FinOps fixed cloud. Here’s why AI needs its own category

Remember 2013?

Cloud spend was spiralling. Teams were provisioning infrastructure with no visibility, no accountability and no shared framework for asking the most basic question: is this necessary?

Then FinOps happened. Not just as a set of tools, as a discipline. A shared language between Finance and Engineering that turned uncontrolled cloud costs into a manageable, measurable business function. Today FinOps is a profession, a community and a category worth billions.

AI inference spend is going through the same reckoning. The question is whether your organisation waits for the crisis or gets ahead of it.

The pattern is identical and it’s moving faster

When cloud first scaled, the problem wasn’t technical. It was organisational. Engineering teams could spin up resources faster than Finance could track them. Attribution was guesswork. By the time anyone noticed the number on the bill, the waste had already compounded.

AI inference spend is following the same trajectory, at a faster pace.

Organisations are deploying large language models across every function and often without centralised oversight, standardised usage policies, or any way to attribute token spend to outcome. The result: token waste compounds, costs spike and no one has a clear answer for the board when they ask what the AI budget is generating.

The difference between cloud in 2013 and AI today is that inference costs are harder to see. Cloud spend appeared on a single AWS or Azure bill. Token spend is fragmented across providers, embedded inside products, distributed across teams and metered in units of tokens, calls, compute measurements that Finance teams were never trained to interpret.

Why FinOps professionals own this problem

This is not a technology problem. It’s a governance problem.

The people best positioned to solve it are not the ones building AI models it’s the ones who already understand how to build financial discipline around rapidly scaling, technically complex infrastructure and that is FinOps.

The FinOps discipline maps directly to what AI cost governance requires: showback and chargeback that attributes inference spend to the teams and workflows consuming it; unit economics that maps cost to outcome rather than cost to token; rightsizing that ensures high-cost model usage is reserved for high-value tasks via policy-controlled routing and forecasting that predicts inference spend rather than reacting to last month’s bill.

The category doesn’t have a name yet. That’s the opportunity.

Cloud cost management was chaotic until FinOps gave it structure. AI cost governance is in the same pre-category moment and the organisations that define their internal frameworks now will be the ones setting the standard when the rest of the market catches up.

Enterprise AI deployments face 20 – 40% margin compression and 30–60% budget volatility without governance in place. That’s the same sprawl pattern cloud waste followed before FinOps disciplines were applied.

cortave reduces token spend by 50 – 80% by sitting between your AI apps and the LLMs, the same strategic position FinOps occupies for cloud, applied to AI inference. It’s not a bolt-on. It’s a layer in the stack.

What good looks like

An organisation with mature AI cost governance has four things: visibility into which models are running at what token spend; attribution that maps inference costs to business outcomes; guardrails that prevent token waste without blocking innovation and accountability shared between Finance and Engineering with shared metrics.

The question isn’t whether this category will emerge

It will. The same market dynamics that created FinOps are already in motion: rapid adoption, accelerating inference costs and Finance teams asking harder questions about returns.

The smartest AI companies won’t just scale fast. They’ll scale efficiently.