Snorkel AI Raises $350M at $3.5B Valuation as AI Data Demand Grows
Snorkel AI raises $350 million in Series E funding at a $3.5 billion valuation as demand grows for AI training datasets.
Snorkel AI has raised $350 million in a Series E funding round that values the artificial intelligence data company at $3.5 billion, nearly tripling its previous valuation of $1.3 billion from a Series D round 17 months earlier.
The round was led by Insight Partners and S32, with existing investors including Addition, Lightspeed, Greylock, GV, and Wells Fargo also participating.
Snorkel AI’s Shift Toward AI Data Services
Snorkel originally built software to automate data labelling for machine learning applications but has shifted toward providing complete datasets and simulated environments through its data-as-a-service model.
The company combines software, AI models, synthetic data generation, and subject matter experts to create training datasets for AI systems rather than operating only as a human labelling marketplace.
Growing Demand for AI Training Data
Snorkel said its annualised revenue run rate has reached $375 million, an eighteenfold increase over the past year, driven by demand from AI labs and companies looking for high-quality training data.
Other AI data companies have also reported rapid growth. Mercor has reached a reported gross annualised revenue figure of $2 billion, while Handshake reached the $1 billion milestone earlier this year. The Information reported on Handshake and Mercor’s growth as demand for human contributors involved in AI training increases.
Snorkel said its model differs from companies focused mainly on human labour because it sells datasets and reinforcement learning environments, and records expert payments as costs rather than including them in headline revenue figures.
Company Background
Snorkel AI launched commercially in 2019 after four years of research by co-founder and CEO Alex Ratner and his team at a Stanford AI laboratory.
The new funding highlights investor interest in companies building AI development infrastructure, especially tools that create the datasets modern AI systems need.
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