Bridging the AI infrastructure gap

The Control Plane for
AI-Ready Data

Built on our highly scalable data version control architecture, lakeFS manages the data lifecycle, provenance, and unified access for AI and data teams

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Building AI Factories at Enterprise Scale

Discover how to accelerate AI delivery, ensure reproducibility, reduce data friction, and support compliance

Ensure 
Data Quality

Make 
Training Reproducible

Reduce 
Data Access Friction

Curious how lakeFS can help you deliver AI projects faster and more efficiently?

CASE STUDY:

How Arm Powers Its Data Management
Infrastructure with lakeFS

With lakeFS, Arm implemented automated data cleaning, avoided costly data duplication, streamlined engineering workflows, and established a robust governance framework to manage data across distributed teams. The result: faster go-to-market, reduced storage costs, improved development velocity, and stronger data governance. Read full case study

Faster  
go-to-market

Improved development velocity

Reduced
storage costs

Stronger data governance

lakeFS saved us from the analysis paralysis of overthinking how to test new software on our data lake at Netflix scale. In less than 20 min I had lakeFS up and running, and was able to run tests against my production data in isolation and validate the software change thoroughly before pushing to production. With lakeFS, we improved the robustness and flexibility of our data systems

Holden Karau,
Open Source Engineer

With lakeFS, we have streamlined data science and MLOps workflows, adapted data access controls for different teams, accelerated productivity and reduced time-to-insight for ML engineering projects.

Leonard Aukea
Head of ML Engineering & Operations

Transparent, traceable and repeatable development of AI is critical to us. What’s important for Lockheed Martin is that we don’t just focus on what we’re building but also on the how.

Greg Forrest
Director of AI Foundations

lakeFS allows managing versions for any type of feed. Some files are tabular; some are not. Tracking feeds in lakeFS is pretty fast.

Vara Ghanta
Principal Software Engineering Manager

Moving to a data branching solution has paid off quickly for us. A few days after completing the migration, we’ve already reduced testing time by 80% on two different projects. And we’re excited to see how data branching increases our product velocity.

Ryan Green
CTO

With lakeFS we can easily achieve advanced use cases with data, such as running parallel pipelines with different logic to experiment or conduct what-if analysis, compare large result sets for data science and machine learning, and more.

Stephen Seewald,
Raghvendra Verma,
Cory Matheson

It used to take our entire ML engineering team 2 weeks to launch 2-3 new models. After implementing lakeFS, we now launch 6 new models in the same time with half the team.

David van Son,
Software Engineer