Berlin-based AI lab Prior Labs has been acquired by SAP for over $1 billion, just 18 months after founding. The deal funds frontier research in tabular foundation models for enterprise data.
In a move that has sent ripples through the European tech scene, Berlin-based frontier AI lab Prior Labs has been acquired by multinational software giant SAP. The deal, backed by more than $1 billion in investment from SAP, is set to fund infrastructure, hiring, and long-term frontier research. It's a stunning exit for a company that was just a research project 18 months ago.
### The Backstory: From Research Project to Billion-Dollar Exit
Prior Labs was founded in 2024 by Frank Hutter, Noah Hollmann, and Sauraj Gambhir. Their focus? Tabular foundation models (TFMs), a category of AI specifically built for enterprise data. Think of it as a Swiss Army knife for structured data: instead of training a separate AI model for every dataset, TabPFN—their flagship model—uses a single pre-trained foundation model to solve prediction tasks like payment delays, churn, supplier risk, and demand forecasting.
The company had already raised a $9 million pre-Seed round in 2025, but this acquisition catapults them into a whole new league. "Eighteen months ago, Prior Labs was a research project," says Frank Hutter, co-founder and CEO. "Today we're beginning our next chapter as an AI lab with the resources to tackle problems we simply couldn't before."
### Why SAP Made This Move
SAP's acquisition of Prior Labs isn't just about buying a cool startup—it's a strategic bet on the future of enterprise AI. "Early on, SAP recognised that the greatest untapped opportunity in enterprise AI wasn't large language models; it was AI built for the structured data that runs the world's businesses," adds Philipp Herzig, CTO of SAP.
This deal follows substantial investment in the European infrastructure needed to train, deploy, and govern enterprise AI. To put it in perspective, EU-Startups’ 2026 coverage includes approximately $1.93 billion across comparable and adjacent companies. That's led by Nscale’s $1.7 billion AI-compute round and Verda’s $100 million financing, alongside investments in enterprise data, memory, and ERP infrastructure at Conduct, OpsMill, and Modern Relay.
### What This Means for Prior Labs
Prior Labs will continue under its own brand, leadership, research agenda, and customer relationships. They'll still publish their research and make their models openly available, but now with SAP's deep pockets and enterprise reach. That's a game-changer for an 18-month-old company.
Here's what changes:
- **Access to enterprise data environments:** Prior Labs can now train on real-world data from SAP's massive customer base.
- **Long-term investment:** They can pursue multi-year frontier research programs that would have been out of reach otherwise.
- **Deployment at scale:** Their models can be deployed across industries where enterprise data carries the most value.
### Real-World Impact Already
Prior Labs' technology isn't just theoretical. It's already helping prevent train failures with Hitachi, improving financial forecasting with TD, and has been applied across hundreds of published research projects—from pancreatic cancer diagnosis to wildfire prediction to next-generation battery materials.
### The Bigger Picture
Germany has produced several direct comparisons to Prior Labs, including Berlin-based SPREAD, Cognee, and Qorelo, as well as Stuttgart’s Blockbrain and Munich’s Interloom. Including SAP’s commitment to Prior Labs, the activity referenced represents more than $2.93 billion in total.
This acquisition is a clear signal that enterprise AI is moving beyond the hype of large language models and into practical, data-driven applications. For Prior Labs, it means they can now pursue "moonshots" in medical data and material sciences—problems that could genuinely change the world.
In short: an 18-month-old startup just proved that if you build something truly useful, the big players will come knocking. And with $1 billion behind them, Prior Labs is now positioned to define the category of tabular foundation models for years to come.