Spanlens

Spanlens is an MIT-licensed LLM observability platform that gives you full visibility into every request your app makes to OpenAI, Anthropic, or Gemini. It works as a drop-in replacement for the provider SDK, so you swap one import and start seeing data immediately. No agents to run, no infrastructure to wire up.

It's built for teams shipping LLM-powered products who need to answer real questions fast: why did the bill spike, which agent step is slow, did the new prompt actually improve quality?

Core capabilities:

• Request logging – full body, headers, tokens, cost, and latency per call. Filter, group, and export anytime.
• Cost tracking – per-model and per-route breakdowns, daily rollups, and budget alerts via Slack or webhook before you exceed your limit.
• Agent tracing – multi-step workflows rendered as waterfall span trees with critical path highlighting and per-span cost attribution.
• Anomaly detection – flags 3σ deviations in latency or cost against your 7-day rolling baseline.
• PII and security scanning – regex detectors run at log time on request bodies; API keys are auto-masked before storage.
• Model recommender – identifies calls that could run on a cheaper model and shows projected monthly savings in dollar figures.
• Evals and experiments – LLM-as-judge scores responses 0 to 1 per prompt version; replay a fixed dataset across versions and models to compare quality, cost, and latency side by side before shipping.