Popular open source AI developer tool Ollama raises $65M, grows to nearly 9M users
Julie Bort — 6:00 AM PDT · July 9, 2026
The popular open source AI tool Ollama has raised a $65 million Series B, led by Theory Ventures, founder and CEO Jeff Morgan tells TechCrunch.
This round follows a previous 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.
Docker heritage
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.
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.
Investor perspective
“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.”
“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 has a “vital existential project” pushing them to move “to open-weight models,” he says.
Morgan sees its cloud service as an evolution of its open source mission. 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.
“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,” Fenton adds.