Arize Phoenix
6.3#10 in machine learning model monitoring softwareFree planFree planWebAPISelf-hosted
VisitArize Phoenix vs Evidently AI
Evidently AI
8.3#2 in machine learning model monitoring softwareFrom $80 a monthFree planWebAPILinuxmacOS
VisitWhat both publish
| Fact | Arize Phoenix | Evidently AI |
|---|---|---|
| Integrations | Documented integrations include frameworks and providers such as LlamaIndex, LangChain, DSPy, OpenAI, Bedrock, and Anthropic. | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. |
| Purpose | Phoenix helps teams trace, evaluate, debug, and improve AI applications. | Evidently evaluates, tests, and monitors LLMs, RAG applications, AI agents, and machine-learning models. |
| Support | The Phoenix documentation points users to a Slack community for questions and developer discussion. | Community support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact. |
| Tracing | It captures model calls, retrieval, tool use, and custom logic in traces that can be sent over OpenTelemetry. | Open-source tracing records inputs, outputs, intermediate events and function calls, and allows traces to be exported as tabular datasets. |
Arize Phoenix plans
- PhoenixFree
Evidently AI plans
- DeveloperFree
- Open-sourceFree
- Pro$80 a month
- EnterprisePriced on request
- StartupsPriced on request
Pick Arize Phoenix if…
Phoenix suits teams building AI applications who need to inspect traces, evaluate outputs, and compare application versions. It offers local and self-hosted deployment for teams managing their own trace data.
Pick Evidently AI if…
It suits teams evaluating or monitoring AI applications and predictive ML models, including those that want open-source tooling or a hosted platform. Its synthetic-data and red-team capabilities may also suit teams assessing adversarial inputs and risks.