What it does
Open-source LLM observability for monitoring, evaluation, experimentation, latency, requests, and usage behavior.
Why it belongs here
A clean feedback loop for where tokens are going, which calls are slow, and which experiments are worth keeping.
Best use case
Teams that need request logs, cost visibility, latency monitoring, experiments, and simple observability around LLM apps.
How to use it
Proxy or instrument calls, add request metadata, and use cost and latency views to find expensive workflows and bad experiments.
Limits
It helps identify problems, but cost fixes still come from routing, prompt changes, caching, and workflow design.
Tags
observabilityexperimentsusage
