Manage data and machine learning models like code with Git-like version control.
Free plan available, paid tiers aboveVisit WebsiteFreemium Visit Website
Tracked since 2026
The Bottom Line
- Price
- Free plan available, paid tiers above. Compare plans
Key facts
- Applies Git-like version control to data and machine learning models.
- Enables reproducibility, collaboration, and traceability for data science projects.
- Scalable for both individual data scientists and enterprise AI teams.
Pros
- Free and open source
- Brings software engineering best practices to data science
- Enhances reproducibility and collaboration
- Scalable for various project sizes
- Integrates well with existing Git workflows
Cons
- Requires familiarity with Git concepts
- May have a learning curve for new users
What is DVC?
DVC (Data Version Control) brings software engineering best practices, specifically Git-like version control, to data, AI/ML, and data science teams. It allows users to manage data and models in the same way they manage code, enabling reproducibility, collaboration, and traceability in data-intensive projects.
DVC is designed to serve both individual data scientists and enterprise AI/data engineering teams. For individual data scientists, it offers an easy-to-use Git extension for small data science projects, integrating data version control into workflows with minimal overhead. For enterprise teams, DVC provides a highly scalable data version control infrastructure suitable for complex AI operations and big data environments, including petabyte-scale multimodal object stores and data lakes.
By applying version control to data, DVC helps teams track changes, revert to previous versions, and ensure consistency across different stages of their machine learning pipelines. It aims to make data science projects more robust, collaborative, and easier to manage, similar to how Git revolutionized software development.
Available on: Web, macOS, Linux, Windows
Louis Corneloup Source dvc.org ↗
Key Features
- Git-like data version control
- Integration with Git
- Support for large datasets and models
- Scalable for enterprise AI operations
- Compatible with object stores and data lakes
- VS Code extension available
Pricing Plans
Pricing checked Oct 4, 2026
| Plan | Price | Details |
|---|---|---|
| lakeFS Enterprise | Contact us |
|
| lakeFS (Free and open source) | Free | |
| DVC (Free and open source) | Free |
|
Is DVC worth the price?
Great value
The pricing for DVC and lakeFS is exceptionally generous, offering robust data version control solutions completely free of charge for their open-source versions.
The 'Contact us' for Enterprise indicates a higher, custom price, but the free options are excellent for individuals and small teams. This model is best for data scientists and MLOps teams looking for powerful version control without upfront costs.
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DVC FAQ
How does DVC enhance reproducibility in machine learning projects?
DVC enhances reproducibility by applying Git-like version control to data and models, allowing teams to track changes and revert to previous versions. This ensures consistency across different stages of machine learning pipelines, making projects more robust and traceable.
Which teams benefit most from using DVC?
DVC is beneficial for both individual data scientists and enterprise AI/data engineering teams. It offers an easy-to-use Git extension for small data science projects and provides a scalable data version control infrastructure for complex AI operations and big data environments.
How is DVC priced?
DVC is available on a free tier, with additional paid plans offered for more extensive usage and advanced features. It is also free and open source, providing a foundational set of capabilities without cost.
What kind of limitations might users encounter with DVC?
Users may encounter a learning curve with DVC, as it requires familiarity with Git concepts. This is a trade-off for bringing software engineering best practices to data science workflows.
Can DVC manage petabyte-scale multimodal object stores?
Yes, DVC is designed to be highly scalable for enterprise teams, supporting complex AI operations and big data environments. This includes managing petabyte-scale multimodal object stores and data lakes.
How does DVC compare to MLflow for data versioning?
DVC focuses on bringing Git-like version control directly to data and machine learning models, integrating with existing Git workflows. MLflow, while also supporting ML lifecycle management, has a different approach to tracking and managing experiments and models.
Source: dvc.org