AI features are the first workload many teams have run where unit cost varies with input. A single verbose customer can cost fifty times the median case.
We instrument cost per run from the first release and attribute it per feature and per tenant. It is a one-day job at the start and a quarter-long forensic exercise later.
Cost visibility changes design decisions: caching becomes obvious, model routing becomes justified, and the temptation to send everything to the largest model disappears.
Accuracy, latency and cost belong on one dashboard. Optimising any one of them in isolation produces a system nobody wants to run.
If you cannot measure the change, you cannot defend the system that caused it.
If this is the kind of problem you are working on, we are usually happy to compare notes — even when it does not become an engagement.
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