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Observability for AI Features: Logs, Traces, and Cost

August 22, 2026
Observability for AI Features: Logs, Traces, and Cost

AI Without Metrics Is Guesswork

If you cannot see prompt latency, token spend, and error rates, you cannot operate an AI feature. Treat model calls like any other dependency.

What to Log

Request IDs, model name, token counts, cache hits, retrieval scores, and user-visible failures. Redact PII before logs leave the trust boundary.

Budgets and Alerts

Set per-feature cost ceilings and p95 latency SLOs. Alert when spend spikes or when fallback rates climb — that is often a silent UX regression.

Feedback Loops

Capture thumbs-up/down and sampled transcripts into an evaluation dataset. Continuous improvement requires continuous measurement.

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