Manage your ML lifecycle: track, register, and deploy models
Visit WebsiteReviews onCapterraSourceForge
208 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Open source
Biggest con
UI basic
TL;DR - MLflow
- ML experiment tracking and versioning
- Log metrics, parameters, and artifacts
- Compare runs to find the best model
Pricing: Free forever
Best for: Individuals & startups
4.1/5 across review platforms
What is MLflow?
MLflow manages the machine learning lifecycle. Experiment tracking, model registry, and deployment-MLOps platform that's open source and widely adopted.
The experiment tracking is solid. The model registry helps management. The deployment options are flexible.
ML teams use MLflow because it's the open-source MLOps standard.
Available on: Web
Pros & Cons
Pros
- Open source
- Experiment tracking
- Model registry
- Deployment support
- Self-hostable
Cons
- UI basic
- Scale limitations
- Setup required
- Databricks dependency growing
- Less modern feel
Ratings Across the Web
4.1(208 reviews)
Ratings aggregated from independent review platforms. Learn more
Key Features
MLOps platformExperiment trackingModel registryDeploymentOpen sourceDatabricks
Pricing Plans
Open Source
Free
- Self-hosted free
- Experiment tracking
- Model registry
- Apache 2.0 license
- Community support
Reviews
4.1/5
Across 208 verified user reviews on Capterra, SourceForge
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MLflow FAQ
Is MLflow free?
Yes, MLflow is free and open source. Managed options available from Databricks and others. Apache 2.0 license.
What is MLflow?
MLflow is an open-source ML lifecycle platform. Track experiments, package models, deploy anywhere. Created by Databricks.
MLflow vs Weights & Biases?
MLflow is open source and self-hosted. W&B is cloud-first with better visualization. MLflow for control; W&B for collaboration.
What is experiment tracking?
Log parameters, metrics, and artifacts from ML runs. Compare experiments to find best models. Essential for reproducible ML.
Source: mlflow.org