The best Quilt alternatives
The rest of Best Data Version Control Tools, in this site's order: each with what it costs, where it runs and its record score.
It suits AI developers working on model training, fine-tuning, AI application development, or production monitoring.
Roboflow is intended for developers and enterprises building computer vision applications.
DagsHub suits developers and teams managing machine-learning data and experiments across multiple project stages.
git-annex suits Git users comfortable with the command line who need to coordinate large files across repositories and storage locations.
Dolt suits developers and teams that want SQL data changes to be reviewable and reversible through version-control workflows.
It suits teams developing, training, and deploying AI/ML models that need GPU infrastructure management and experiment tracking.
Graviti is aimed at AI developers acquiring, storing, and processing unstructured data.
It suits people building, sharing, or managing machine learning models, datasets, and apps.
Neon suits developers and teams building relational database-backed projects that need managed cloud hosting, branching, or programmatic…
Apache Iceberg suits teams managing analytic tables who need multiple query or processing engines to work with shared data.
Apache Paimon may suit teams building lakehouse systems that need streaming and batch operations, table time travel, or CDC ingestion.
Bauplan suits developers and AI agents building, validating, and shipping data pipelines on production data.
It suits researchers, developers, and AI teams working with files and tables across cloud or local storage.
Crowdee is aimed at newsrooms, compliance teams, and AI researchers that need content checks and review evidence.
Hudi suits teams building data lakes or lakehouses that need streaming ingestion, incremental processing and table operations.
Delta Lake suits teams building lakehouse architectures that need table transactions, version access, and batch and streaming workloads.
lakeFS is aimed at data engineers, data scientists, ML engineers, ML platform teams, and agentic AI initiatives.
It suits developers and data engineers handling auditable lineage, complex domain models, collaborative workflows, or changing structured content.