The popular open source AI tool Ollama has raised a $65 million Series B, led by Theory Venture, founder and CEO Jeff Morgan tells TechCrunch.
This round follows a previous $15 million Series A led by Benchmark’s Peter Fenton. All told, the company has now raised $88 million.
Ollama, which launched in 2023, helps devs run open-weight AI models on their PCs, getting them up and running in minutes. It has been praised by developers across countless training sites, videos, blogs and social media posts. It has amassed 176,000 stars and nearly 17,000 forks on GitHub.
Developers can also use Ollama to find models and access larger, more complex ones that it hosts on its neocloud via several subscription tiers, from free to $100/month. It also tracks usage based on GPU time, not token limits.
If the mission to help developers more easily build on their PCs sounds vaguely familiar, it should. Morgan and his co-founder Michael Chiang previously helped build Docker Desktop. They landed at Docker after it bought their previous startup, Kitematic. Docker makes containers that help cloud apps easy to move from cloud to cloud, or from desktop to cloud, abstracting away all the pesky hardware configuration issues.
So Ollama essentially did for AI what Docker and Docker Desktop did for cloud.
“Open models started coming out in 2023 but they were really hard to use,” Morgan said. They had been geared toward researchers at the time, not programmers. “As a result, it was really hard to get them up and running.” Three years after launching, Ollama is now “used by over 8.9 million developers every month, sitting in 85% of the Fortune 500 and growing like crazy,” he said. All with only 14 employees.
That career experience is what drew Benchmark’s Peter Fenton to lead its earlier round and join the board.
“What Jeff and Michael built with Docker is being used by 10 million-plus developers every day. The creative powers to create a product that goes to ubiquity for developers is extremely rare,” Fenton told TechCrunch.
Morgan and Fenton declined to discuss the startup’s revenues and new valuation. However, Morgan says that the proving point for Ollama as a business happened around January, when OpenClaw became hot. That’s when larger open models “suddenly became able to do these agentic tasks, like coding. Obviously, we saw the explosion of the assistants like OpenClaw, and this idea that open models can get real work done.”
Since then, the industry has been abuzz with the idea that paying users (particularly deep-pocketed enterprises and fast-growing AI application-layer startups) will increasingly turn to more affordable open models, reserving their use of closed models like Anthropic for more of an as-needed basis.
“I still think that this is the part that most of the debate gets wrong. It’s not an either/or,” Fenton says of open versus closed AI models. There will be plenty of business for both, he contends. However, every company with high inference expenses — the costs of using the models — has a “vital existential project” pushing them to move “to open-weight models,” he says.
There’s plenty of evidence that such startups and enterprises are already turning to open models for their daily needs. That, obviously, bodes well for Ollama’s cloud business.
But even more interesting, Ollama is another example of how AI is birthing a large new crop of open source projects that are turning into companies pursued by VCs. There are open source inference providers like Inferact, maker of vLLM, and RadixArk, maker of SGLang. There is OpenClaw and its alternatives like NanoClaw. There are even tiny startups building their own open models from scratch, like Arcee.
To be sure, not every Ollama fan has been happy that the company has been pursuing making a living. About a year ago, a bunch of blog and social media posts complained that its cloud business was drawing attention away from its beloved free project and cited Ollama as an example of the so-called “Enshittification” of dev tools, as the trend is called.
But Morgan sees its cloud service as an evolution of its open source mission to help programmers find and easily use models. Those state-of-the-art, large, open models are often “too big to run on your own computer. So we said, ‘Hey, let’s help find the compute for that,’” he explained.
Board member Fenton adds, “Nothing has changed for the core product that’s free on the desktop. There’s zero change to the premise that this is the place you can discover and run local models.”
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