Track every AI request with team_id, user_id, model, token counts, and feature context, or your invoice will stay unexplainable.
Build a request-level cost ledger first, then roll it up into team, user, feature, and model views.
Most LLM spend spikes come from a small set of causes: model switches, prompt bloat, retry storms, and unbounded feature adoption.
The fastest useful audit is not perfect chargeback. It is a weekly process that shows who spent what, why it changed, and what action to take next.
When an LLM bill jumps from $9,000 to $17,500 in one month, most teams start in the wrong place. They open the provider invoice, sort by model, and try to reason backward. That tells you what was billed, but not which team shipped the change, which user pattern drove it, or whether the increase came from a healthy launch or a bug.
