mlop: Open Source Alternative to MLflow

mlop is an MLOps platform with a Weights & Biases-compatible API — an MLflow alternative for experiment tracking, model registry, and team collaboration. Self-hosted with W&B API compatibility.

Compare mlop with Weights & Biases and Neptune before you choose your stack.

Key features

Experiment tracking

• W&B-compatible API for seamless migration
• Real-time metric logging and visualization
• Hyperparameter tracking and comparison
• Rich media logging (images, audio, video)
• Experiment tagging and organization

Model registry

• Centralized model versioning
• Model lineage and provenance tracking
• A/B testing and model comparison
• Deployment-ready model artifacts
• Integration with ML frameworks

Collaboration

• Team workspaces and projects
• Shared experiments and notebooks
• Comments and annotations
• Role-based access control
• Usage analytics and quotas

Infrastructure

• Self-hosted with easy deployment
• Scalable backend with PostgreSQL
• REST API and CLI
• Integration with ML frameworks
• Webhook and notification support

mlop vs MLflow

	mlop	MLflow
License	Apache-2.0 (open source)	Proprietary
Models	Bring your own keys / local models	Vendor-locked models
Deployment	Self-hosted or cloud	SaaS only
Privacy	Data stays on your infrastructure	Processed by vendor
Cost	Free software + API usage	Subscription pricing

Choose mlop if you want open-source code, self-hosting options, and full control over your data and deployment.

Choose MLflow if you prefer a managed proprietary product with vendor support and minimal setup.

Browse more open-source alternatives to MLflow, or explore other tools in Developer Tools.

At a glance

License	Apache-2.0
Stack	Python, TypeScript, PostgreSQL
Self-hosted	Yes — mlop OSS
Cloud	mlop.ai (managed)
API	W&B-compatible

Self-hosting

pip install mlop

mlop can be self-hosted with Docker or pip install. The cloud version provides managed infrastructure and team features.

FAQ

Is mlop a free alternative to MLflow?

Yes. mlop is open source under Apache-2.0. You can self-host it at no software cost — you only pay for infrastructure or optional managed services.

How does mlop compare to MLflow?

mlop gives you source code access, self-hosting, and data ownership. MLflow is a proprietary product focused on managed convenience. See the comparison table above for a side-by-side breakdown.

Can I self-host mlop?

Yes. mlop supports self-hosted deployment, which is a core reason teams choose it over MLflow. Check the Getting started or Self-hosting section for install commands.

Is mlop suitable for production?

mlop is actively maintained with a strong open-source community. Many teams run it in production as a Developer Tools alternative to MLflow. Review the At a glance table for license and stack details.

What are alternatives to mlop and MLflow?

Browse alternatives to MLflow for more open-source options, including tools compared to Weights & Biases. Explore the full Developer Tools category for related projects.