Iterative / dvc.org
Git-based data version control system for ML datasets and model files, enabling reproducible ML experiments by tracking which data and code produced each model.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
DVC (Data Version Control) is an open source tool built on top of Git that brings version control to ML datasets, model files, and ML pipelines — assets that are too large for standard Git but critical for ML reproducibility.
The core problem DVC addresses is that ML reproducibility requires knowing not just which code produced a model, but which data, with which hyperparameters, in which environment. Git tracks code changes, but a 10GB training dataset or a 500MB model file cannot be stored in Git. DVC stores large files in configurable remote storage (S3, GCS, Azure Blob, SSH) and tracks their versions in Git-compatible metadata files.
Data versioning with DVC allows branching and checking out datasets like code — `dvc checkout` switches the local data to the version associated with the current Git commit, enabling reproducible experiment environments. Teams can tag releases with specific data versions for audit trails.
ML Pipelines define multi-step ML workflows (data preparation, feature engineering, training, evaluation) as directed acyclic graphs. DVC tracks dependencies between steps and skips re-running steps whose inputs haven't changed — similar to Make but for ML pipelines with data awareness.
Iterative Studio, the managed cloud product, provides a web UI for tracking experiment metrics across DVC-tracked experiments, managing dataset versions, and visualising ML pipeline runs for teams using DVC.
DVC runs as ml platform software built around code and data workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, cli, and web, with API access for teams that want to embed it into their own products.
The capabilities that matter most for teams evaluating DVC.
Tracks large ML datasets and model files in remote storage with Git-compatible metadata, enabling reproducible experiment environments by checking out data and code together.
Defines multi-step ML workflows as dependency graphs, caching stages and skipping re-execution of unchanged steps for efficient pipeline iteration.
Extends familiar Git version control semantics to data and models — branches, tags, and commits apply to both code and data assets without new paradigms.
DVC is open source and free. Iterative Studio managed platform from $25/month. Enterprise custom.
Model
Open Source
Starting price
Free
Free trial
No
MLflow (rank 480) handles experiment tracking alongside DVC for data versioning. Weights & Biases (covered) provides both. Pachyderm provides enterprise data versioning as an alternative. Lakefs provides a Git-like data lake versioning alternative.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
Iterative
Platforms
Python, CLI, Web
Deployment
Open Source, SaaS, Self-hosted
Integrations
Git, GitHub, GitLab, MLflow, Weights & Biases, S3, GCS, Azure, API
Team Collaboration
Yes
Launch Year
2022
Compliance signals and data-handling notes as reported by the vendor.
Open source self-hosted keeps data on customer-controlled storage. Iterative Studio subject to Iterative's data handling. Enterprise agreements available.
DVC metadata stored in Git. Large data files stored in customer-controlled remote storage (S3, GCS, Azure) — Iterative does not access data files, only experiment metadata for Studio.
Editorial Verdict
DVC is the essential open source data versioning tool for ML teams who want reproducible experiments by tracking which data, code, and parameters produced each model, without paying for hosted MLOps infrastructure.
Last verified July 24, 2026.
With hundreds of thousands of users and integrations into VS Code, GitHub Actions, and popular ML frameworks, DVC has become a foundational MLOps tool alongside MLflow.
DVC is open source and free. Iterative Studio managed platform from $25/month. Enterprise custom.
Open source and free for self-hosted. ZenML Pro managed from $59/month. Enterprise custom.
Open source self-hosted keeps data on customer-controlled storage. Iterative Studio subject to Iterative's data handling. Enterprise agreements available.
Open source self-hosted keeps all pipeline data on customer infrastructure. ZenML Pro subject to ZenML's data handling. Enterprise agreements available.
DVC metadata stored in Git. Large data files stored in customer-controlled remote storage (S3, GCS, Azure) — Iterative does not access data files, only experiment metadata for Studio.
Self-hosted ZenML keeps pipeline metadata and artefacts on customer-controlled storage. ZenML Pro transmits pipeline metadata to ZenML's managed infrastructure.
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