Your AI pilot worked. Now 200 employees want access.

I think companies are going to underestimate the second half of the AI problem. A proof of concept is easy to make look good. Put a model behind a chat box, connect a few documents and show it to a room. The hard part is figuring out what happens when everybody starts using it.

Now you have concurrency, usage limits, access controls and somebody asking why the bill spiked. The FinOps Foundation’s 2026 survey makes the broader trend clear: AI cost management is becoming routine technology management, not a side project.

For a private model, the bill looks different but it does not go away. You pay for GPUs, power, support, replacement equipment and engineering time. For an API model, you pay by use and might have very different bills depending on the size of documents and conversations.

I would measure three things

First, whether the model is actually good at the task. Second, how it behaves when several people use it at once. Third, what one completed task really costs, whether that is a support ticket, document review or code change.

Then choose the architecture. There is no prize for hosting everything yourself, and there is no prize for spending the most on an API either. The point is to run something useful at a cost the business understands.

Measure the thing people actually care about

I've seen a lot of planning conversations start with token prices or GPU utilization. Those numbers matter, but they don't tell you whether the work got better. If the assistant saves two minutes on a task but creates five minutes of checking, the cheapest model in the world won't make the project worthwhile.

I'd pick a few common requests and track them from beginning to end. Was the result useful? Did somebody have to correct it? Did the system need access to data that wasn't available in the pilot? What happened when the request failed? Those answers influence both the system design and the real cost of operating it.

With APIs, I would pay attention to long context requests, retries and models being used for simple jobs that could run on a smaller option. With private infrastructure, I'd look at how much of the week the equipment is actually busy. A GPU server that stays idle most of the day can be a surprisingly expensive answer to a small problem.

One practical rule: give the first version a budget and a usage dashboard before people love it. Nobody wants to discover the true operating cost only after the department depends on it.