Phoenix Observability

Phoenix - AI Observability Platform

Open-source AI observability and evaluation platform for LLM applications with tracing, evaluation, datasets, experiments, and real-time monitoring.

When to use Phoenix

Use Phoenix when:

• Debugging LLM application issues with detailed traces
• Running systematic evaluations on datasets
• Monitoring production LLM systems in real-time
• Building experiment pipelines for prompt/model comparison
• Self-hosted observability without vendor lock-in

Key features:

• Tracing: OpenTelemetry-based trace collection for any LLM framework
• Evaluation: LLM-as-judge evaluators for quality assessment
• Datasets: Versioned test sets for regression testing
• Experiments: Compare prompts, models, and configurations
• Playground: Interactive prompt testing with multiple models
• Open-source: Self-hosted with PostgreSQL or SQLite

Use alternatives instead:

• LangSmith: Managed platform with LangChain-first integration
• Weights & Biases: Deep learning experiment tracking focus
• Arize Cloud: Managed Phoenix with enterprise features
• MLflow: General ML lifecycle, model registry focus

Quick start