Curated List
Compare the top observability and monitoring tools for LLM applications. Trace, evaluate, and debug your AI systems in production.
OpenTelemetry-compatible tracing standard for AI/LLM applications
OpenInference is an open standard for capturing AI model observability data using OpenTelemetry. It enables vendor-neutral tracing of LLM calls, embeddings, and retrieval operations across any observability backend.
Open-source LLM observability, tracing, and evaluation platform
Langfuse is an open-source platform for LLM application observability. It provides traces, spans, scores, and evals for debugging and monitoring LLM pipelines, with SDKs for Python and JavaScript and integrations with LangChain, LlamaIndex, and more.
Open-source MLOps platform for the full ML lifecycle
MLflow is an open-source platform for managing the complete machine learning lifecycle including experiment tracking, model packaging, model registry, and deployment. Widely adopted across data science and ML engineering teams.
Open-source AI observability and evaluation for LLMs and ML models
Phoenix by Arize is an open-source AI observability platform that provides real-time tracing, evaluation datasets, and retrieval analysis for LLM and ML applications. Runs locally or on cloud infrastructure.
