Modal Labs Targets $2.5 Billion Valuation for AI Inference Work

AI startup Modal Labs is reportedly looking to raise funds at a $2.5 billion valuation.

The company, which specializes in artificial intelligence (AI) inference infrastructure, is in talks with venture capitalists about a new funding round, TechCrunch reported Wednesday (Feb. 11), citing sources familiar with the matter.

According to the report, the deal would more than double Modal Lab’s previous valuation of $1.1 billion, reached months ago when the company announced an $87 million Series B round.

The report added that Modal Labs Co-founder and CEO Erik Bernhardsson denied that the company was actively raising funds and said his recent interactions with venture capitalists were simply general conversations.

As covered here late last year, inference refers to the stage where a trained model processes new data and generates results. Examples of inference at work include a customer service chatbot replying to a query, or an AI system analyzing a financial document.

“While training creates the model by processing vast datasets to learn patterns, inference applies that learned knowledge to perform specific tasks at scale,” PYMNTS wrote. “As enterprises deploy AI systems that manage thousands or millions of requests daily, inference becomes the dominant operational challenge and cost driver.”

TechCrunch notes that Modal is among a small group of inference-focused startups catching the eye of investors. Last month, rival firm Baseten announced it had raised $300 million — half of it from Nvidia — valuing the company at $5 billion.

And in October, cloud inference company Fireworks AI raised $250 million to expand its platform, with that round valuing the startup at $4 billion. Fireworks helps organizations use and customize large language models more efficiently, reducing costs and delays in deploying them.

Last year, PYMNTS looked at inference and why it is now more important than training for most enterprises. Training a large language model happens just on a periodic basis, while inference takes place continuously each time a user interacts with an artificial intelligence system.

“A single model might manage millions of inference requests per month, each requiring computational resources, adding latency and incurring costs,” that report said. “For companies running artificial intelligence in customer-facing applications, inference performance directly affects user experience, system reliability and operational expenses.”

Inference, the report added, is emerging as a competitive category all its own. Brookfield has forecast that by 2030 around three-quarters of AI compute demand will come from inference, moving the economics of artificial intelligence “from training breakthroughs to the efficiency of serving models at scale,” PYMNTS wrote.

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