lakeFS, the leading “git-for-data” version control system for enterprise data and AI initiatives, has raised $20 million in a growth funding round. With thousands of organizations including Arm, Bosch, Lockheed Martin, NASA, Volvo, and the U.S. Department of Energy already using lakeFS as part of their data management infrastructure, this new investment will accelerate its growth supporting data engineering, AI and ML projects in the enterprise and public sector markets. The funding round brings the company’s total raised capital to $43 million, and was led by Maor Investments along with existing investors Dell Technologies Capital, Norwest and Zeev Ventures.
“We’re still at the very beginning of the AI revolution and organizations struggle to unlock value and business efficiencies using AI,” said Dr. Einat Orr, co-founder and CEO of lakeFS. “Enterprises are adopting lakeFS as an infrastructure layer in their data and AI operations to reduce time-to-market on their AI initiatives while increasing data and model quality. This is more important than ever because the organizations that innovate fastest will be the ones that win. This funding will allow us to double down on innovation – particularly around features critical to enterprise-scale AI operations.”
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Closing the AI data infrastructure gap
Organizations are racing to obtain value and a competitive edge through artificial intelligence (AI) and machine learning (ML) initiatives. Yet, according to the most recent EY Survey on AI Adoption, the vast majority (83%) of surveyed executives said “AI adoption would be faster if they had a stronger data infrastructure, and 67% say they could move faster on AI adoption, but the lack of data infrastructure is holding them back”.
There is a huge and growing gap between data’s increasing importance and an organization’s ability to manage it with confidence and control. As AI, MLOps and data teams are building the AI infrastructure for their organization in real time training proprietary LLMs, building agents and agentic workflows, they are wasting precious time and resources wrangling the data that powers the AI transformation.
This often requires manually juggling copies of petabyte-sized datasets, building sub-datasets for experiments or specific projects, or reproducing the data that was used to train a specific LLM for research, compliance or training purposes. As a result, AI projects are delayed, are much more costly, open organizations up to compliance risks and deliver overall disappointing results.
Enterprise data version control: A foundational layer for AI infrastructure
Just as Git revolutionized software development, lakeFS is reshaping enterprise AI by versioning the data that powers it. Designed for massive volumes of unstructured, semi-structured and structured data in data lakes—text, images, audio, video—lakeFS gives organizations control, safety, and reproducibility at scale.
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