Berlin AI lab Prior Labs acquired by SAP for €1 billion investment 18 months after launch
Berlin-based frontier AI lab Prior Labs has been acquired by SAP in a deal backed by over €1 billion in investment. The company, founded in 2024 and specializing in tabular foundation models for enterprise data, will continue to operate under its own brand with independent leadership and research agenda.
Prior Labs, a Berlin-based frontier artificial intelligence lab, announced today that SAP has completed its acquisition, supported by more than €1 billion in investment from the software giant. The funds will be used to finance infrastructure, hiring, and long-term frontier research.
The company will continue under its own brand, leadership, research agenda, and customer relationships. It will also publish its research and make its models openly available, with SAP's backing. This follows Prior Labs' €9 million pre-Seed round in 2025.
Founded in 2024 by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, Prior Labs pioneered tabular foundation models (TFMs)—a category of AI purpose-built for enterprise structured data. Instead of training separate AI models for each dataset, its TabPFN model uses a single pre-trained foundation model to handle prediction tasks such as payment delays, churn, supplier risk, and demand forecasting.
The technology is already helping prevent train failures with Hitachi, improving financial forecasting with TD, and has been applied in hundreds of research projects, from pancreatic cancer diagnosis to wildfire prediction and next-generation battery materials.
Philipp Herzig, CTO of SAP, noted that the biggest untapped opportunity in enterprise AI is not large language models but AI built for structured data. He said Prior Labs has defined the TFM category and built the world's strongest research team in this area, topping public benchmarks since its inception.
The acquisition enables Prior Labs to pursue multi-year frontier research programs that would have been out of reach for an 18-month-old company. Access to enterprise data environments and long-term investment will allow it to tackle ambitious projects in medical data and material sciences, among others.
