LLM Observability & Evaluation Platform

A design for making production LLM features observable: per-call traces, cost and latency accounting, and evaluation suites that gate prompt and model changes.

LLM features fail in ways ordinary monitoring does not see: a response can be fast, cheap and wrong. Teams need to know which prompt and model version produced an answer, what it cost, and whether quality moved after a change.

Applications emit spans for every model call — prompt template, model version, retrieved context, latency and token counts — into an event stream; workers aggregate them into a trace store and time series. An evaluation service replays fixed datasets against candidate prompts or models and compares scores before rollout.

Rule-based checks and model-graded evaluations over curated datasets; clustering of failed traces to surface recurring failure modes.

Design objective: every production answer traceable to its prompt, model and context; cost and latency visible per feature; and no prompt or model change released without an evaluation comparison.