# lakefs

> Publicly sourced profile assembled by Optimly, in the Optimly AI Brand Index. Last refreshed 2026-10-04.

- **Business Profile:** https://optimly.ai/brand/lakefs
- **Dataset:** https://optimly.ai/brand/lakefs.json
- **Markdown:** https://optimly.ai/brand/lakefs/llms.txt
- **Official website:** https://lakefs.io
- **Slug:** lakefs
- **Categories:** data-analytics-business-intelligence

## Overview

- **Tagline:** A highly scalable data version control architecture that bridges the AI infrastructure gap, enabling compliance by design, ensuring data quality, making training and agent runs reproducible, and reducing data access friction. It allows testing pipeline and model changes in isolation on production data with zero data copy, and versions data alongside code and models for end-to-end reproducibility.
Source: Brand's website (https://lakefs.io) · 2026-10-04

- **Description:** Lakefs provides a control plane for AI-ready data, built on a highly scalable data version control architecture. It manages the data lifecycle, provenance, and unified access for AI and data engineering teams, enabling compliance, data quality, reproducibility, and reduced data access friction across various data workloads.
Source: Brand's website (https://lakefs.io) · 2026-10-04

## Offerings

- **Core capabilities:**
  - lakeFS
  - a data version control and management platform acting as a control plane for AI-ready data. It offers capabilities for data lifecycle management
  - provenance tracking
  - unified data access
  - compliance
  - data quality assurance
  - and reproducible AI/ML training and agent runs.
Source: Brand's website (https://lakefs.io) · 2026-10-04

- **Pricing:** Not specified on the provided page.
Source: Brand's website (https://lakefs.io) · 2026-10-04

## Audience

- **Target industry:** AI and data engineering teams, ML engineers, data scientists, and organizations across various industries (e.g., automotive, aerospace, technology) seeking to manage, govern, and ensure the quality and reproducibility of their data for AI and machine learning projects.
Source: Brand's website (https://lakefs.io) · 2026-10-04

Page last updated 2026-10-17.