incident.io controls AI spending with per-prompt cost attribution and OpenAI project billing limits
incident.io hit OpenAI billing limits and temporarily broke all early-access AI features. As they expanded adoption — particularly Investigations, which costs 100x more than their other AI features — they lacked visibility into which features and code paths were driving spend.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Per-prompt token attribution
Token usage is attributed to a named prompt type via Go reflection and piped into observability tooling.
incident.io now has control over production, development, and training AI costs, with per-prompt attribution, feature-level billing limits, daily Slack cost reports, and predictive backfill cost estimation, enabling teams to ship AI features confidently.
What failed first
Billing limits were breached and all early-access AI features broke. Without per-prompt tracking, the team had no way to attribute spend to features or code paths, and discovered weeks later they had been burning hundreds of dollars on bugs.