Open Source Alternatives
Open source alternatives to DVC
Open source alternatives to DVC, ranked by GitHub stars and freshness.
8 open-source alternatives in the index, ranked by GitHub stars and freshness.
Metaflow
Community
Build, Manage and Deploy AI/ML Systems
Alternative to Prefect, Argo Workflows, Kubeflow +1 more
Best for: Data scientists and ML engineers building reproducible, scalable machine learning pipelines
Pachyderm
Community
Data-Centric Pipelines and Data Versioning
Alternative to Dvc
Best for: Data engineers and ML teams needing reproducible data pipelines
LakeFS
Community
lakeFS - Data version control for your data lake | Git for data
Alternative to Dvc
Best for: Data engineers and teams managing data lakes that need version control for data pipelines and experiments
Ploomber
Community
The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Alternative to Prefect, Dvc
Best for: Data engineers and scientists building Python pipelines with iterative development needs
Hamilton
Community
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere pytho
Alternative to Prefect, Dvc, Argo Workflows +1 more
Best for: Data scientists and engineers who need testable, documented dataflows with automatic lineage tracking.
Quilt
Community
Quilt is a Scientific Data Management Platform on AWS that helps teams and AI find, trust, and reuse data through deeply versioned, context-rich data packages.
Alternative to Dvc
Best for: Research teams and data engineers managing versioned scientific datasets on AWS
ArtiVC
Community
A version control system to manage large files.
Alternative to Dvc
Best for: Teams needing lightweight version control for large files in observability or data science workflows
Airflow
Community
Platform created by the community to programmatically author, schedule and monitor workflows.
Alternative to Prefect, Argo Workflows, Kubeflow +1 more
Best for: Teams that need a robust, code-driven scheduler for batch-oriented data pipelines