Enterprise software giant SAP made a significant bet on artificial intelligence on Monday, announcing its intention to acquire German AI startup Prior Labs. Pending regulatory approval, SAP plans to invest €1 billion (approximately $1.16 billion) into the business over the next four years to grow it into an AI lab focused on structured data — the tables and databases where enterprise information typically sits.

The acquisition underscores SAP's determination to stay relevant in the rapidly evolving AI landscape, especially after its stock dropped significantly in 2026 partly due to what analysts have dubbed the 'SaaSpocalypse.' The term refers to a broad downturn in software-as-a-service valuations as investors question growth prospects amid rising interest rates and maturing markets.

SAP declined to disclose how much it spent on the acquisition itself, but sources familiar with the matter told Pathfounders that this was a healthy exit: an 'almost all cash' deal, with well over half a billion dollars in cash up front for the startup's founders — Frank Hutter, Noah Hollmann, and Sauraj Gambhir.

A Focus on Structured Data

The trio co-founded Prior Labs just 18 months ago with a focus on tabular foundation models (TFMs) — AI models that can make predictions from data that sits in tables and databases. This is potentially a better fit for enterprises than large language models (LLMs), which are designed primarily for unstructured text. For SAP, whose widely used software products for accounting, HR, procurement and expense management rely on structured data, TFMs represent a natural extension of its core capabilities.

'Early on, SAP recognized 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,' said SAP CTO Philipp Herzig in a statement. This sentiment echoes observations from industry leaders: OpenAI's COO admitted last February that 'we have not yet really seen AI penetrate enterprise business processes.' The gap is not just about technology — it's about understanding how enterprises actually operate and where AI can add measurable value.

Prior Labs' TabPFN model series has experienced strong traction among developers. In a blog post announcing the deal, the startup's founders noted that its open source models have been downloaded over three million times. This community validation was likely a key factor in SAP's decision to acquire rather than build from scratch. Building a competitive TFM from the ground up would have required years of research and development, whereas Prior Labs already had a proven model and a growing user base.

Strategic Implications for SAP's AI Portfolio

The acquisition is part of a broader strategy by SAP to integrate AI across its entire product suite. The company had already developed SAP-RPT-1, a relational pretrained transformer model, and invested in several generative AI startups. In 2023, it backed OpenAI rival Anthropic as well as Aleph Alpha and Cohere, which now intend to merge to form 'a global AI powerhouse.' These investments show that SAP is hedging its bets across multiple AI approaches — from large language models to specialized structured data models.

However, the Prior Labs acquisition provides a more direct path to productization. SAP promised that Prior Labs will maintain its open source versions, stating: 'The lab will operate as an independent unit to ensure research velocity while SAP provides long-term investment and a direct path to productization across the SAP portfolio with SAP AI Core and SAP Business Data Cloud as well as the agentic layer with Joule.' This structure aims to preserve the startup's innovative culture while giving it access to SAP's massive enterprise customer base.

The investment is also a bet on European AI leadership. Founder and CEO Frank Hutter celebrated the deal in a post on X, expressing hope that Prior Labs, with this 'massive boost' from SAP, can become a new 'globally-leading frontier AI lab for structured data — in Europe, in the open.' Europe has struggled to compete with US and Chinese AI giants, but specialized areas like tabular foundation models offer a niche where European startups can excel.

The Agentic AI Dilemma

However, Germany's most valuable company also seems to be playing defense as the tech industry marches toward agentic AI — autonomous software agents that can perform tasks without human intervention. While it works to create its own AI lab, SAP has blocked OpenClaw and any other agent technology that it has not explicitly authorized. The Information was first to spot this policy shift.

In response to a request for comment, SAP's press department referred to the company's latest API policy, which does say that SAP 'prohibits' AI agents from accessing its products through its API except for those that are 'SAP-endorsed architectures.' This is a stark contrast to the approach taken by Salesforce, another incumbent caught in the SaaSpocalypse. Salesforce is allowing enterprises to choose their own agents, including OpenClaw if they so wish, with its new Headless 360 architecture.

Authorized architectures of course include SAP's own offering, Joule Agents, still in beta, which lets customers create their own agents. Nvidia also announced in March that SAP's Joule supports Nvidia's Agent Toolkit, which is software for managing agents. This toolkit is the foundation for Nvidia's enterprise-ready, security-focused method for deploying OpenClaw, NemoClaw. Hence SAP customers will be authorized to use NemoClaw agents.

Financial and Competitive Context

For a giant incumbent player like SAP, AI is both a threat and an opportunity. 'It's all about how quickly [we can] as SAP actually also embark [on] these technologies in our R&D portfolio to keep the relative economies of scale advantage,' CFO Dominik Asam told CNBC in January. The company has not been sitting on its hands, but the SaaSpocalypse has put pressure on its valuation, making bold moves necessary.

The financial details of the Prior Labs deal highlight SAP's willingness to pay a premium for AI talent and technology. Prior Labs had raised only about $9.3 million in a pre-seed funding round led by Balderton Capital in February 2025 — more than competitor Neuralk-AI but far less than Fundamental, which emerged from stealth with a $255 million Series A earlier this year. The acquisition price, while undisclosed, reportedly includes well over half a billion dollars in cash upfront for the founders, making it one of Germany's largest venture outcomes according to Balderton partner James Wise.

SAP's stock has reacted positively to the news, trading slightly upwards as of press time. Investors seem to view the acquisition as a strategic move that strengthens SAP's position in the enterprise AI market without diluting its focus on structured data.

Broader Industry Implications

The deal also signals a shift in how enterprise software companies are approaching AI. While many have rushed to integrate large language models into their products, SAP is betting that the real value lies in models specifically designed for structured data. This could set a precedent for other enterprise software vendors, particularly those in industries like finance, supply chain, and human resources, where tables and databases are the primary data format.

Moreover, SAP's restrictive agent policy creates a walled garden that could either protect customers from security risks or limit innovation. By endorsing only NemoClaw and its own Joule agents, SAP is essentially defining what 'safe' AI looks like within its ecosystem. This could become a competitive advantage if customers value security over flexibility, or a liability if developers and enterprises demand more openness.

The company plans to continue developing Prior Labs' models at its new AI lab in Freiburg, Germany, while integrating them into SAP's cloud platforms. The goal is to create TFMs that can grab data from tables, combine it with natural language understanding, reasoning, and domain knowledge. This hybrid approach could unlock new use cases in areas like financial forecasting, inventory optimization, and HR analytics.

Source: TechCrunch News