Phoenix is an open-source observability platform built for developers to trace steps, run evaluations, and test changes in AI models and workflows.
What it is
Phoenix is an open-source platform designed for developers building applications powered by large language models and autonomous agents. It provides local-first tools for tracing internal operations, running evaluations, managing prompt versions, and debugging failures across complex execution paths.
Key features
- Record step-by-step traces of prompts, retrievals, tool calls, and final outputs.
- Build custom evaluations to score model responses for accuracy and relevance.
- Organize execution logs into structured datasets to run repeatable experiments.
- Use the interactive prompt playground to test and version changes.
- Keep sensitive data within local infrastructure through open-source self-hosting.
- Integrate natively with standard telemetry and existing data science workflows.
Who it's for
Phoenix is built for machine learning engineers, software developers, and data science teams building and scaling production-grade language model workflows.
Pricing
Phoenix is available as an open-source project that you can run locally for free. Managed cloud plans start at $50 per month for teams requiring higher span volumes and extended data retention.
Use cases
- Trace agent execution steps
- Evaluate model response quality
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