SAP completed its acquisition of Prior Labs on July 17, according to a company announcement, closing a deal first disclosed in May. The German enterprise software giant will invest more than €1 billion over the next four years to grow the Freiburg-based startup into what it calls a globally leading frontier AI lab focused on structured business data.
From research project to acquisition target
Prior Labs was founded by Frank Hutter, Noah Hollmann and Sauraj Gambhir, with a team that includes former researchers from Google, Apple, Amazon, Microsoft and CERN. The company built TabPFN, an open-source foundation model designed to analyze tabular data — the spreadsheets and database tables that run most business software, rather than the free text large language models are built for. The underlying research was published in the journal Nature and, according to SAP, has set state-of-the-art results across hundreds of independent academic studies. TabPFN has been downloaded more than 3 million times, and its newest version, TabPFN-2.6, tops TabArena, a leading benchmark for tabular models, matching what SAP says a four-hour automated machine-learning pipeline can do in a single, near-instant query.
Betting on structured data, not just chatbots
“SAP recognized that the greatest untapped opportunity in enterprise AI wasn’t large language models; it was AI built for structured data,” SAP Chief Technology Officer Philipp Herzig said in the announcement. Prior Labs CEO Frank Hutter said joining SAP “gives us the resources, data environment and customer reach to take this category to its full potential.”
Prior Labs will keep operating as a distinct unit inside SAP rather than being absorbed into an existing product line. The move follows SAP’s acquisition of data-platform company Dremio on July 6 and a €3.5 billion Eurobond placement in late May, part of a broader spending push into owned AI research capacity.
The deal puts SAP among a growing set of enterprise software vendors investing directly in the AI models that sit closest to their customers’ own data, arguing that structured business data — not conversational chatbots — is where enterprise AI spending will ultimately concentrate. Open-source tools like TabPFN have already found wide use among data scientists working outside any single vendor’s platform, which SAP will now need to balance against its commercial roadmap for the technology.