Prior Labs funding news – Freiburg-based Prior Labs Secures €9 Million in Pre-Seed Funding

The funding round was led by Balderton Capital along with XTX Ventures, SAP founder Hans Werner-Hector’s Hector Foundation, Atlantic Labs, and Galion.exe. Prominent AI angel investors such as Peter Sarlin (Founder & CEO, Silo AI), Thomas Wolf (Founder & CSO, Hugging Face), Guy Podjarny (Founder, Snyk & Tessl), Ed Grefenstette (Director, DeepMind), Robin Rombach (Founder & CEO, Black Forest Labs), Chris Lynch (Founding Investor Data Robot & CEO, AtScale), Ash Kulkarni (CEO, Elastic) and other business leaders also participated.

Frank Hutter, Co-founder and CEO of Prior Labs said: “Most of the world’s critical decisions are powered by tabular data, yet tools to analyse it are outdated and lacking. We’re bringing a quantum leap to the predictions that businesses can make from their most valuable data and building a future where engaging with tables is as seamless as using AI for text or images. We can deliver faster, more accurate predictions that empower businesses to do more with less.”

With over 20 years of experience in machine learning, Hutter’s team used their expertise to create an advanced model for tabular data called TabPFN.

Now, Prior Labs is taking this academic work to the next level by adding their API to business data systems, helping companies make the most of their tabular data.

Tabular data—like data in tables, spreadsheets, and databases—plays a key role in industries such as healthcare, finance, environmental monitoring, and manufacturing.

Even though tabular data is important, its analysis has not advanced as quickly as AI for text and images. Issues like messy, varied, and specific data have left businesses using outdated tools or expensive, custom machine learning models for each task, according to Prior Labs.

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TabPFN, trained on 130 million synthetic datasets, is designed to quickly understand and find patterns in any data without needing task-specific training. As a foundation model, it can also be fine-tuned with a company’s own data, making it more accurate and adaptable.

In a recent study, TabPFN showed it was more accurate than other top models in 96% of cases with small tabular data. It needs only half the data to reach the same accuracy as the next best model, and it takes just 2.8 seconds to outperform models that take hours.

In fields like healthcare, medicine, and climate science, where data is hard and expensive to get, TabPFN can deliver results using 50% less data.

Recent updates include support for text data, the ability to fine-tune using a company’s own data, and the option to add extra context to improve predictions making it more accurate and easier to use.

James Wise, Partner, at Balderton Capital, said: “Tabular data is the backbone of science and business, yet the AI revolution transforming text, images and video has had only a marginal impact on tabular data – until now. Prior Labs’ breakthrough gives everyone the super-powers of machine learning without needing to train their own models on their own data. We’re thrilled to support this world-class team as they redefine how industries unlock the value of their data.”

About Prior Labs

Prior Labs is leading a new era in tabular machine learning. It was founded in late 2024 by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, with advisors Bernhard Schölkopf and Alex Diehl.

The company’s TabPFN model builds on academic research to provide real-world benefits and help more companies. Prior Labs’ models offer fast, accurate, and efficient solutions that will change how businesses use their most important and complex data to gain insights.