OpenAI's usage surfaces can explain organization and project activity, while application operators often need a different view: which user-facing workload made a request, how long it took, whether it failed, what it cost and which trace or agent run it belonged to.
Key takeaways
- Keep request identity beside provider usage evidence.
- Measure tail latency and failures, not only averages and totals.
- Separate provider billing reconciliation from application-level operational attribution.
Provider usage is necessary but not full application observability
OpenAI documents organization usage dashboards, token usage in API responses and APIs for usage and cost analysis. Those surfaces are valuable for provider-level reporting. Application observability adds workload, release, trace and incident context that exists outside the provider account.
Capture the request dimensions operators investigate
Record application or agent identity, model, status, latency and available token usage for every production request. For streaming workflows, preserve the timing signals your product actually experiences rather than relying only on total duration.
Connect agent and retry behavior
One user-visible operation can create multiple model calls, subagent work or retries. OpenAI's current observability guidance explicitly notes that agent cost can include root and subagent calls plus retries. Group those child operations under a stable run or trace identity.
Treat cost as part of observability
A slow or failing workflow can also become an expensive workflow. Keep estimated cost close to latency, status and retry evidence so engineering can distinguish healthy product growth from an efficiency or reliability regression.
FAQ
Common questions
What should I monitor for OpenAI API requests?
Useful production dimensions include workload identity, model, status, latency, token usage, estimated cost and trace or agent-run context.
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