Snorkel AI, a startup founded by researchers from the Stanford AI Lab, has reached a $3.5 billion valuation after announcing a major funding round. The company was established in 2019 and initially focused on helping organizations label and curate training data for machine learning models. Its business model shifted in September 2025 when it launched a data-as-a-service offering that delivers ready-to-use datasets directly to customers.
The Stanford AI Lab spinout raised $350 million as demand for specialized training data and AI environments continues to surge.
The new service helped push Snorkel’s annualized revenue run rate from about $20 million a year earlier to more than $350 million. Snorkel said on September 22 that the figure had crossed $375 million. The company now serves frontier AI labs and hyperscalers as well as enterprise customers and U.S. government agencies.
CEO Alex Ratner described the platform as an ‘agentic data development platform’ that uses AI agents to scale data creation and validation.
Snorkel raised $350 million in its latest funding round led by Insight Partners and S32. Total funding has now reached about $585 million across its funding rounds. Ratner told Reuters that Snorkel expects to become profitable in 2026.
Meanwhile Southeast Asia’s AI funding landscape has seen a sharp rise in large investments after Kling AI raised a $2.8 billion Series D round. This round accounted for about 68% of the region’s native AI funding through July 2026.
Singapore is by far the largest fundraising hub for native AI companies in Southeast Asia but smaller amounts have also been recorded in other countries.
The funding pattern shows investors are focusing on infrastructure needed to build and run AI systems rather than spreading investments broadly. Sectors like logistics technology and autonomous vehicles have also attracted significant investment in the region.
Snorkel’s latest valuation is nearly three times the $1.3 billion valuation it received during a $100 million funding round in May 2025. Its rapid growth reflects increasing demand for specialized datasets and reinforcement-learning environments used to train advanced AI systems.