For years, the conversation about technology costs in companies across the region revolved around a single protagonist: the cloud. Today, in this autumn of 2024, a second engine of spending has emerged, one that grows at its own pace and follows its own logic: generative AI. Every query to a model, every token processed, every hour of GPU reserved translates into an invoice that many organizations still don't know how to read. The good news is that the discipline we learned to apply to the cloud, FinOps, is maturing just in time to extend to AI. The question is no longer whether it makes sense to govern this spending, but how quickly you can gain visibility before the experiment becomes a structural cost.
In brief: Generative AI adds a new layer of cost on top of the cloud, with its own dynamics of tokens, inference, and GPUs that scale quickly and in counterintuitive ways. Mature FinOps responds with three practices: spending visibility, allocation by team or use case, and optimization based on choosing the right model for each task. The goal is not to spend less for the sake of spending less, but to connect every dollar invested with the value it generates.
Traditional cloud cost, though variable, tends to be reasonably predictable: instances, storage, data transfer. Generative AI introduces new variables that break that intuition. It's worth understanding where the spending comes from before trying to control it:
The pattern is clear: the cost of AI behaves like a consumption expense that grows with success. Without discipline, a cheap proof of concept becomes a surprising budget line the following quarter.
FinOps is the practice that brings finance, engineering, and business together to make informed decisions about variable technology spending. It was born to tame the cloud, and its principles transfer naturally to AI, because both share the same challenge: resources provisioned on demand and billed by consumption.
FinOps maturity is usually described in three stages that apply equally well to the new context:
The key is not to treat AI as a separate silo. The cost of AI adds to that of the cloud and should be governed within the same framework, with the same people at the table. If your organization has already built FinOps capabilities for cloud infrastructure, extending them to AI is an evolution, not a project from scratch.
The first obstacle with AI is that spending arrives aggregated and opaque. A single vendor invoice can hide dozens of applications, teams, and experiments. Gaining visibility means instrumenting consumption at the source:
Without this layer, conversations about cost become anecdotal. With it, you can move from "AI is costing us a lot" to "this specific use case has a cost per interaction that isn't justified."
Visibility without allocation is information without an owner. Attributing the cost of AI to teams and use cases changes the incentives: when an area sees its own consumption, it optimizes without anyone imposing it. Allocation also lets you answer the question every executive committee will eventually ask: what return is this investment producing?
Some practices that help:
Here is the most powerful lever, and the most underestimated. Not every task needs the largest, most expensive model. Using a maximum-capacity model to classify an email or extract a data point is like hiring a specialist for a routine task: it works, but the cost doesn't match the value.
The most effective optimization decisions tend to be architectural:
Designing solutions with this mindset from the start, an AI-first approach that weighs capability against cost in every decision, avoids the rework of having to optimize an expensive architecture after deploying it.
One-off optimization dissolves if it isn't sustained. Spend governance turns good practices into institutional habits:
Governance doesn't aim to slow innovation, but to give it a sustainable framework. An organization that knows how much each experiment costs can afford to experiment more, because it cuts what doesn't work in time and doubles down on what does.
No. The recommended approach is to extend the existing team and framework. AI cost adds to cloud cost and shares the same consumption logic, so it's best to govern them together with the same people and processes.
Because it scales with usage, not with a closed project. Every interaction consumes tokens and, unlike a fixed license, spending grows as adoption increases. That's why per-transaction visibility is so important.
Rarely. The biggest opportunities are usually in the architecture: choosing the right model for each case, shortening contexts, caching, and eliminating redundant steps. Those decisions reduce spending without sacrificing the quality of the result.
Before scaling. It's much easier to instrument visibility and allocation when you have a few use cases than when there are already dozens in production consuming without control.
You don't have to solve everything at once. The first step is to gain visibility: instrument AI consumption with tags and per-use-case measurement, and integrate it into the FinOps framework that already governs your cloud. From that foundation, allocation and optimization become business decisions, not technical guesswork. At SUMāTO, we help organizations across the region extend their cost discipline to the AI era, with a pragmatic approach that connects every investment with the value it produces. If you want to bring order to your cloud and AI spending before the experiment becomes structural, let's talk.