LLM Observability: Metrics That Matter in Production

Observe the complete request journey

A model response is only one stage of an AI feature. Production observability should connect the user action, application logic, routing attempts, provider response, and final product outcome under one traceable request identity.

Core operational metrics

Track a small set of consistent measures before adding model-specific detail.

• End-to-end success rate and failure reason.
• Time to first token and total response duration.
• Retry count and fallback route used.
• Input, cached input, and output usage.
• Cost per request and per successful task.

Add quality signals

Operational health does not guarantee a useful answer. Pair system metrics with task-specific evaluations, user feedback, and sampled review. Quality measurements should be segmented by feature and workload so regressions are visible where users experience them.

Make routing observable

Record why a route was eligible, which path was selected, and what happened on every attempt. This evidence makes it possible to distinguish a model issue from provider health, policy configuration, or application-level timeouts.