What a real AI cost-governance ledger looks like
Cost governance for AI usually gets pitched as a future dashboard. Here's a live one, running on our own infrastructure, with the actual numbers.
The number that matters isn't the total spend
Our own routing gateway has logged 17 model calls in its current window, spending $0.000958 against a $30 cap. The interesting number isn't the total — it's that only 2 of those 17 calls ever touched a paid backend. 88% settled at $0 on a local or free tier before any billed credit was at risk, by policy, not by luck.
Why this requires routing, not just tracking
Cost governance tools that only measure spend after the fact tell you what happened. A router that decides, per call, whether a free local model, Google's free tier, or a paid OpenRouter backend actually serves the request changes what happens — the $30 monthly cap is a policy enforced before the call, with a per-request ceiling checked independently, not a number a dashboard reports after the bill arrives.
What gets logged, and why it's unsampled
Every call records what model was requested versus what actually served it, tokens in and out, and cost — every call, not a statistical sample. That distinction matters for the same reason it matters in the governance frameworks this maps to (NIST AI RMF MANAGE-1.3, MAP, and MEASURE functions, see the framework mapping below): a sampled log can miss the one call that mattered. An unsampled one can't.
See it built, not just described
AI Cost & Model Governance Assessment is the engagement this pattern comes from.
AI Cost & Model Governance Assessment →