The Control Plane for AI-Ready Data
lakeFS gives tools, users, and AI agents fast, governed access to petabyte-scale multimodal data. It eliminates unnecessary data copies, removes operational bottlenecks, and provides complete auditability.
Reduce data access friction
Get centralized access to distributed data across formats, from images to log files to structured tables, and work with it as if it were local.
Ensure data quality
Test changes in isolated, zero-copy environments, with automated checks and human review before anything merges. And if you need to, roll back instantly.
Make training and agent runs reproducible
Every experiment, training run, and agent action is tied to an immutable data version tracked alongside code and models, so any past result can be re-created, debugged, or built on.
Compliance and governance by design
Trusted By:
Bridging the AI Data Infrastructure Gap
A light infrastructure layer
lakeFS drops into your existing stack and sits between your storage and the tools, users, and agents that work on your data.
Zero data copies
Allows you to test, and sandbox without moving or copying data.
Built for Multimodal data
AI runs on multimodal data, not just tables. lakeFS gives you a single interface to manage documents, images, audio, video, logs, JSON, and open table formats like Iceberg and Delta Lake across any storage backend.
Powered by enterprise-scale data version control
Under the hood, lakeFS is powered by a highly scalable data version control engine that manages data the way code is managed. Git-like branches, commits, merges, and rollbacks bring software engineering best practices to data, AI, and ML work: safe development and testing, early error detection, and reproducible results. Because branches are zero-copy, creating an isolated environment on a petabyte-scale repository takes seconds and adds nothing to storage costs.
Data Infrastructure for Enterprise AI
Reduce data access friction
Reduce data access friction
Get data to the people, tools, and agents that need it, without copies, migrations, or code changes.
- Zero-copy branches give teammates or agents a full working sandbox of any dataset in seconds, without the cost or complexity of duplication
- Mount a versioned subset as a local filesystem and use it in notebooks, scripts, or any tool - no code changes or expensive data clones required
- One repository for any data type - images, video, audio, logs, JSON documents, and Parquet files, versioned together
- Works with the tools you already use, including Spark, Trino, Airflow, SageMaker, Vertex AI, MLflow, or any other S3 client
- Multiple storage backends let you manage data across the cloud incl. AWS S3, Azure Blob, Google Cloud Storage, or on-prem object stores such as MinIO from a single lakeFS instance - no migrations or consolidation required
IMAGES
VIDEO
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Ensure data quality
Catch bad data before it reaches production, and recover in seconds when something slips through.
- Write-Audit-Publish workflows gate every change: automated hooks validate data and enforce data contracts, while Pull Requests for data add human review, so bad data never reaches downstream consumers
- Test on production data in isolation with zero-copy branches that let teams and agents work in isolation without the cost, risk, or complexity of making copies
- Recover instantly with rollback: if bad data lands, revert to a known-good state in a single command - no waiting to diagnose and rebuild
Make AI training and agent runs reproducible
Tie every result back to the exact version of data that produced it, so any experiment, training run, or agent action can be reproduced or built on.
- Version data alongside code and models for end-to-end reproducibility
- Tagging binds any model, report, or audit to a named data version
- Built-in lineage tracking traces every output back to the exact inputs that produced it
- Immutable commits permanently record every data state, so it is always recoverable
- Object and content-level diff, including image comparison, shows exactly what changed between versions
Enable compliance and governance by design
Audit trails, lineage, and access controls are built in by design.
- Full traceability and auditability build a trail of every data action, so you know who accessed what data, when, and why
- Full version history gives you a complete, queryable record of every change, so you can inspect, diff, and restore any prior dataset state
- Metadata search works fast across petabyte-scale datasets, surfacing data audit gaps
- Role-based access control, geo-based policies, and data immutability keep data viewable and editable only by the right people, tools, and agents
- Preventive data controls reduce compliance risk and simplify regulatory audits with built-in evidence, not manual reporting
Enterprise scale
Battle-tested in some of the most demanding environments and organizations around the world, managing petabytes of data.
- Perform at enterprise scale: in-memory metadata delivers sub-second branch and commit operations, even on billions of objects, while asynchronous commits land hundreds of thousands of objects in a single operation
- Enterprise IAM: SSO and SCIM plug into your identity stack
- SOC 2 compliance accelerates security review with the certifications enterprise buyers require
- Flexible deployment: on-premises, public and private clouds, government clouds (AWS GovCloud, Azure Government), and air-gapped environments - no migration, no lock-in, no rewrites
- Data stays in place, under your control, with no copying or duplication
Built for Teams Driving Enterprise AI
Teams training models
ML platform and data science teams run experiments and train models on reliable, reproducible data, without slow and expensive data duplication.
Agentic AI initiatives
AI agents get isolated, governed, and reproducible access to enterprise data, with the same controls as human workflows.
The people behind the data
Data engineers, data scientists, and ML engineers work on shared data without getting in each other’s way, through the interfaces they already use.
Accelerate Enterprise AI
Deliver AI faster
Teams and agents get instant, governed access to the data they need, so experiments and training runs start in seconds instead of waiting days for data copies.
Cut storage and infrastructure costs
Zero-copy branches let everyone work from the same data without duplicating it, so you stop paying to copy petabytes for every project and environment.
Pass audits with confidence and less effort
Every data change is already recorded with full lineage, so compliance means pulling existing history, not reconstructing it under a deadline.
Trust what you ship
See lakeFS in Action
Uses lakeFS to power its AI factory, scaling AI with cross-team collaboration, low costs, and strong compliance.
Solves reproducibility issues while cutting data duplication and supporting FDA compliance.
