MLflow GenAI Evaluation
MLflow GenAI Evaluation helps teams assess and monitor LLM applications and AI agents through development and production.
What MLflow GenAI Evaluation is
MLflow GenAI Evaluation helps teams assess and monitor LLM applications and AI agents through development and production. Evaluation Datasets organize test cases, expected outcomes, and related evaluation data. Teams can collect feedback from end users and domain experts, attach it to traces, and retain metadata. Scoring options include LLM-as-a-Judge, custom LLM judges, and code-based scorers, while automatic evaluation supports LLM judges but not code-based scorers. The MLflow UI displays results by record and allows comparisons between agent versions. MLflow Tracing captures latency and token-usage metrics for production monitoring, and lists integrations with more than 40 LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic. Built-in adapters allow judge models to use providers such as Anthropic, Bedrock, Google, and xAI. The open-source evaluation API and UI are free, with API, self-hosted, and web options. Optional basic HTTP authentication controls access to experiments, registered models, and scorers, requiring a configured CSRF secret key and an admin password.
Good
- Evaluation datasets manage test cases and expected outcomes.
- Feedback can be attached to traces with metadata.
- Results include per-record detail and version comparisons.
- Tracing records latency and token usage.
- Free open-source evaluation API and UI.
Watch out
- Automatic evaluation does not support code-based scorers.
- Basic HTTP authentication requires a CSRF key and admin password.
Who it suits
It suits teams building or operating LLM applications and AI agents that need evaluation and monitoring across the application lifecycle. Python SDK support is listed.
Verdict
MLflow GenAI Evaluation combines dataset management, judge-based scoring, feedback, and production tracing in a free open-source offering. Teams relying on automatic evaluation should note its limitation around code-based scorers.
Plans and prices
From MLflow GenAI Evaluation's own pricing page, checked 29 Sep 2026.
| Plan | Price | What it gives you |
|---|---|---|
| MLflow GenAI Evaluation | Free | Open source; evaluation API and UI |
The facts
Each one read from MLflow GenAI Evaluation's own pages, with the date it was checked.
What it does
- Access control detail
- Basic HTTP authentication requires a configured secret key for CSRF protection and an admin password when first enabled.checked 29 Sep 2026
- Automatic evaluation limit
- Automatic evaluation supports LLM judges; code-based scorers are not supported in that mode.checked 29 Sep 2026
- Evaluation data
- Evaluation Datasets provide a centralized place to manage test cases, ground-truth expectations, and evaluation data.checked 29 Sep 2026
- Human feedback
- Users can collect and manage feedback from end users and domain experts, with feedback attached to traces and recorded with metadata.checked 29 Sep 2026
- Integrations
- MLflow Tracing lists integrations with 40+ LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic.checked 29 Sep 2026
- Judge providers
- MLflow's evaluation quickstart says judge models can use providers including Anthropic, Bedrock, Google, and xAI through built-in adapters.checked 29 Sep 2026
- Judges and scorers
- MLflow includes LLM-as-a-Judge scorers and supports custom LLM judges and code-based scorers.checked 29 Sep 2026
- Production monitoring
- MLflow Tracing captures metrics such as latency and token usage to help monitor application performance in production.checked 29 Sep 2026
- Purpose
- MLflow helps teams measure, improve, and monitor the quality of LLM applications and AI agents from development through production.checked 29 Sep 2026
- Review and compare
- Evaluation results can be reviewed in the MLflow UI, including per-record results and comparisons across agent versions.checked 29 Sep 2026
- Security controls
- MLflow supports optional basic HTTP authentication for access control over experiments, registered models, and scorers.checked 29 Sep 2026
- Who it is for
- The product is designed for teams building and operating LLM applications and AI agents who want evaluation and monitoring across the application lifecycle.checked 29 Sep 2026
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