Operations | Monitoring | ITSM | DevOps | Cloud

What do 475 senior leaders across software, financial services, cybersecurity, and other industries all have in common? They have little to no idea whether their AI investments are paying off. CloudZero just released FinOps in the AI Era: A Critical Recalibration, a report assessing the state of cloud and AI spending. Culled from hundreds of responses from people directly accountable for cloud spending, the report shows that while FinOps maturity is accelerating, cloud efficiency is plummeting.

Whoever thought we’d see the day when cloud cost management (CCM) seemed easy? CloudZero just released FinOps In The AI Era: A Critical Recalibration, an annual report on the state of cloud and AI costs. The report surfaced what looks like a paradox: FinOps maturity is accelerating, but organizational cloud efficiency is plummeting. 72% of organizations now have formal cloud cost management (CCM) programs. That’s nearly double what we saw in our last survey (39%).

Q: What do you call it when FinOps maturity surges but cloud efficiency plummets? A: An AI-nigma. I don’t claim to be a comedian. But I do claim to be Fred FinOps, so the paradoxical findings from CloudZero’s new report titled FinOps in the AI Era: A Critical Recalibration, created in partnership with B2B SaaS benchmarking firm Benchmarkit, had me scratching my head. The good news: These numbers tell a story of cloud cost maturity and control. But then there’s the bad news.

According to our new report, FinOps in the AI Era: A Critical Recalibration, 40% of companies now spend $10M or more annually on AI. Most can’t tell you if it’s working. That’s not a budgeting problem. It’s a systems problem. And Donella Meadows wrote the playbook for understanding it.

Welcome to February’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from experiment to expectation. Last month, we closed out 2025 with a settling: provider shares locked in, compute softened, and AI claimed more of the mix (big surprise there). January confirmed those patterns weren’t year-end hustle and bustle. They signify a new baseline. Also, the Big Three (AWS, GCP, Azure) barely moved. They’re as entrenched as can be.

Kubernetes and OpenStack are not competitors. They operate at different layers of the stack and are often used together. OpenStack manages cloud infrastructure such as compute, storage, and networking. Kubernetes runs on top of that infrastructure to deploy, scale, and manage containerized applications. Teams often compare them as alternatives, but in practice, Kubernetes frequently runs on OpenStack.

Like most SaaS companies, you’re under pressure to ship AI-powered features faster, smarter, and at scale. For many teams, that pressure leads to relying on external AI platforms, managed models, and third-party APIs instead of building everything from scratch in-house. At first, it feels like a win. Your team ships an AI-powered feature in weeks instead of months. No GPU clusters to manage. No models to train. No infrastructure to babysit.

For most of the last decade, the direction of cloud strategy was clear: standardize, consolidate, and reduce sprawl. Engineering teams worked to pick a primary cloud, reduce vendor dependencies, and simplify their stacks. FinOps teams unwound years of fragmentation. Platform teams built guardrails to make sure it didn’t happen again. Then AI arrived, and it’s a fundamentally different class of workload. AI demands specialized hardware and, increasingly, diverging providers.

AI is the new frontier for FinOps maturity. It introduces fresh spend patterns and new opportunities for value. As GPUs, inference, and retraining reshape costs, FinOps maturity grows through visibility, forecasting, and shared mindset about how these workloads drive business impact. In this 2025 post, I gave my guidelines for implementing AI tagging to give business context and clarity to vague AI invoices. Now, I’m sharing the next level up: how to drive FinOps in AI with AI.

When Anthony Leung talks about FinOps, he’s speaking from operating at real scale — not theory. As VP of Engineering Platforms and Security Research at Forcepoint, he led a transformation that cut cloud spend in half while improving availability, and built a culture where engineers own their economics.