Tools
Arize Phoenix vs MLflow
Arize Phoenix or MLflow? Their plans monthly and yearly, the capabilities their makers state, security and latest updates, side by side — read from the makers' own pages.
In short
- Both: Command line, Self-hosted, API, Traces and evaluates
- Only MLflow states: Official SDKs, Builds agents and workflows
Arize Phoenix
Open-source platform for tracing, evaluating, and improving AI agents and LLM applications.
Plans
AX Free
Free
- 10 issues per month
- 25k spans per month
- 1 GB per month
- 15 days retention
- SaaS deployment
- Unlimited users and evals
AX Pro
- $50 per month
- 25 issues per month
- 50k spans per month
- 10 GB per month
- 30 days retention
- SaaS deployment
- Unlimited users and evals
AX Enterprise
Price on request
- Unlimited issues
- Custom span volume
- Custom ingestion volume
- Custom retention
- SaaS or Self-Hosted deployment
- Unlimited users and evals
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Command line — “Get Started Locally $ uvx arize-phoenix serve” source
- Self-hosted — “Self-host Phoenix and keep sensitive data on your own infrastructure.” source
- API — “Docs Quickstarts, API reference, and integration guides” source
- Traces and evaluates — “Phoenix shows every step your agent takes so you know what went wrong.” source
Security
- SOC 2 Type II — “Arize AI Announces SOC 2 Type II Certification” source
- SOC 2 — “Arize AI Announces SOC 2 Type II Certification” source
- ISO 27001 — “ISO/IEC 27001 Certified” source
- GDPR — “GDPR Compliant” source
- HIPAA — “HIPAA Compliant” source
- Data kept in the EU — “Arize AI: Support for EU Data Residency” source
Latest updates
- Get Alyx help tailored to your experience
Alyx adapts how it helps based on your experience level in each part of Arize AX.
- Pick up where you left off with Alyx long-term memory
Alyx remembers context across conversations, including preferences, goals, project and asset learnings, team conventions, and decisions.
- Add and annotate entire sessions and traces in annotation queues
Send sessions and traces to an annotation queue and review them at session or trace granularity.
MLflow
MLflow is an open-source platform for tracking, evaluating, deploying, and monitoring machine learning models, LLM applications, and agents.
Plans
Not read from the maker’s page yet.
Prices checked 2026-09-25 on the maker’s page.
Capabilities
- Command line — “The easiest way to start MLflow server is to run the mlflow CLI command in your terminal.” source
- Self-hosted — “Thousands of users and organizations run their own MLflow instances to meet their specific needs.” source
- API — “This page hosts the API documentation for MLflow.” source
- Official SDKs — “Python API” source
- Builds agents and workflows — “Hands-on guides and code examples for building Agents and LLM applications with MLflow.” source
- Traces and evaluates — “Capture complete traces of your LLM applications and agents to get deep insights into their behavior.” source
Latest updates
- MLflow 3.16.0 Highlights: Build-Your-Own Trace Views, a Redesigned Trace Explorer, and Span Links (3.16.0)
Build trace views in plain English, use the trace explorer, and record links between spans.
- MLflow 3.15.0 Highlights: MCP Registry, a Smarter Assistant, and Multimodal Judges (3.15.0)
Register and share MCP servers, choose LLM providers in the Assistant, save and share Runs table views, transfer artifacts using presigned URLs, and evaluate image…
- MLflow 3.14.0 Highlights: One-Line Agent Onboarding, Review Queues, Pytest Integration, and the LLM Playground (3.14.0)
Onboard apps with mlflow agent setup, collect trace reviews, manage evaluation datasets in the UI, run pytest regression tests, and use an in-browser LLM Playground.