Baseten has announced the closing of its $1.5 billion Series F funding round, valuing the company at $13 billion. The round was led by Altimeter Capital, Conviction Partners, and Spark Capital, with Sands Capital and Wellington Management serving as co‑leaders. Additional participation came from Battery Ventures, Blackbird, D.E. Shaw Ventures, Durable Capital Partners, Greylock, IVP, Verified Capital, and 01A. This marks Baseten’s fourth fundraise in 18 months.
The company reported significant momentum over the past year, with revenue increasing 20x and inference volume rising 40x. Baseten attributes this growth to the expanding role of inference as a critical layer in the AI stack, positioning the company as a preferred partner for organizations seeking support with inference and post‑training workflows.
Baseten works with a growing roster of high‑profile customers, including Cursor, Notion, Lovable, Harvey, HubSpot, OpenEvidence, Abridge, Decagon, and Parallel. These companies are building products where intelligence is central to the user experience and are increasingly focused on developing proprietary learning systems. Industry leaders, including Satya Nadella, have emphasized that long‑term competitive advantage will come from systems built around a company’s own data, workflows, and feedback loops — a trend Baseten says it observes across its customer base.
With open‑weight models now strong enough to serve as viable alternatives to closed APIs, Baseten sees a major opportunity for enterprises to adopt more customizable AI architectures. The company’s researchers and engineers work directly with customers to post‑train and optimize specialized models and deploy them with high performance, reliability, observability, and cost efficiency at production scale.
The new financing will enable Baseten to expand investments in compute infrastructure, software development, and talent as AI becomes increasingly central to enterprise products and operations. The company also noted that it continues to support organizations looking to post‑train custom models and run inference on high‑performance infrastructure, and it is actively hiring across its AI and engineering teams.
