Cloud FinOps gave engineering leaders a way to allocate, forecast, and optimize cloud spend. AI FinOps does the same for the new line item that just landed on every CFO's desk — API calls, IDE seats, agent runs, and the workflows that string them together.
Most teams we meet are at Level 0 or 1. Reaching Level 3 unlocks the first board conversation. Level 4 is where the bleeding stops.
"You found out about a $40K Anthropic bill from an email forward."
"You can answer 'what do we spend on AI?' in under 60 seconds."
"Engineering managers see their team's AI cost in their weekly metrics."
"You bring an AI-spend forecast to the next quarterly board update."
"A runaway agent gets blocked at the gateway, not at the invoice."
"AI gross margin per product is a KPI in the company scorecard."
You can't cut a number you can't attribute. Every dollar maps to a team, product, or workflow before any cost work begins.
Soft alerts are too late. Hard caps and virtual keys move enforcement to the request path, where overspend can actually be stopped.
Routing a workflow from GPT-4o to a mini-tier model saves 80% — only if quality holds. Continuous evals are the safety net that lets cost decisions ship.
Total spend is a vanity metric. AI cost per active user, per support ticket, per signed PR — that's the language a CFO will fund.
An anonymized, k-anonymous benchmark of what real engineering teams spend on AI — by provider, by model tier, by team size. So you can stop wondering whether your numbers are normal.
Aggregated only when a cohort has at least 5 tenants. No per-customer detail, ever.
Paste a read-only billing key. We'll show you the L0 → L5 picture for your stack in under 5 minutes.