On September 22, 2026, Snorkel AI announced a $350 million Series E at a $3.5 billion valuation. Insight Partners and S32 co-led the round. New investors include Third Point Ventures, March Capital, Blumberg Capital, Allegis Capital, Standard and Frontline, and existing backers Addition, Lightspeed, Greylock, GV, Prosperity7, Wells Fargo, Walden Catalyst and Factory also took part.
In a post announcing the round, co-founder and CEO Alex Ratner writes that since Snorkel launched its data-as-a-service offering nearly a year earlier the company has grown more than 18x and crossed an annualized revenue run rate of $375 million. He describes customers as frontier labs, hyperscalers, “neolabs,” vertical AI companies, enterprises and U.S. government agencies. The company says it will use the money to expand its “data factory,” invest in vertical and enterprise AI, extend research into new domains and modalities, and fund open research such as its Open Benchmarks Grants.
Snorkel began as a Stanford research project on weak supervision - labeling data programmatically instead of by hand - and spent years selling labeling software to enterprises. Its turn to delivering finished datasets, reinforcement-learning environments and evaluations for model builders follows where the money in AI data now sits: post-training and evaluation for frontier models, a market also served by companies such as Scale AI.
What the announcement does not show: the run-rate figure is Snorkel’s own annualized metric, not audited revenue, and the company does not name its lab customers, give customer concentration, or disclose margins. Ratner’s “Data 2.0” framing is a positioning argument, not evidence that expert-built data will keep commanding these prices as labs automate more of their own data generation.