❯ Applied Compute raises $350 million at a $3.25 billion valuation after reaching a $100 million annualized revenue run rate

Funding and revenue disclosed togetherApplied Compute announced $350 million in new funding on November 2 at a $3.25 billion valuation, led by Elad Gil with participation from NVIDIA, Kleiner Perkins, Lux Capital and others. It says it grew from zero to a $100 million annualized revenue run rate in less than 18 months. That measure extrapolates the current pace of revenue rather than describing a completed year’s sales.

Custom models for enterprisesApplied Compute helps companies adapt open-weight models with their own data and business judgment, then deploy and improve those models. Customers include Microsoft, Shopify, Harvey, DoorDash and Cognition, across fields including law and logistics. It sells an integrated training, inference and improvement service so customers do not have to assemble an entire model infrastructure stack themselves.

Adding operation after trainingThree months ago, it introduced AC2 to combine training, inference and continuous optimization. The company says customers have completed more than 1,700 training runs since July; about 40% now buy both training and inference, with inference processing more than one trillion tokens a day. Moving from model adaptation into daily operation increases both the frequency of usage and the need for compute.

Compute and delivery staffThe enterprise customer base has more than doubled since the previous fundraising, while the team has tripled since January, with researchers and engineers making up about 85% of employees. The new capital will expand compute capacity, hiring and enterprise deployment. Continued customer usage depends on model performance as well as operating costs, reliability and the speed of improvement after launch.

▮ SIGNALConverting training customers into inference customers extends Applied Compute’s relationship across the model’s operating life; business knowledge gained during training can also inform later cost optimization and give customers more reason to keep buying from the same platform.