Phoenix

license

Elastic-License-2.0(ELv2, non-OSI: prohibits offering as a managed/hosted service to third parties)

source

public

commercial

Arize-AX-SaaS-is-separate-closed-product

Phoenix's LICENSE file is the Elastic License 2.0 verbatim, not an OSI license, even though Arize markets Phoenix on its site as "the open source platform." The source is published and self-hostable, but Arize AX, a separate closed SaaS product, is sold beside it, which keeps the reading at source-available rather than open source.

• https://arize.com/docs/phoenix/self-hosting/license recorded 2026-09-29

  License page: Phoenix is released under the "Elastic License 2.0 (ELv2)" by Arize AI; "Self-hosting on your own infrastructure or in your cloud account is free and fully permitted." and "There are no feature gates — Phoenix is a fully open-source platform."

• https://arize.com/phoenix/ recorded 2026-09-29

  Vendor page: "ELv2 licensed. 10k+ GitHub stars. Built in the open with contributions from teams building production agents." and a separate Arize AX product offered as a "Managed AI engineering platform" beside the self-hostable Phoenix.

• https://github.com/Arize-ai/phoenix/blob/main/LICENSE recorded 2026-08-13

  Elastic License 2.0 (ELv2) full text, read rather than assumed. It opens "Elastic License 2.0 (ELv2)" and its Limitations state "You may not provide the software to third parties as a hosted or managed service, where the service provides users with access to any substantial set of the features or functionality of the software." That is the non-OSI restriction the record scores on.

• https://pypi.org/pypi/arize-phoenix/json recorded 2026-09-29

  PyPI metadata for arize-phoenix: license field "Elastic-2.0" and a Source project URL of "https://github.com/Arize-ai/phoenix", so the source is published in a public repository.

Adoption is measured by monthly PyPI downloads of arize-phoenix. Broad OpenTelemetry-based framework integrations and the project's GitHub stars corroborate a wide install base without adding to the reading.

• https://github.com/Arize-ai/phoenix recorded 2026-08-13

  11,033 stars; OTel-based tracing, evaluation, playground and prompt management in the README

• https://pypistats.org/api/packages/arize-phoenix/recent recorded 2026-10-07

  626,210 downloads in the trailing 30 days for arize-phoenix (pypistats last_month)

Phoenix is a full-coverage observability and evaluation platform: tracing, LLM-as-judge evaluation of both responses and retrieval, datasets and experiments, prompt management and a playground, cost and latency tracking, and broad framework integrations. That covers every dimension this category grades and puts it level with MLflow and Langfuse.

• https://arize.com/docs/phoenix recorded 2026-09-29

  Docs home: a "Phoenix Prompt Playground" figure and a card "Measure quality with LLM-as-a-judge and custom evaluators".

• https://arize.com/docs/phoenix/datasets-and-experiments/overview-datasets recorded 2026-09-29

  Datasets and experiments overview: "Experiments and evaluations solve this, helping distill the indeterminism of LLMs into tangible feedback that helps you ship more reliable product." and "Understand whether an update is an improvement or a regression".

• https://arize.com/docs/phoenix/evaluation/llm-evals recorded 2026-09-29

  Evaluation docs: "Python and TypeScript SDKs for running evaluations against Phoenix traces, datasets, or any data source." and evaluators include code-based checks and "LLM-as-a-judge evaluators".

• https://arize.com/docs/phoenix/prompt-engineering/overview-prompts recorded 2026-09-29

  Prompt docs: "Prompt management allows you to create, store, and modify prompts for interacting with LLMs." and a "Prompt Playground" section.

• https://arize.com/docs/phoenix/tracing/llm-traces recorded 2026-09-29

  Tracing docs: "Phoenix traces AI applications, via OpenTelemetry and has first-class integrations with LlamaIndex, LangChain, OpenAI, and others." plus cards for "Monitor latency, token usage, and error rates" and "Score your traces with LLM-as-a-judge evaluators".

• https://github.com/Arize-ai/phoenix recorded 2026-08-13

  README feature list - Tracing "using OpenTelemetry-based instrumentation", Evaluation with "response and retrieval evals", Playground and Prompt Management, plus the arize-phoenix-evals and arize-phoenix-otel packages.