Lesson 896 of 1596
AI Observability Stack 2026: Traces, Metrics, and Cost in One Pane
Building a unified view across LangSmith, Datadog LLM Observability, OpenTelemetry, and custom dashboards.
Creators · Tools Literacy · ~18 min read
The premise
AI observability is logs + traces + cost + quality — one of the four is always missing in vendor pitches.
What AI does well here
- Capture full prompt/response with PII scrubbing at ingest
- Tag every call with user, route, prompt version, and model
- Correlate cost and latency to user-visible outcomes
- Alert on quality regressions, not just error rates
What AI cannot do
- Replace a real eval suite for quality monitoring
- Surface novel failure modes without sample-based human review
- Hide the bill for storing every prompt forever — define retention
Key terms in this lesson
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