LakeFS
Claim this toolApply Git-like version control to your data lake for reproducible AI and streamlined data workflows.
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Tracked since2026
0 reviews tracked·1 press mentionThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Accelerates AI delivery and development velocity.
Biggest con
Specific advanced features like Iceberg REST Catalog and Metadata Search are only available in the Enterprise plan.
TL;DR - LakeFS
- Applies Git-like version control to data lakes for managing data lifecycle and provenance.
- Enables isolated testing, instant rollbacks, and reproducible AI/ML training.
- Integrates with existing data and AI stacks, supporting various storage, compute, and orchestration tools.
Pricing: Free plan available
Best for: Growing teams
What is LakeFS?
lakeFS is a data version control system designed to bridge the AI infrastructure gap by bringing software engineering best practices to data management. It provides a control plane for AI-ready data, enabling teams to manage the data lifecycle, provenance, and unified access for AI and data initiatives. Built on a scalable architecture, lakeFS allows users to test pipeline and model changes in isolation on production data without creating copies, instantly rollback from data incidents, and enforce data quality and compliance standards.
The platform helps make training reproducible by tracking data used in experiments and model training, offering full visibility into data history with a built-in audit trail, and automatically satisfying model governance requirements. It also reduces data access friction by allowing users to work with any tool on remote data as if it were local, manage access permissions across all storage from one place, and keep GPUs busy without waiting for data. lakeFS integrates seamlessly with a wide range of object storage solutions, compute engines, ingest technologies, data formats, orchestration tools, and ML/AI stacks, making it a versatile solution for organizations looking to accelerate AI delivery, ensure reproducibility, and reduce data friction.
Available on: Web
Pros & Cons
Pros
- Accelerates AI delivery and development velocity.
- Ensures data quality and compliance with isolated testing and instant rollbacks.
- Reduces storage costs by avoiding data duplication.
- Streamlines data science and MLOps workflows.
- Provides transparent, traceable, and repeatable development for AI.
Cons
- Specific advanced features like Iceberg REST Catalog and Metadata Search are only available in the Enterprise plan.
- SOC2 support is exclusively offered in the Enterprise plan.
- While lakeFS supports multiple cloud providers, lakeFS Cloud currently supports AWS, Azure, and GCP, potentially limiting options for users on other cloud platforms for the managed service.
Preview
Key Features
Format-Agnostic Data Version ControlCloud-Agnostic Zero Clone copy for isolated environment (via branches)Atomic Data Promotion (via merges)Configurable Garbage CollectionData CI/CD Using lakeFS HooksRole-Based Access Control (RBAC)Integrates with Your Data StackAudit Logs
Pricing Plans
Pricing checked Jul 6, 2026
Open Source
Free forever
Everything in all plans.
Enterprise
Contact sales
Everything in all plans, plus:
- Unlimited seats
Included in all plans
- Format-Agnostic Data Version Control
- Cloud-Agnostic
- Zero Clone copy for isolated environment (via branches)
- Atomic Data Promotion (via merges)
- Data Stays in One Place
- Configurable Garbage Collection
- Data CI/CD Using lakeFS Hooks
- Integrates with Your Data Stack
Reviews
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LakeFS FAQ
How does LakeFS help ensure data quality and compliance?
LakeFS ensures data quality and compliance by enabling isolated testing of pipeline and model changes on production data without creating copies. This allows teams to instantly roll back from data incidents and enforce data quality and compliance standards effectively. It also provides a built-in audit trail for full visibility into data history.
Which teams would benefit most from using LakeFS?
LakeFS is best suited for teams involved in AI and data initiatives, particularly those focused on data science, MLOps, and data engineering. It helps accelerate AI delivery, streamline data workflows, and ensure reproducibility in model training and experimentation. The platform bridges the AI infrastructure gap by applying software engineering best practices to data management.
How does LakeFS compare to Prefect for data workflow management?
LakeFS focuses on applying Git-like version control to data lakes to ensure reproducible AI and streamlined data workflows, including instant rollbacks and isolated testing. In contrast, Prefect is primarily an orchestration tool designed for building, running, and monitoring data pipelines. LakeFS specifically addresses data versioning and management within the data lake context.
What kind of limitations should users be aware of with LakeFS?
Users should note that specific advanced features, such as Iceberg REST Catalog and Metadata Search, are exclusively available in the Enterprise plan. Additionally, SOC2 support is only offered in the Enterprise plan. While LakeFS supports multiple cloud providers, its managed cloud service currently supports only AWS, Azure, and GCP, which might limit options for users on other cloud platforms.
How is LakeFS priced?
LakeFS is available on a free tier, allowing users to get started without initial cost. For organizations requiring more extensive usage and additional features, paid plans are offered. These paid plans provide access to advanced capabilities and increased capacity.
Can LakeFS help with reproducible AI training?
Yes, LakeFS helps make AI training reproducible by tracking the specific data used in experiments and model training. It provides full visibility into data history through a built-in audit trail, which automatically satisfies model governance requirements. This ensures transparent, traceable, and repeatable development for AI.
How does LakeFS reduce data access friction for AI teams?
LakeFS reduces data access friction by allowing users to work with remote data as if it were local, using any tool. It centralizes access permission management across all storage, ensuring that GPUs remain busy without delays caused by waiting for data. This streamlines workflows for data scientists and MLOps teams.
Source: lakefs.io