming together because they have to come together, and not be siloed. What's your view?

Tim Cassell

>> Yeah. I think it's going to be less of what I would say reporting bookkeeping and providing that to more of an advisory role. Because now, you're going to have... A lot of the blocking and tackling, you can have agents do that today, and so, it's going to have more influence on what is it that we go after and get returns. More of bookkeeper versus advisory.

John Furrier

>> Yeah. Reporting. Analytics used to be dashboards. Now, you got value.

Tim Cassell

>> The way we use it, man, I just want... I still like to see the pictures. But it's like, what is that picture telling me now? Instead of me going through and I can get it, is now I can leverage the Agentix stuff to help me tell what is that story.

John Furrier

>> And the real time nature. Tim, great to have you on.

Tim Cassell

>> Awesome.

John Furrier

>> Very dynamic. You give me a lot of energy here, and we'll see on the golf course.

Tim Cassell

>> Yeah. I love it.

John Furrier

>> All right. I'm John Furrier with Paul Nashawaty here at FinOps X in San Diego. People are chill here hanging out, but they're solving a lot of big problems. An engineering game and it's going to report in on what's going on with the money and the tokens and the platforms all coming together. Thanks for watching.