Applied Compute hits $100M run rate and raises $350M at $3.25B, led by Elad Gil

What's the deal? Applied Compute, the San Francisco AI cloud founded by former OpenAI researchers Yash Patil, Rhythm Garg and Linden Li, says it has crossed a $100 million annualized revenue run rate less than 18 months after founding. On 2 November 2026 it announced $350 million in new funding at a $3.25 billion valuation, led by Elad Gil.

Who's in the round? Alongside Gil, participants named by the company include Kleiner Perkins, Lux Capital, Hanabi Capital, NVIDIA, Blackstone, Shopify Ventures, HubSpot Ventures, Diffusion, Definition, Timeless, Layer Global, Neo, Abstract, Nova, Quantum Light, First Harmonic and Swish. The prior disclosed raise was $80 million in April 2026 at a $1.3 billion valuation, led by Kleiner Perkins.

Context The company's bet is that enterprises would rather own and improve the AI they run on, post-training open-weight models on their own data rather than renting frontier APIs. Its AC2 Agent Cloud combines training, inference and continual improvement. Applied Compute says 40% of customers now use it for both training and inference, AC Inference serves more than 1 trillion tokens a day, and customers have completed more than 1,700 training runs on AC2 since July. Named customers include Microsoft, Shopify, Mercor, Harvey, DoorDash, Cognition, Clay and Decagon. Enterprise customer count has more than doubled since the last raise; the team has tripled since January and is about 85% researchers and engineers.

Why it matters Crossing nine-figure annualized run rate before a second birthday, then pricing at roughly 32x that run rate, puts Applied Compute among the fastest AI-infrastructure climbs of 2026 and underscores demand for custom open-model post-training plus owned inference.

Source Applied Compute company blog (2 Nov 2026); @appliedcompute on X; Yash Patil on X; SiliconANGLE

Image: Applied Compute founders (via SiliconANGLE); secondary team photo from company X post