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lakeFS Acquires DVC, Uniting Data Version Control Pioneers to Accelerate AI-Ready Data

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lakeFS Pricing

Discover how lakeFS empowers your team with data version control.
Choose the right plan to maximize impact, efficiency, and return on investment

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Compare Features

Compare all the features included in lakeFS and find
the plan that best suits your business needs.

Most Popular
Open Source

Free forever
Most Popular
Enterprise

Unlimited seats

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

Role-Based Access Control (RBAC)

Single Sign On (SSO)

SCIM Support

IAM Roles

Mount Capability

Audit Logs

Transactional Mirroring

Iceberg REST Catalog

Metadata Search

Multiple Storage Backends Support

Simplified Garbage Collection (Managed or Standalone)

SOC2

Support SLA
Run locally
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Frequently Asked Questions

What Cloud providers does lakeFS support?

lakeFS Cloud currently supports AWS, Azure and GCP.

What integrations does lakeFS currently support?

lakeFS is fully compatible with a wide ecosystem of data engineering tools and technologies including Spark, Delta Lake, AWS CLI, Airflow, DuckDB, Python, Kubeflow, Airbyte, dbt, Iceberg, and Kafka. Get a full list of all the lakeFS integrations here.

Does my data stay in place? Will it leave my VPC?

Your data and metadata will always be stored on your VPC. lakeFS manages metadata: Pointers to the locations of the files in your buckets per commit, which also sits inside your buckets.

How can lakeFS connect securely to my data?

You can configure a private link connecting your VPC with lakeFS Cloud, providing private connectivity between your VPC and lakeFS Cloud, without exposing your traffic to the public internet.

Got additional billing questions?

For more details on the different pricing plans with lakeFS, get in touch by sending an email to [email protected].

Trusted by

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

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.

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.

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

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.

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.

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