D
DVC
DVC (Data Version Control) brings software engineering best practices, specifically Git-like version control, to data, AI/ML, and data science teams.
Our verdict
Who it's for
Manage data and machine learning models like code with Git-like version control.
Who should look elsewhere
Requires familiarity with Git concepts
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
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
| Plan | Price | Billing | Notes |
|---|---|---|---|
| lakeFS Enterprise | — | month | Highly scalable data version control infrastructure; Designed for complex AI operations and big data environments; Petabyte-scale multimodal object stores and data lakes |
| lakeFS (Free and open source) | — | month | — |
| DVC (Free and open source) | — | month | Easy to use data version control Git extension; For small data science projects; Apply data version control to your data science workflows with minimal overhead |