What This Stack Does
Monitoring AI applications requires specialized tooling that traditional APM cannot provide. This stack combines five tools that together cover every observability need for AI systems. Langfuse provides the foundational LLM tracing and prompt management platform with open-source flexibility and a generous free tier. OpenLLMetry extends OpenTelemetry with AI-specific instrumentations, letting you send LLM traces to any existing OTel-compatible backend like Datadog or Grafana.
Drift Detection and Evaluation
Evidently AI covers the traditional ML monitoring side — data drift detection, model performance tracking, and data quality checks — alongside newer LLM evaluation capabilities with 100+ built-in metrics. Braintrust adds a focused evaluation and prompt optimization layer with logging, scoring, and dataset management designed specifically for iterating on LLM quality. DeepEval provides a testing framework for LLM outputs with metrics for hallucination, faithfulness, and answer relevancy.
The Bottom Line
Start with Langfuse for tracing and OpenLLMetry for OTel integration. Add Evidently for data-level monitoring and drift detection. Layer Braintrust and DeepEval when you need structured evaluation and testing workflows. The full stack provides visibility at every layer: infrastructure metrics via OTel, LLM call traces via Langfuse, output quality via DeepEval, and data health via Evidently.