Observability

Concept

LLM Observability in Production

You can't debug what you can't see. LLM observability means tracing every prompt, token, latency spike, and failure — here's the production stack.

Tool Guide

Langfuse Guide

The definitive guide to Langfuse for LLM observability — traces, prompt versions, evals, cost tracking, self-host setup, and when to pick it over Braintrust or Phoenix.

Comparison

Langfuse vs Braintrust vs Phoenix

Three observability options for production LLM apps — open-source tracing, eval-first SaaS, and RAG-focused OSS from Arize.

Directory

Braintrust

AI evaluation and observability platform — traces feed directly into eval datasets, regression tests, and prompt experiments.

Directory

Langfuse

Open-source LLM observability — traces, evals, prompt versioning, and cost dashboards for production AI apps.

Directory

Phoenix (Arize)

Open-source LLM tracing and evaluation from Arize — inspect spans, embeddings, and retrieval quality locally.