A step-by-step guide to setting up LLM observability using self-hosted Langfuse and vLLM. Covers why LLM apps need traces beyond logs and metrics, how to deploy Langfuse Server, Langfuse Worker, PostgreSQL, and vLLM via Docker Compose, and how to instrument Python LLM pipelines using the Langfuse @observe decorator. Demonstrates capturing prompts, outputs, token usage, latency, and nested trace hierarchies in a local dashboard — transforming a blind LLM script into a fully observable workflow.
Table of contents
LLM Observability with Self-Hosted Langfuse and vLLMIntroduction to LLM Observability with LangfuseHow Langfuse Fits into an LLM Observability StackLangfuse Architecture for LLM ObservabilityWhy Understanding LLM Observability Architecture MattersSetting Up a Self-Hosted Langfuse and vLLM StackBaseline LLM Application (Before Observability)Adding LLM Observability with the Langfuse @observe DecoratorRunning and Verifying a Self-Hosted Langfuse Observability StackSummarySort: