Full-fidelity traces of every LLM call and tool step. Token, latency, and cost on each. Replay traces, run evals against datasets, ship to your warehouse. LangSmith-grade observability, billed per trace.
LangSmith-grade observability. Full traces, token + latency + cost, replay, eval.
Each LLM call, each retry, each tool step is captured. See the full agent run, not just the final response. Tokens, latency, cost on every step.
Filter by user, model, route, status. Pin a slow request. Replay with a different model or prompt. Share a permalink with your team.
Build datasets from production traces. Run evals on every commit. Compare runs side-by-side. Catch quality regressions before the user does.
Datadog for spend, BigQuery for cost views, a CFO spreadsheet for the receipts — it's three tools doing what should be one log table.
We watched five AI startups in a row hand-roll the same logs stack — and burn three weeks doing it. We packaged ours so you don't have to. Drop the SDK in once; this product, plus the rest of the suite, comes with it.
Filter, pin, and replay any trace. Compare runs side-by-side. Find the one slow tool call buried in a 12-step agent run.
Per-user, per-model, per-route. Find your most expensive user in 2 seconds. Find your slowest provider. Reroute.
Build datasets from real traces. Run evals on every commit. Catch regressions before they ship.
Webhook to Snowflake, BigQuery, S3. Stable schema, hash-chained, audit-ready.
Every call you make through the SDK is logged.
logs.search({ user, model, ... })Webhook URL or scheduled S3 dump
Same schema, same primary keys
First time we've had real numbers on AI cost per user. It changed the pricing conversation in the same week.
— Tom · COO, Loomstackper request, after free tier. No markup on tokens. Cancel anytime.
We onboard 1–2 indie startups a week. If you'd rather ship features than maintain a logs stack, talk to us.