StartupsNov 3, 2026, 9:19 PM UTC·2 min read

fal Raises $1.3 Billion at $16 Billion Valuation

AI inference startup fal, co-founded by Turkish entrepreneurs, has doubled its valuation to $16 billion with a $1.3 billion round led by Bessemer Venture Partners.

Illustration of AI inference servers generating images, video and audio, representing fal's $1.3 billion funding round. — Image: egirişim

SUMO

Dialo

ediviewer

fal, the AI inference startup that hosts image, video and audio models for creators and developers, has raised $1.3 billion at a $16 billion valuation, doubling its worth in a matter of months.

The round, closed in October and announced on Tuesday, was led by existing investor Bessemer Venture Partners. Nvidia, Sound Ventures, HOF Capital and T. Rowe Price Group also participated. Bessemer partner Talia Goldberg is joining fal's board. In April, the company raised $350 million at an $8 billion valuation in a round led by Sequoia.

Compute shortage drove the raise

Inference, the stage where trained AI models are actually run, is seeing surging demand from developers. CEO and co-founder Burkay Gür said one reason fal took such a large round is its need for more computing power. "We reached a point in the summer where we couldn't serve our customers and had to limit their usage," he said.

A source familiar with the matter said fal's annualized revenue reached $800 million earlier this year. Other inference startups, including Modal and Baseten, are reportedly in talks to raise at high valuations as well.

Strategy and plans

Over the past year, fal has worked with both open-source and closed-source model developers, focusing increasingly on post-training improvements to video models. Some of these partnerships involve licensing fees or revenue-sharing deals. Gür said the company does not plan to train its own models from scratch in the near term, but has not ruled it out for the future.

Based in San Francisco, fal intends to spend the new capital on expanding compute capacity and growing its 185-person team. "Operating across the AI market increasingly requires more capital," Gür said. "Compute and scalability are a major bottleneck. The larger the scale you can operate at, the better your chances of succeeding at the end of the day."