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LLM Observability & Monitoring: Traces, Cost and Latency SLOs, Eval Harnesses, and Alerting
Traditional APM misses token streams, tool loops, and judge drift. This guide covers OpenTelemetry-style traces for LLM chains, LangSmith / Phoenix-style eval sessions, cost attribution per tenant, latency SLOs by model tier, golden-set regression, and production alerts for schema drift in structured outputs.
Why LLM Production Needs a Different Observability Stack
Classic metrics — request rate, error code, p99 latency — still matter, but LLM calls are internally multi-step: retrieval, prompt render, model completion, tool invocation, second completion. A single HTTP 200 can hide partial failures (empty retrieval, wrong JSON shape, silent tool timeout).
Observability for GenAI means trace spans per chain step, token and cost accounting, output schema validation, and offline eval hooks tied to the same trace_id as production sessions for regression triage.