What is Arize Phoenix used for?

Arize Phoenix is an open-source AI observability platform from Arize for tracing, evaluating and troubleshooting LLM applications and agents. It is a good fit for debugging retrieval-augmented generation pipelines and benchmarking prompt changes before release.

Is Arize Phoenix open source?

Yes. Arize Phoenix is open source under a custom licence. Its source code is on GitHub at Arize-ai/phoenix and is written mainly in Python.

Is Arize Phoenix free?

Yes. Arize Phoenix is open source, so the software itself is free to use under the terms of its own licence. A managed cloud version is also available.

Can I self-host Arize Phoenix?

Yes. Arize Phoenix can be self-hosted on your own server or infrastructure.

What is Arize Phoenix an alternative to?

Arize Phoenix is an open-source alternative to Weights & Biases, LangSmith, Arize AI and Braintrust. Other open-source alternatives to Weights & Biases include Opik, Langfuse and Laminar.

Is Arize Phoenix actively maintained?

Yes. The most recent commit to Arize Phoenix was on 2 October 2026. The project has 12k stars on GitHub.

Arize Phoenix: open-source alternative to Weights & Biases

About Arize Phoenix

Arize Phoenix is an open-source AI observability platform for experimentation, evaluation and troubleshooting. It helps teams building applications on large language models understand what their systems are doing at runtime, measure quality and debug problems before and after release.

Tracing relies on OpenTelemetry-based instrumentation to record an LLM application's runtime behavior. Evaluation uses LLMs to benchmark an application's performance with response and retrieval evals, and datasets can be versioned. The topics point to integrations with OpenAI, Anthropic, LangChain, LlamaIndex and smolagents, as well as work on agents and prompt engineering.

Phoenix is written in Python and published under a license listed in the repository. For managed production workflows, Arize also offers a separate product called Arize AX. The README is available in English and Simplified Chinese, and the project documentation is hosted on the Arize site.

Key features

OpenTelemetry-based tracing for LLM apps
LLM-assisted response and retrieval evals
Versioned datasets for experiments
Integrations with common LLM frameworks
Support for agent workflows

Good fit for

→Debugging retrieval-augmented generation pipelines
→Benchmarking prompt changes before release

Built with: Python
Tags: llm-observability; tracing; evals; opentelemetry; ai-monitoring; llmops; python; agents

Arize Phoenix: questions and answers

What is Arize Phoenix used for?: Arize Phoenix is an open-source AI observability platform from Arize for tracing, evaluating and troubleshooting LLM applications and agents. It is a good fit for debugging retrieval-augmented generation pipelines and benchmarking prompt changes before release.
Is Arize Phoenix open source?: Yes. Arize Phoenix is open source under a custom licence. Its source code is on GitHub at Arize-ai/phoenix and is written mainly in Python.
Is Arize Phoenix free?: Yes. Arize Phoenix is open source, so the software itself is free to use under the terms of its own licence. A managed cloud version is also available.
Can I self-host Arize Phoenix?: Yes. Arize Phoenix can be self-hosted on your own server or infrastructure.
What is Arize Phoenix an alternative to?: Arize Phoenix is an open-source alternative to Weights & Biases, LangSmith, Arize AI and Braintrust. Other open-source alternatives to Weights & Biases include Opik, Langfuse and Laminar.
Is Arize Phoenix actively maintained?: Yes. The most recent commit to Arize Phoenix was on 2 October 2026. The project has 12k stars on GitHub.