Glossary entry · FinOps and TBM
The application of FinOps practice to AI spend: making it visible, allocated and efficient, with the same aim of business value that FinOps has for cloud.
It is usually the first discipline to get hold of AI cost, and the one that makes usage transparency possible. Knowing where it stops matters too: billing data does not contain outcome evidence, which is where AI value management picks up.
The FinOps Foundation defines FinOps as a practice for maximising the business value of cloud and technology, so FinOps for AI already aims at value, not only at lower bills. This site treats AI value management as the next step after FinOps rather than a rival to it: it adds the outcome evidence that billing data does not contain.
For how the practice applies to AI workloads, see FinOps and AI and the AI Economics Map.
Put it to work
Excel template. An outline model for reporting or charging AI cost to teams: cost pools, allocation drivers, the monthly split, an unallocated line and a readiness test for moving to chargeback.
Browser tool. Build a monthly inference estimate from the rate card up, adding output ratio, caching, uplifts and fees. Runs in your browser; nothing is sent anywhere.
