Test your knowledge in live multiplayer Jeopardy-style trivia battles with AI-assisted grading that recognizes correct answers even with spelling mistakes.
DVC
Streamline AI and machine learning workflows by managing data and models like code with Git-like versioning, ensuring total reproducibility for data science teams.
Paid only
Price not published
About DVC
DVC (Data Version Control) is an open-source tool designed to bring software engineering best practices to data science and machine learning. It functions as a Git extension that allows users to version control datasets, machine learning models, and intermediate files that are typically too large for standard Git repositories. By creating lightweight meta-files that point to actual data stored in external systems, DVC enables teams to track the evolution of their data alongside their code, ensuring that every experiment is perfectly reproducible. The tool works by keeping data in remote storage, such as Amazon S3, Azure Blob Storage, or Google Cloud, while maintaining small text-based placeholders within Git. Users interact with the platform through a familiar command-line interface using commands like dvc add, dvc push, and dvc pull. Beyond simple storage management, the ecosystem includes features like DVCLive for tracking experiment metrics and a dedicated VS Code extension that provides a visual interface for managing data pipelines and comparing different runs. This structure allows developers to switch between data versions as easily as they switch between code branches. DVC is primarily built for individual data scientists and small-to-medium teams who need a lightweight, decentralized way to manage their local workflows and experiments. It is ideal for those already comfortable with Git who want to extend its capabilities to large-scale binary files without changing their existing development environment. For larger organizations handling petabyte-scale data lakes or complex AI operations, the ecosystem extends into lakeFS. This transition path makes the suite suitable for a wide range of users, from solo researchers to enterprise data engineering teams at Fortune 500 companies. What makes DVC different from many data management platforms is that it is storage-agnostic and integrates directly into existing development tools rather than requiring a proprietary database. It does not require a dedicated server, as it relies on the user's existing Git infrastructure and cloud storage providers. Following its acquisition by lakeFS, DVC offers a unique lifecycle that bridges the gap between local experimentation and enterprise-scale data versioning while remaining community-driven and open-source.
DVC pros & cons
Pros
- Open-source and free to use without proprietary platform lock-in.
- Integrates directly with existing Git workflows and command-line tools.
- Supports massive datasets by keeping metadata in Git and actual files in cloud storage.
- Provides high reproducibility for machine learning experiments and models.
- Scales from local projects to enterprise data lakes through the lakeFS ecosystem.
Cons
- Requires significant familiarity with Git and command-line interfaces.
- The VS Code extension will require a future reinstallation due to ownership transfer.
- Primary DVC tool is optimized for small teams rather than standalone petabyte-scale management.
- Requires manual configuration and management of remote cloud storage providers.
DVC use cases
- Data scientists can version control large datasets and machine learning models alongside their code to ensure experiment reproducibility.
- MLOps engineers can build automated pipelines that track data changes, making it easier to troubleshoot model performance regressions.
- Enterprise data teams can use lakeFS to manage petabyte-scale data lakes with Git-like branching and merging for safe data promotion.
- Research teams can collaborate on shared datasets without duplicating files by using remote storage to sync work.
DVC features
- vs code integration
- metadata management
- git-like data versioning
- large-scale data lake support
- reproducible pipelines
- zero-copy data branching
- experiment tracking with dvclive
- storage agnostic (s3, azure, gcp)
DVC pricing
Is DVC free? No, DVC doesn't offer a free plan.
DVC Open Source
Price varies
- Git-like data versioning
- Open source license
- Works with S3/Azure/GCP
- Experiment tracking
- VS Code extension
- Command-line interface
lakeFS Open Source
Price varies
- Git-like operations for data lakes
- Petabyte-scale scalability
- Zero-copy branching
- Data lifecycle management
lakeFS Enterprise
Price varies
- Scalable data version control infrastructure
- Designed for multimodal object stores
- Enterprise support
- Complex AI operations management
DVC FAQs
Is DVC still open source after the lakeFS acquisition?
Yes, DVC remains 100% open source under the same license. lakeFS is committed to the project's public development on GitHub and has no plans to paywall existing features.
Will my existing DVC projects break with the ownership transition?
There are no breaking changes to DVC behavior, commands, or workflows. Projects will continue to run as before, and future updates are intended to remain backward-compatible.
How does DVC differ from lakeFS?
DVC is a lightweight tool for local workflows and small-to-medium projects. lakeFS is a scalable infrastructure designed for enterprise-grade petabyte-scale data lakes.
Where is my data stored when using DVC?
DVC stores your actual data in your own preferred remote storage, such as S3, Google Cloud, or Azure. It only tracks small metadata files within your Git repository.
Do I need to reinstall the DVC VS Code extension?
Users will eventually need to reinstall the extension once the transfer to Treeverse is complete. The team will notify the community when this change occurs.
Ratings & reviews
No reviews yet. Be the first to share how DVC worked for you.
Sponsored tools
Connect with engaged niche audiences or monetize your subscriber base through an automated marketplace featuring verified metrics and secure Stripe payments.
Swap characters, backgrounds, or styles in any video while preserving original motion and camera paths. Perfect for digital marketers and creators.
Track brand citations and mentions across Google AI Mode, ChatGPT, Claude, and more. Discover top-cited sources and queries with zero signup required.