Built by engineers who needed this tool
We ran an LLM-powered product in production for six months without knowing what it was actually doing. Token costs were invisible. Prompt regressions went undetected. Retrieval failures were silent. So we built Spanloom: the observability layer we wished existed.
What we believe about AI observability
Observability tools that hide complexity do more harm than good. We show you what is actually happening, even when that is uncomfortable to see.
We design for people who read stack traces for fun. No marketing dashboards. No inflated metrics. Just clean data and sharp tooling.
OpenTelemetry is the right foundation. Proprietary instrumentation creates lock-in that hurts teams. We build on open standards and give data back in open formats.
Every Spanloom feature started from a real production failure one of us experienced. We do not add features that solve hypothetical problems.
Two lines to instrument. One place to debug. We respect the complexity budget of your codebase and stay out of the way when you do not need us.
We are an early-access product. We move fast, fix issues the same day, and tell users what broke and why. Transparency is a product feature, not a policy.
Three engineers, one obsession
We have worked on distributed systems, developer tooling, and LLM applications. We started Spanloom because the tooling we wanted did not exist.
Previously led platform engineering at a 200-person AI tooling company. Built internal observability pipelines before deciding to turn the approach into a product.
Distributed systems engineer with a background in high-throughput telemetry pipelines. Designed Spanloom's span ingestion architecture from the ground up.
Former ML platform engineer who spent three years debugging why LLM applications misbehaved in production. Shapes everything users see in Spanloom.