Observabilité LLM
Tracking what your AI app actually does in production: every model call, its cost, its latency, and above all the answer quality. An LLM app can fail without anything breaking: the server is green, the API is fast, and the chatbot still returns a wrong answer. Classic infra monitoring sees none of it. LLM observability captures the full trace (prompt, response, agent steps) so you can debug after the fact. Two words people mix up: a log records isolated events from your application, a trace follows one request end to end, across model calls, tools, retrieval steps and agents. The trace is what tells you where an answer went off the rails.
Strengths
- Replays the exact trace (prompt, response, agent steps) that produced a bug
- Measures cost and latency per call, not just in aggregate
- On an agent, exposes the success rate and the step sequence taken: that is what shows a loop, which no average reveals
- Serves as the base to wire evals and catch quality regressions
Limitations
- One more tool to instrument and maintain in the stack
- Traces contain user data: mind the retention scope
Best for
- An AI app or agent going to production in front of real users
- A team that wants to stop debugging hallucinations by gut feel