Langfuse is a free, open-source LLM observability platform that helps engineers debug and understand their AI applications in production by recording model calls, agent steps, and tool use as structured traces. It can be self-hosted for free with no limits or used via a managed cloud service, offering the same SDKs and UI.
The author developed Torrix, a self-hosted LLM observability platform that logs calls, calculates costs, and flags anomalies automatically, to solve issues like surprise bills and silent model failures. A live demo with 30 days of simulated LLM traces across three projects was created to facilitate experimentation.
The current state of LLM observability tools is flawed, focusing solely on input metrics like requests and costs without measuring actual output or return on investment. This deficiency leads AI product teams to make expensive architectural decisions and struggle to identify which customers or agents are driving budget spikes.