Analyst(s): Keith Kirkpatrick
Publication Date: July 22, 2026
SAP completed its acquisition of Prior Labs and committed more than €1 billion to tabular model research. The investment targets better predictions from the structured data that supports business operations.
What Is Covered in This Article:
- SAP completed its acquisition of Prior Labs, which will continue operating independently.
- SAP will invest more than €1 billion over four years to expand Prior Labs into a frontier AI lab.
- TabPFN-2.6 analyzes structured data and matches the accuracy of a four-hour AutoML pipeline instantly.
- SAP plans to bring Prior Labs’ research into SAP AI Core, SAP Business Data Cloud, and Joule.
- Dremio, Reltio, and SAP Knowledge Graph provide the broader data foundation for using TFMs across business processes.
The News: SAP completed its acquisition of Prior Labs, a German company specializing in tabular foundation models (TFMs). Prior Labs will remain independent, while SAP will invest more than €1 billion over the next four years to develop it into a frontier AI lab focused on structured business data.
SAP plans to bring Prior Labs’ research into SAP AI Core, SAP Business Data Cloud, and the Joule agentic layer. Prior Labs’ open-source TabPFN technology has recorded more than 3 million downloads, while TabPFN-2.6 ranks first on the TabArena benchmark for TFMs.
SAP Completes Prior Labs Acquisition to Advance Structured Data AI
Analyst Take: The Prior Labs acquisition targets a weakness in SAP’s model strategy: language models cannot reliably handle the tables, numbers, and statistical relationships that support business decisions. SAP’s applications manage finance, procurement, human resources, supply chains and customer operations, making structured data central to the value of its software. Prior Labs gives SAP models designed to predict payment delays, supplier risks, customer churn and upsell opportunities from that information. SAP has already developed SAP-RPT-1, so it must use this acquisition to accelerate deployment rather than duplicate research. SAP has picked the right technical problem, but the €1 billion commitment now requires measurable results inside customer workflows.
Language Models Are Not Enough for SAP’s Core Data
Large language models have only a rudimentary understanding of tables, numbers, and statistics, making them unsuitable for predictions that require mathematical accuracy. Prior Labs designed TFMs to learn statistical relationships directly from structured records, while in-context learning allows users to receive predictions without training a separate model for every task. TabPFN-2.6 currently leads the TabArena benchmark and can instantly match the accuracy of a four-hour automated machine learning pipeline through a single model. Its meaningful competitors are AutoGluon, H2O, XGBoost, and established AutoML pipelines, not general-purpose conversational tools. SAP is right to stop treating one type of model as the answer to every problem and use TFMs for the data its applications actually manage.
SAP Must Turn Research Into Products
Prior Labs has established technical credibility in two years, with more than 3 million TabPFN downloads and a model series published in Nature. The company raised one €9 million pre-seed round led by Balderton Capital approximately 15 months before the acquisition agreement, while SAP will now invest more than €1 billion over four years. Prior Labs will remain independent under co-founders Frank Hutter, Noah Hollmann, and Sauraj Gambhir, with Yann LeCun and Bernhard Schoelkopf serving on its scientific advisory board. SAP will maintain the open-source strategy while creating a direct product route through SAP AI Core, SAP Business Data Cloud, and Joule, where users could select datasets, ask questions, and run what-if scenarios without specialist machine learning skills. Benchmark rankings and download figures justify SAP’s interest, but only improvements in prediction accuracy, speed, and usability will justify the size of its investment.
Dremio Gives Prior Labs Access to Distributed Data
Prior Labs can interpret structured data, but its models will have limited value if customers must first move that information through slow and fragile extract, transform, and load pipelines. Dremio separates storage from compute and uses Apache Iceberg to let different engines access the same data without unnecessary duplication, while Apache Polaris governs Iceberg tables across different environments. Third-party operational data can remain in its original cloud storage location while Dremio makes it accessible within SAP, removing the need to force every dataset into a proprietary structure. SAP’s March 2026 acquisition of master data management specialist Reltio adds another layer of control over the information feeding these models. Dremio, Reltio, and Prior Labs give SAP the pieces to access, govern, and interpret distributed business data, but SAP must connect them instead of leaving that work to customers.
Joule Needs Predictions It Can Act On
SAP Knowledge Graph can map business relationships, organizational hierarchies, and regulatory classifications, while Joule can use that context to interpret predictions and initiate workflows. According to Futurum Group’s 1H 2026 Enterprise Software Decision Maker Survey Report, 65.9% of buyers favor a mostly platform-based model with point solutions. That preference supports SAP’s decision to connect Prior Labs with SAP AI Core, SAP Business Data Cloud, Dremio, Reltio, and Joule rather than sell another isolated analytical tool. Buyers should still demand complete examples showing the source data, prediction, explanation, recommended action, and resulting business outcome. SAP now has enough technology to deliver useful predictive workflows, so a weak customer experience would reflect an integration failure rather than a missing capability.
What to Watch:
- How quickly can SAP bring Prior Labs’ models into SAP AI Core, SAP Business Data Cloud, and Joule while preserving the lab’s independence?
- Can TabPFN-2.6 reproduce its TabArena performance when predicting payment delays, supplier risks, churn, and upsell opportunities inside SAP applications?
- SAP should explain where Prior Labs’ models complement SAP-RPT-1 and which technology will support each business workload.
- Will users receive reliable predictions and run what-if scenarios without requiring specialist data science or machine learning expertise?
- How effectively will SAP combine Dremio, Reltio, SAP Knowledge Graph, and Prior Labs within complete customer workflows?
See the complete announcement on SAP’s acquisition of Prior Labs on the SAP News Center.
Declaration of generative AI and AI-assisted technologies in the writing process: This content has been generated with the support of artificial intelligence technologies. Due to the fast pace of content creation and the continuous evolution of data and information, The Futurum Group and its analysts strive to ensure the accuracy and factual integrity of the information presented. However, the opinions and interpretations expressed in this content reflect those of the individual author/analyst. The Futurum Group makes no guarantees regarding the completeness, accuracy, or reliability of any information contained herein. Readers are encouraged to verify facts independently and consult relevant sources for further clarification.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
