See every span.
Catch every drift.
Full trace visibility into your LLM applications. Track every request, monitor token costs, detect prompt drift, and debug production pipelines before users notice.
Everything your AI pipeline needs
Purpose-built telemetry for LLM applications. Spanloom instruments your calls, chains, and evaluations so you always know what your model is doing in production.
Capture every LLM call, retrieval step, and tool invocation as a structured span. Build full traces from input to output across your entire pipeline.
See per-request token usage and cost as it happens. Break down spend by model, endpoint, and user segment before the invoice arrives.
Track prompt versions over time. Alert when live prompt hashes diverge from your deployed baseline so silent regressions surface immediately.
Inspect which chunks were retrieved, their relevance scores, and whether they contributed to the final answer. Debug retrieval failures fast.
Understand where time goes in each request. Waterfall view shows each sub-step's contribution so you can optimize the slowest segments first.
Set thresholds on latency, error rate, token spend, or custom metrics. Get notified via webhook or email when your pipeline drifts out of spec.
Instrument in minutes, not days
Spanloom wraps your existing LLM calls with two lines of Python or TypeScript. No infrastructure to manage, no schema to define.
One pip or npm install. Spanloom works alongside your existing OpenAI, Anthropic, or LangChain setup with zero changes to business logic.
Wrap your LLM clients or pipeline functions with our span decorators. Every call starts emitting trace data to your Spanloom workspace immediately.
Open the Spanloom dashboard and see your first traces within seconds. Drill into any request, filter by model or user, and set your first alert.
From early-access pilot teams
Engineering teams running LLM apps in production share what changed after adding Spanloom to their stack.
We were flying blind on token costs. Spanloom showed us a single endpoint was burning 40% of our monthly budget on duplicate context. Fixed it the same day.
Prompt drift was invisible until we saw two weeks of A/B results diverge. Spanloom's version tracking caught the culprit in 10 minutes. That was the convincing moment.
The retrieval audit view is the thing I didn't know I needed. We could finally see which documents were actually influencing answers versus just eating tokens.
Works with your existing stack
Spanloom integrates with the LLM frameworks and APIs engineering teams already use. No migration required.