lakeFS vs DataLad

lakeFS9.4Visit DDataLad9.0Visit

Higher score lakeFS #1 in Data Version Control Tools 9.4/10 Free plan · pricing on request · 15-day trial Free planFree trial✓ 0 of 2 features Visit lakeFS

vs

D DataLad #3 in Data Version Control Tools 9.0/10 Free plan Free plan✓ 2 of 2 features Visit DataLad

• lakeFS fits best

  Enterprise lakehouse and object-storage teams

• DataLad fits best

  Researchers versioning large file datasets

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Side by side

Feature	lakeFS 9.4/10 Visit ↗	DDataLad 9.0/10 Visit ↗
At a glance
Editor score	9.4	9.0
Ranking	#1 in Data Version Control Tools	#3 in Data Version Control Tools
Best for	Enterprise lakehouse and object-storage teams	Researchers versioning large file datasets
Pricing model	Free plan + paid	Free
Starting price	—	Free plan
Free plan	✓	✓
Free trial	15 days	—
Deployment	Cloud, Self-hosted	Self-hosted
Platforms	Web	Windows, macOS, Linux
Support	Community, Docs	Community, Docs
Integrations	20 integrations	5 integrations
Built for	Mid-market, Enterprise	Solo, Small business, Mid-market
Features lakeFS 0/2 · DataLad 2/2
Dataset branching	Not checked yet	✓ (best)
Point-in-time rollback	Not checked yet	✓ (best)
Specs
Data scope	Not checked yet	Files
Snapshot granularity	Not checked yet	File
Storage backend	Not checked yet	Bring_your_own
Deployment model	Not checked yet	Self_hosted
Our review
Pros	Zero-copy branching, atomic merges, rollbacks, and version history Works across Iceberg, Delta Lake, structured, and unstructured data Hybrid deployment with broad integrations and governance controls	Versions arbitrarily large files with branching and rollback Captures provenance and supports reproducible command execution Connects datasets with GitHub, Figshare, Dropbox, Amazon S3, and OSF
Cons	Enterprise pricing uses a contact-sales model The 15-day trial applies to Enterprise, not the permanent Community edition Advanced features such as RBAC and Metadata Search are Enterprise highlights	Uses a self-hosted deployment model Storage depends on user-configured siblings or special remotes The workflow centers on Git and git-annex dataset management
Our verdict	lakeFS is an open-source and commercial data version-control platform for data, AI, and ML teams. It adds Git-like branches, commits, merges, tags, rollbacks, audit trails, lineage, and zero-copy data environments to object storage. The… Read the review →	DataLad is a free, open-source, Python-based tool for managing and versioning datasets. Built on Git and git-annex, it tracks file changes, supports arbitrarily large files, and helps researchers and technical teams work with… Read the review →

Strengths and trade-offs

• lakeFS — where it wins

  • Zero-copy branching, atomic merges, rollbacks, and version history
  • Works across Iceberg, Delta Lake, structured, and unstructured data
  • Hybrid deployment with broad integrations and governance controls

  Where it doesn't

  • Enterprise pricing uses a contact-sales model
  • The 15-day trial applies to Enterprise, not the permanent Community edition
  • Advanced features such as RBAC and Metadata Search are Enterprise highlights

• DataLad — where it wins

  • Versions arbitrarily large files with branching and rollback
  • Captures provenance and supports reproducible command execution
  • Connects datasets with GitHub, Figshare, Dropbox, Amazon S3, and OSF

  Where it doesn't

  • Uses a self-hosted deployment model
  • Storage depends on user-configured siblings or special remotes
  • The workflow centers on Git and git-annex dataset management

• lakeFS9.4/10 · Free plan · pricing on request · 15-day trial

  Open-source, Git-like version control for governed data on object storage.

  Visit lakeFSFull verdict →
• DDataLad9.0/10 · Free plan

  A free, technical toolkit for versioning, reproducing, and synchronizing large file datasets.

  Visit DataLadFull verdict →