Langfuse
LLM observability & tracing
1 case studies
1 specialists
1 specialists
Data Dev Framework LLM observability & tracing
Langfuse is used to trace, monitor, and debug LLM applications in production by capturing every prompt, completion, latency, cost, and error across your pipeline. It provides dashboards to track quality over time, annotation tools for human evaluation, and dataset management for systematic testing of prompts.
Sign up at cloud.langfuse.com (or self-host) and create a project to get your public and secret API keys. Install with `pip install langfuse` and set LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY as environment variables. Use the `@observe()` decorator on your functions or the LangChain callback handler for automatic tracing.
$ pip install langfuse` and set LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY as environment variables Case studies
Series B AI startup, 6 production LLM pipelines
An AI startup's inference costs grew 40% in a month with no engineering changes. The culprit was hidden somewhere in 6 interconnected LLM pipelines — impossible to debug without request-level tracing.
Instrumented all 6 pipelines with Langfuse traces, capturing prompt/completion tokens, latency, model version, and user context per request. Built cost dashboards by pipeline, model, and user segment.
Identified an inefficient system prompt in the document summarization pipeline generating 3x more tokens than necessary. Fix took 2 hours. Monthly inference cost reduced from $8.1k to $5.8k — $2.3k/month savings.
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