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Open-source version control system for data science and machine learning projects, providing a Git-like experience to organize data, models, and experiments. Runs locally as a Git extension with no server, tailored for individual data scientists and small teams on small-to-medium projects.
Start here for the decision-making essentials: what DVC does, who it is for, how it is accessed, and the first-party sources behind this profile.
See official pricing
Read-only public captures of DVC’s homepage. Screenshots are dated, never live embeds, and open full-screen.
Short answers to the questions buyers and builders commonly ask about DVC. Each answer cites the shared ledger below, where every source is listed once.
Open-source version control system for data science and machine learning projects, providi · Version control for data, models, and experiments · DVCLive (maintained alongside the DVC project)
Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.
Individual data scientists working on small data science projects · Enterprise AI and data engineering teams handling complex AI operations and big data envir · Individual data scientists and small teams
For individual datascientists The easy to use data version control Git extension for small data science projects.
Versioning and organizing data, models, and experiments across data science and ML workflo
Git-like experience to organize your data, models, and experiments.
Optimized for single-project, small-dataset workflows that comfortably round-trip through · VS Code extension users will need to reinstall from Treeverse once transferred; no other b
DVC is excellent at what it was built for: lightweight, Git-native versioning for a data scientist working on a single project with small datasets that comfortably round-trip through a local cache.
Compatible with S3, GCS, and Azure Blob Storage as DVC remote storage backends · Visual Studio Code extension (DVC extension for VS Code)
Supported remotes are S3, GCS, and Azure Blob Storage.
Local-first Git-based workflow: data stored in cloud storage, version metadata kept in Git
DVC keeps your data in cloud storage and your version info in Git, and moves bytes through a local cache with dvc add, dvc push, and dvc pull.
This profile connects the jobs DVC is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
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Home – DVC
Open-source version control system for Data Science and Machine Learning projects · Git-like experience to organize data, models, and experiments · Designed for individual data scientists and small teams on small-to-medium projects
Open-source version control system for data science and machine learning projects, providing a Git-like experience to organize data, models, and experiments
Versioning and organizing data, models, and experiments across data science and ML workflows
Individual data scientists working on small data science projects
Enterprise AI and data engineering teams handling complex AI operations and big data environments with petabyte-scale multimodal object stores and data lakes
Open-source
Git extension that runs locally, starts in seconds, and requires no server
Local-first Git-based workflow: data stored in cloud storage, version metadata kept in Git, bytes moved through a local cache via dvc add, dvc push, and dvc pull
Compatible with S3, GCS, and Azure Blob Storage as DVC remote storage backends
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
lakeFS acquired the DVC open-source project from Iterative.ai and took over stewardship and active development; DVC remains open source.
View source [2]Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with DVC. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Optimized for single-project, small-dataset workflows that comfortably round-trip through a local cache; not designed for tens of thousands of files or datasets too large to pull onto a laptop
DVC (alternate name: Data Version Control), which was acquired by lakeFS
DVC Community chat for fast help from data science practitioners
https://github.com/iterative/dvc
Version control for data, models, and experiments
Individual data scientists and small teams
Lightweight Git-based tool for small-to-medium data science projects
Yes, open-source project with its own community and website (dvc.org)
100% open source under the same license (no paywall or restricted features)
lakeFS / Treeverse (acquired DVC from Iterative.ai)
Visual Studio Code extension (DVC extension for VS Code)
DVCLive (maintained alongside the DVC project)
Public development on GitHub (repos to move from Iterative to Treeverse)
Discord (https://discordapp.com/invite/dvwXA2N), community forum (discuss.dvc.org), and email at support@dvc.org
X/Twitter (@DVCorg at https://x.com/DVCorg) and YouTube (https://www.youtube.com/channel/UC37rp97Go-xIX3aNFVHhXfQ)
VS Code extension users will need to reinstall from Treeverse once transferred; no other breaking changes to DVC behavior, commands, or workflows
Open-source version control system for Data Science and Machine Learning projects · Git-like experience to organize data, models, and experiments · Acquired by lakeFS (Treeverse) from Iterative.ai
Open-source version control system for Data Science and Machine Learning projects with Git-like experience to organize data, models, and experiments
Enterprise AI and data engineering teams: highly scalable data version control infrastructure for complex AI operations and big data environments with petabyte-scale multimodal object stores and data lakes
Individual data scientists: easy-to-use data version control Git extension for small data science projects with minimal overhead
Thousands of users and customers from startups to Fortune 500 companies
Open-source project with public GitHub repository
Serverless model: starts in seconds and needs no server, using a Git-like model with data in cloud storage and version info in Git
Moves data bytes through a local cache using dvc add, dvc push, and dvc pull commands
Supported remotes are S3, GCS, and Azure Blob Storage
DVC was acquired by lakeFS
Migration tool dvc-to-lakefs enables zero-copy import of DVC-tracked data into lakeFS; installed via pip install dvc-to-lakefs and run with lakectl import-from-dvc
GitHub repository at https://github.com/iterative/dvc
Community support via a Slack-like chat where data science practitioners can get help
