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  1. Home
  2. MLflow

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.

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“It’s one of the things that zero to anything is like night and day…We looked at all the options on the market and went with Comet.”

Erica Green

Erica Green

ML Team Lead

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“MLflow was not well maintained, and compared to Comet, it didn’t have all the features needed to track experiments, especially as we started scaling.”

Greig Cowan, NatWest

Greig Cowan

HEAD OF Data Science

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“With MLflow we needed to take care of updates, backups and implement our own user management layer. Comet handles all of that and much more.”

Ashwin Bhide, Affirm

Ashwin Bhide

ML Platform Engineer

In-Depth Feature Breakdown: Comet vs MLflow

Explore why data scientists and Machine Learning teams are choosing Comet over MLflow.

FeatureComet logoMLflow logoCategory
Deployment OptionscheckmarkSupport for VPC/On-Prem Deployments and SaaS hosted solutionscrossCustomer needs to manage backups, scalability and securityPlatform
ScalabilitycheckmarkDesigned for high scalability: Efficiently handles large datasets and high volume of experimentscrossLimited scalability, performance issues with high experiment volume, projects with many experiments or big filesPlatform
Collaboration & User ManagementcheckmarkAdvanced tools for collaboration and admin governancecrossNo notion of user management for teamwork or securityPlatform
SupportcheckmarkDedicated technical support teamcrossNo direct supportPlatform
Compliance StandardscheckmarkSOC 2 Type 2 compliant and ISO 27001 certifiedcrossDocumented security vulnerabilitiesPlatform
User Interface and UsabilitycheckmarkFast, snappy, intuitive and customizable user interfacecrossLess intuitive UI with usability issues and no customizationExperiment Tracking
Advanced VisualizationscheckmarkHundreds of visualizations available with the ability to build your owncrossBasic built-in chartsExperiment Tracking
Integration EcosystemcheckmarkSeamless integration with a wide range of popular ML tools and platformscrossLimited integrations that requires the user to write a lot of boilerplate codeExperiment Tracking
Dataset Versioning & LineagecheckmarkRobust versioning for model reproducibility and traceabilitycrossNo dataset versioningModel Registry
Model History for AuditingcheckmarkDocuments all necessary information for accountability and transparency in the model lifecyclecrossNo auditing capabilitiesModel Registry
Data Drift DetectioncheckmarkData drift detection for model input features and output predictionscrossNo production monitoringModel Monitoring
Model Fairness MonitoringcheckmarkDetect model bias across different data segmentscrossNo production monitoringModel Monitoring
Alerts for ProductioncheckmarkConfigure customizable alerts for Model Performance degradation in productioncrossNo production monitoringModel Monitoring

Monitor and manage models, from small teams to massive scale

Easy to integrate with any training environment

Add two lines of code to your notebook or script and automatically start tracking code, hyperparameters, metrics, and more, so you can compare and reproduce training runs.

Experiment Management

Track and share training run results in real time

Comet’s ML platform gives you visibility into training runs and models so you can iterate faster.

Experiment Management

Build your own tailored, interactive visualizations

In addition to the 30+ built-in visualizations Comet provides, you can code your own visualizations using Plotly and Matplotlib.

Track and version datasets and artifacts

Knowing what data was used to train a model is a key part of the MLOps lifecycle. Comet Artifacts allows you to track data by uploading directly to Comet’s machine learning platform or by storing a reference to it.

Comet Artifacts

Manage your models and trigger deployments

Comet Model Registry allows you to keep track of your models ready for deployment. Thanks to the tight integration with Comet Experiment Management, you will have full lineage from training to production.

Comet Model Registry

Monitor your models in production

The performance of models deployed to production degrade over time, either due to drift or data quality. Use Comet’s machine learning platform to identify drift and track accuracy metrics using baselines automatically pulled from training runs.

Comet Model Production Monitoring

Evaluate and optimize LLM apps and agents

Track, score, and unit test LLM applications and complex agentic systems, with automated prompt engineering, guardrails, LLM-as-a-judge metrics, simple cross-functional collaboration, and more

Open-Source LLM Evaluation

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.

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