Choose the plan that best meets your business needs
$0 / Free forever
Best for teams looking to
speed up development and deployment cycles and do
not need additional features
or customer support.
$2900 / month
Best for teams using
managed SaaS solutions and
need RBAC, auditing, SSO, support and managed
Get in touch for an estimate
Best for teams in need of enterprise solutions like
support SLA and customer support services, and are
not working in the Cloud.
Compare all the features included in lakeFS and find
the plan that best suits your business needs.
$0 / Free forever
$2900 / month Unlimited seats
Format-agnostic data version control
Data stays in one place
Managed Service (auto updates,auto scaling, disaster recovery, etc.)
Managed Garbage Collection
Frequently Asked Questions
lakeFS Cloud currently supports AWS, Azure and GCP.
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.
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.
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.
For more details on the different pricing plans with lakeFS, get in touch by sending an email to firstname.lastname@example.org.
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.
Open Source Engineer
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.
The cloud never warned us about the data getting clouded. As the blessing of infinite storage quickly became an unmanageable mess, there is a need for technologies like lakeFS to make data accessible again
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
Since introducing lakeFS to our production data environment, we’ve enjoyed the benefits of atomic and isolated operations in our data pipelines. This has allowed us to spend more time improving other aspects of our data platform, and less time dealing with the fallout from race conditions and partially failed operations
Data Platform Team Lead
By using lakeFS we produce a commit history on the production branch that easily allows for rollbacks. In the case of data quality issues in production, this allows us to simply revert to the previous high quality snapshot of our data.
Big Data R&D Team Lead
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