opsmeter

Usage-based AI needs the same safety engineering as any “expensive actuator”: rate limits, quotas, and automatic shutdown thresholds. Otherwise a leaked key becomes an unbounded liability.

This reads like an “incident without guardrails”: per-project caps/quotas, anomaly alerts (minutes), env-split keys, and an automated kill-switch should be defaults for usage-based APIs. Billing emails are post-mortems.

Nice — those two features tend to unlock the “why” behind drift. One thing we found especially useful was pairing cost/outcome alerts with a root-cause slice: when slope jumps, immediately show top contributing endpoint/feature + tenant/user + prompt version changes + retry ratio/context size trend. For your event_id model: how do you handle partial outcomes (e.g., success after fallback/escalation) and do you keep pricing snapshots by timestamp so historical cost/outcome comparisons stay consistent across model price changes?