Originally published at mlopslab.org/llm-observability — updated weekly. 0 sponsors, 0 affiliate links.

⚡ Quick answer: LLM observability is the practice of collecting metrics, traces, and logs from large language model applications to monitor behavior, catch failures, control costs, and improve output quality — in real time. Unlike traditional APM, it handles non-deterministic outputs, prompt/response pairs, token costs, hallucination rates, and multi-step agent chains that standard monitoring tools were never built for.

Table of Contents

LLM observability: the actual definition

Why traditional APM fails for LLMs

Why it matters in 2026

The three pillars: metrics, traces, logs

Key LLM observability metrics

Best LLM observability tools (2026)

How to implement it in Python — step by step

RAG observability: what's different

Common mistakes to avoid

FAQ

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