One of the fastest-growing enterprise AI startups is betting that companies want AI built around their own data.
Applied Compute said on November 2 that it reached $100 million in annualized revenue run rate less than 18 months after its founding, and has raised $350 million in new funding at a $3.25 billion valuation. Elad Gil led the round, with participation from Kleiner Perkins, Lux Capital, Hanabi Capital, NVIDIA, Blackstone, Shopify Ventures, HubSpot Ventures and others.
What it does
Founded by former OpenAI researchers Yash Patil, Rhythm Garg and Linden Li, Applied Compute trains custom models, often built on open-source foundations, for enterprise customers and then runs them on its own cloud, Forbes has reported.
The company says it has more than doubled the number of enterprises it serves since its last raise. Customers named include Microsoft, Shopify, Mercor, Harvey, DoorDash, Cognition, Clay and Decagon, across life sciences, cybersecurity, hardware design, legal, financial services and logistics.
Where the money goes
Applied Compute says its team has tripled since January, with 85% researchers and engineers. It plans to use the funding to expand compute capacity, grow the team and speed up enterprise deployments.
Why it matters
Enterprises increasingly want AI tailored to their own workflows and data, not only general-purpose models. That is opening a fast-growing market between frontier labs and in-house teams.
For B2B tech companies, it is a reminder that “custom AI” is now a buying category. Vendors with domain data, integrations or specialist expertise should position themselves as part of that build-your-own-AI stack.
Sources: Applied Compute, Forbes. Written by the xRapid Media LLP team.
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