Phoenix
Phoenix is an open source AI observability and evaluation platform built on OpenTelemetry. Features LLM tracing, prompt playground, evaluation workflows, dataset experiments, and clustering analysis for improving AI quality.
About Phoenix
Phoenix is an open source AI observability and evaluation platform developed by Arize AI. Built on OpenTelemetry and powered by OpenInference instrumentation, it provides a vendor agnostic, self hostable solution for understanding and improving AI applications. The platform is completely open source with no feature gates or restrictions.
Phoenix enables teams to inspect AI application runs through detailed tracing, diagnose issues using evaluation workflows, and improve quality via dataset experiments and human annotations. It supports both development time debugging and production monitoring with real time observability.
The platform includes an interactive Prompt Playground for iterating on prompts with real production examples, streamlined evaluation workflows with pre-built templates, and advanced analysis capabilities including dataset clustering and visualization to identify failure patterns.
With 2.5M+ monthly downloads, 8k+ GitHub stars, and 2.4M+ OpenTelemetry instrumentation downloads, Phoenix has strong community adoption. It supports Python and TypeScript/JavaScript auto-instrumentation and integrates with major frameworks including LlamaIndex, LangChain, DSPy, Mastra, and Vercel AI SDK.
Key Features
- Open Source & Self Hostable: Fully open source (no feature gates) with complete self hosting capabilities.
- OpenTelemetry Foundation: Built on OTEL for standardized, vendor agnostic telemetry collection.
- OpenInference Instrumentation: Powered by OpenInference for consistent AI/LLM signal capture.
- Interactive Prompt Playground: Experiment with prompts and models using real production examples.
- Evaluation Workflows: Pre-built eval templates with customization for various AI tasks.
- Dataset Experiments: Systematically test changes by running datasets through different app versions.
- Clustering & Visualization: Uncover patterns in failures using embedding based clustering analysis.
Pricing
Phoenix is completely free and open source. No paid tiers, usage limits, or feature restrictions. Arize AI offers separate commercial solutions, but Phoenix itself is fully open source and self hostable.
Pricing last updated: February 22, 2026 at 9:54 AM
Use Cases
- Trace and debug LLM application runs across frameworks and model providers
- Evaluate LLM outputs and agent workflows with reusable templates
- Iterate on prompts using real production examples in the Playground
- Run systematic experiments comparing different app versions on same inputs
- Identify systemic failure clusters using visualization and clustering tools
- Self host observability infrastructure for data privacy compliance
Pros & Cons
Pros:
- Fully open source and self hostable without any feature gates
- Strong vendor agnosticism via OpenTelemetry/OpenInference standards
- Combines observability, evaluation, and prompt iteration in one platform
- Active community with 2.5M+ monthly downloads and 8k+ GitHub stars
- Supports both Python and TypeScript/JavaScript ecosystems
- No usage limits or paid tiers for core functionality
Cons:
- Self hosting requires infrastructure management and maintenance
- No managed cloud option available for Phoenix specifically (separate Arize products exist)
- Enterprise features like advanced RBAC require custom implementation
- Smaller ecosystem compared to commercial alternatives like LangSmith
Integrations
OpenTelemetry, OpenInference, LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK, OpenAI, Anthropic, AWS Bedrock
FAQ
Last edited
February 22, 2026 at 9:54 AM by Venkatraman
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