Applied Compute hits $100M run rate and raises $350M

Less than 18 months after founding Applied Compute, we've grown from zero to $100 million in annualized revenue run rate.

We’ve just raised $350 million in new funding at a $3.25 billion valuation. The round was led by Elad Gil, with participation from 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.

Since our last fundraise, we've more than doubled the number of enterprises we serve. Companies like Microsoft, Shopify, Mercor, Harvey, DoorDash, Cognition, Clay, and Decagon are using Applied Compute to build and run AI tailored to their work. Our customers now span industries including life sciences, cybersecurity, hardware design, legal, financial services, logistics, and more.

We started Applied Compute because we believed companies would want to own and improve the AI powering their products and operations. Open-weight models have advanced rapidly, giving companies a strong foundation to build on with their own data and expertise.

Our work with customers shows what this makes possible. For Harvey⌝, we trained a model for its Review Table product that improved answer and citation quality at less than half the inference cost of a frontier alternative. For Clay⌝, we trained and deployed a custom router that reduced production inference costs by up to 47% without sacrificing quality.

But training a better model is only part of the problem. Customers also need to deploy it, evaluate its performance, and continually improve it. Three months ago, we introduced AC2⌝, the Applied Compute Agent Cloud, to bring training, inference, and continual improvement together in one platform.

Since July, customers have run more than 1,700 training runs on AC2.

AC Inference⌝ has grown to serve more than 1 trillion tokens a day. 40% of our customers now use Applied Compute for both training and inference, and that share continues to grow.

Training, inference, and routing are the first pieces of a much larger stack. We're building the AI infrastructure every company will need to fully own their intelligence, from the models themselves to the systems that run, monitor, and secure them.

Our team has tripled since January, and 85% of us are researchers and engineers. We plan to use this new funding to expand our compute capacity, grow our team, and accelerate deployment in the enterprise.

If you're looking to train, serve, and improve your own models, work with us.⌝