Snorkel AI Raises $350M Series E at $3.5B Valuation as AI Training Data Demand Booms

Snorkel AI has raised a $350 million Series E at a $3.5 billion valuation. The round was led by Insight Partners and S32, with existing investors including Addition, Lightspeed, Greylock, GV, and Wells Fargo also participating.

The new valuation is nearly triple the $1.3 billion valuation Snorkel reached when it raised a $100 million Series D 17 months earlier.

Snorkel helps AI labs and corporations build training datasets and simulated environments. The company says its current annualized revenue run rate is $375 million, an eighteenfold increase over the last 12 months.

That growth is tied to strong demand from AI labs for high-end training data, a market where reliable datasets and reinforcement learning environments are increasingly central to model development.

Snorkel originally provided software for automating data labeling, but last year shifted toward delivering completed datasets through an offering it calls data-as-a-service. Instead of operating purely as a human expert marketplace, Snorkel uses a hybrid approach that combines software, models that generate synthetic data, and subject matter experts.

The company says payments to human experts are accounted for in its cost of goods sold rather than in annualized revenue figures, because it sells reinforcement learning environments and complete datasets rather than human labor.

Other data companies positioning themselves as AI data labs have also reported rapid growth. TechCrunch notes that Mercor’s gross annualized revenue has climbed to $2 billion, Handshake hit a $1 billion milestone earlier this year, and Micro1 scaled to $500 million.

Those headline gross figures require context: companies in this category pay roughly 60% to 70% of top-line income directly to domain specialists, meaning actual net annual revenue is substantially lower.

Snorkel launched commercially in 2019 after four years of research by co-founder and CEO Alex Ratner and his team at a Stanford AI lab. The company is seven years old and is now leaning into demand for finished datasets and AI training environments.

The takeaway: Snorkel’s round reflects how AI infrastructure investment is extending beyond compute and models into the data pipelines that help train and evaluate them.