tools
Comet
Comet is an MLOps platform for tracking experiments, managing datasets and models, and monitoring machine learning systems.
Comet helps data scientists, ML engineers, and research teams record training runs, compare model performance, version datasets and artifacts, and manage model lifecycles. It provides experiment visualizations, a model registry, hyperparameter search, and production monitoring.
The platform is available as a cloud service or self-hosted deployment. The Free plan supports one platform user; Pro costs $19 per user/month; Enterprise pricing is custom. Production monitoring and flexible deployments are Enterprise features.
Features
- Track and compare machine learning training runs
- Create custom visualizations with Python
- Manage and version datasets and artifacts
- Register, version, and promote models
- Run automated hyperparameter searches
- Monitor production models for data drift
- Define custom metrics and production alerts
- Deploy in the cloud, VPC, or on-premises
Use cases
- Track training runs across machine learning experiments
- Compare model metrics, parameters, predictions, and system data
- Version datasets and connect them to model lineage
- Promote approved model versions through deployment stages
- Detect data drift and performance changes in production
- Collaborate on reproducible ML research and development
Pros
Cons
Pricing
- Starting price
- Free
- Pricing checked
- 2026-09-19
Free
$0
- 1 platform user
- Track and compare machine learning training runs
- Dataset management and versioning
- Model Registry
Pro
$19
- Up to 10 users
- 1500 training hours included
- Email support
- Generous storage limits
Enterprise
Custom
- Unlimited users
- Unlimited training hours
- Flexible deployments
- Model production monitoring
- Single sign on
- Dedicated support and SLAs