COMET VS MLFLOW
Finding MLflow Insufficient? Level Up Your MLOps Tooling with Comet!
MLflow is a great tool for teams who are just starting their ML journey. As teams start to deploy models in production, they require a scalable and secure tool like Comet that allows them to reproduce, debug, govern, and monitor their models.

Trusted by the most innovative ML teams
In-Depth Feature Breakdown: Comet vs MLflow
Explore why data scientists and Machine Learning teams are choosing Comet over MLflow.
Feature | Category | ||
---|---|---|---|
Deployment Options | Platform | ||
Scalability | Platform | ||
Collaboration & User Management | Platform | ||
Support | Platform | ||
Compliance Standards | Platform | ||
User Interface and Usability | Experiment Tracking | ||
Advanced Visualizations | Experiment Tracking | ||
Integration Ecosystem | Experiment Tracking | ||
Dataset Versioning & Lineage | Model Registry | ||
Model History for Auditing | Model Registry | ||
Data Drift Detection | Model Monitoring | ||
Model Fairness Monitoring | Model Monitoring | ||
Alerts for Production | Model Monitoring |
Monitor and manage models, from small teams to massive scale
Frequently Asked Questions
How difficult is it to switch from tracking experiments with MLflow to Comet?
Comet integrates with MLflow! MLflow users will have to change little to none in their training scripts to start logging data to Comet.
Is there a way to retroactively move all the data I have logged in MLflow to Comet?
Yes! We have built migration scripts to make this incredibly easy.
MLflow is open-source and free, why should I pay for a tool?
MLflow has well-known security vulnerabilities, lacks scalability, and has no dedicated support team to help you resolve issues. Fixing all these shortcoming would require a significant resource investment. It’s much easier and cheaper to choose a tool like Comet.