Galileo AI vs MLflow: Observability, Prompts & Evals Comparison

Compare Galileo AI and MLflow side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	Galileo AI	MLflow
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Open Source
Best For	AI teams who need to measure and improve the quality of their LLM outputs	ML engineers and AI teams, especially those in the Databricks ecosystem
Website	rungalileo.io	mlflow.org
Key Features	LLM output quality evaluation Hallucination guardrails RAG evaluation metrics Data-centric AI debugging Automated error detection	OpenTelemetry-native tracing 50+ built-in eval metrics & LLM judges Prompt versioning & management Built-in AI gateway Full MLOps lifecycle (experiments, model registry, deployment)
Use Cases	Monitoring LLM output quality Detecting and preventing hallucinations Evaluating RAG pipeline accuracy Debugging data quality issues Continuous quality assurance	LLM observability & tracing Automated evaluation Prompt optimization Model deployment Production monitoring

When to Choose Galileo AI vs MLflow

Choose Galileo AI if you need

Monitoring LLM output quality
Detecting and preventing hallucinations
Evaluating RAG pipeline accuracy

Pricing: Freemium

Choose MLflow if you need

LLM observability & tracing
Automated evaluation
Prompt optimization

Pricing: Open Source

About Galileo AI

Galileo is a data intelligence platform for AI that helps teams evaluate, debug, and improve LLM applications. It provides metrics for hallucination detection, context adherence, chunk quality, and response completeness. Galileo's guardrails can be deployed in production to catch quality issues in real-time.

View Galileo AI profile →Visit website

About MLflow

Open-source MLOps platform with comprehensive GenAI tracing, evaluation, prompt management, and AI gateway. Maintained by the Linux Foundation.

View MLflow profile →Visit website

What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

Browse all Observability, Prompts & Evals tools →

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Quick Comparison

	Galileo AI	MLflow
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Open Source
Best For	AI teams who need to measure and improve the quality of their LLM outputs	ML engineers and AI teams, especially those in the Databricks ecosystem
Website	rungalileo.io	mlflow.org
Key Features	LLM output quality evaluation Hallucination guardrails RAG evaluation metrics Data-centric AI debugging Automated error detection	OpenTelemetry-native tracing 50+ built-in eval metrics & LLM judges Prompt versioning & management Built-in AI gateway Full MLOps lifecycle (experiments, model registry, deployment)
Use Cases	Monitoring LLM output quality Detecting and preventing hallucinations Evaluating RAG pipeline accuracy Debugging data quality issues Continuous quality assurance	LLM observability & tracing Automated evaluation Prompt optimization Model deployment Production monitoring

About Galileo AI

What is Observability, Prompts & Evals?

Browse all Observability, Prompts & Evals tools →