No. 14 of 37 ·ML Experiment Tracking Software

MLflow GenAI Evaluation

Web · Self-hosted · API

Freedom report

Two barsScore 6.3

Free tierA free tier is on its own pricing page
Open codeNo open-source code on record
Runs widely1 of 6 device platforms
DocumentedPlans, terms and facts published

MLflow GenAI Evaluation is ranked #14 of 37 in ML experiment tracking software on Freedom251. It runs on API, Self-hosted, Web. There is a free plan.

MLflow GenAI Evaluation plans and pricing

All plans

MLflow GenAI Evaluation Free Open source; evaluation API and UI mlflow.org · 29 Sept 2026

Compared on ML experiment tracking software

Free plan: Yes
Evaluation methods: hybrid
Tool-call checks: Yes
Trace ingestion: Yes
Safety evaluations: Yes
Regression runs: Yes
SDK language support: python

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The record

Purpose: MLflow helps teams measure, improve, and monitor the quality of LLM applications and AI agents from development through production.
Evaluation data: Evaluation Datasets provide a centralized place to manage test cases, ground-truth expectations, and evaluation data.
Human feedback: Users can collect and manage feedback from end users and domain experts, with feedback attached to traces and recorded with metadata.
Judges and scorers: MLflow includes LLM-as-a-Judge scorers and supports custom LLM judges and code-based scorers.
Review and compare: Evaluation results can be reviewed in the MLflow UI, including per-record results and comparisons across agent versions.
Production monitoring: MLflow Tracing captures metrics such as latency and token usage to help monitor application performance in production.
Automatic evaluation limit: Automatic evaluation supports LLM judges; code-based scorers are not supported in that mode.
Integrations: MLflow Tracing lists integrations with 40+ LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic.
Judge providers: MLflow's evaluation quickstart says judge models can use providers including Anthropic, Bedrock, Google, and xAI through built-in adapters.
Security controls: MLflow supports optional basic HTTP authentication for access control over experiments, registered models, and scorers.
Access control detail: Basic HTTP authentication requires a configured secret key for CSRF protection and an admin password when first enabled.
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.
Price from: Free

Where it ranks on Freedom251

Best ML Experiment Tracking Software in 2026#14 of 37
Best AI Agent Evaluation Tools in 2026#18 of 29
Best AI Prompt Management Software in 2026#8 of 25
Best MLOps Platforms in 2026#12 of 25

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Sources

mlflow.org/docs/latest/genai/eval-monitor· checked 29 Sept 2026
mlflow.org/genai/evaluations· checked 29 Sept 2026
mlflow.org/docs/latest/genai/eval-monitor/automati· checked 29 Sept 2026
mlflow.org/docs/latest/genai/tracing/integrations· checked 29 Sept 2026
mlflow.org/docs/latest/genai/eval-monitor/quicksta· checked 29 Sept 2026
mlflow.org/docs/latest/self-hosting/security/basic· checked 29 Sept 2026