Ollama, the widely acclaimed open-source AI tool, has successfully closed a $65 million Series B funding round, with Theory Venture at the helm. This significant investment was confirmed to TechCrunch by Jeff Morgan, the company's founder and CEO.
This latest infusion of capital builds upon a prior $15 million Series A round, which was led by Benchmark's Peter Fenton. Cumulatively, the company has now secured a total of $88 million in funding.
Launched in 2023, Ollama empowers developers to efficiently run open-weight AI models directly on their personal computers, enabling rapid deployment within minutes. The tool has garnered widespread acclaim from the developer community, receiving praise across numerous training sites, videos, blogs, and social media platforms. Its popularity is further evidenced by its impressive GitHub statistics, boasting 176,000 stars and nearly 17,000 forks.
Beyond local execution, Ollama also offers developers the capability to discover and access larger, more sophisticated models hosted on its proprietary "neocloud." This cloud service is available through various subscription tiers, ranging from a free option to a $100 per month plan. Notably, Ollama's usage tracking is based on GPU time rather than traditional token limits, offering a distinct billing model.
The company's mission to simplify PC-based development for AI might strike a familiar chord, and for good reason. Jeff Morgan and co-founder Michael Chiang previously played a pivotal role in developing Docker Desktop. Their journey to Docker began after the company acquired their earlier startup, Kitematic. Docker is renowned for its container technology, which streamlines the portability of cloud applications across different cloud environments or from desktop to cloud, effectively abstracting complex hardware configuration challenges.
In essence, Ollama is achieving for the realm of AI what Docker and Docker Desktop accomplished for cloud computing.
Morgan articulated the initial hurdles, stating, "Open models started coming out in 2023 but they were really hard to use." He explained that these models were initially designed for researchers, not everyday programmers, making them "really hard to get them up and running." Fast forward three years, and Ollama's impact is profound: it is now "used by over 8.9 million developers every month, sitting in 85% of the Fortune 500 and growing like crazy," Morgan revealed, all achieved with a lean team of just 14 employees.
It was this very depth of career experience that compelled Benchmark's Peter Fenton to lead Ollama's earlier funding round and subsequently join its 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 shared with TechCrunch, underscoring the founders' unique talent.
While Morgan and Fenton opted not to disclose the startup's revenues or its new valuation, Morgan indicated that Ollama's commercial viability was significantly proven around January. This period coincided with the surge in prominence of OpenClaw, marking a moment when larger open models "suddenly became able to do these agentic tasks, like coding." He added, "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 buzzing with the anticipation that paying users, particularly well-resourced enterprises and rapidly expanding AI application-layer startups, will increasingly gravitate towards more cost-effective open models. This shift would see closed models, such as those from Anthropic, reserved for more specialized or as-needed applications.
Fenton, however, offered a more nuanced perspective on the open versus closed AI model debate. "I still think that this is the part that most of the debate gets wrong. It’s not an either/or," he contended, predicting ample business opportunities for both categories. Nevertheless, he stressed that every company incurring high inference expenses – the costs associated with using these models – faces a "vital existential project" driving them to transition "to open-weight models."
Indeed, there is growing evidence that numerous startups and enterprises are already integrating open models into their daily operations, a trend that naturally bodes well for Ollama's cloud business.
More broadly, Ollama stands as a compelling example of a burgeoning phenomenon within the AI landscape: the emergence of numerous open-source projects that are successfully transforming into venture capitalist-backed companies. This trend is visible across the ecosystem, from open-source inference providers like Inferact, creators of vLLM, and RadixArk, developers of SGLang, to platforms like OpenClaw and its alternatives such as NanoClaw. Even nascent startups like Arcee are building their own open models from the ground up.
It is worth acknowledging that not all of Ollama's proponents have been entirely enthusiastic about the company's commercial endeavors. Approximately a year ago, a flurry of blog posts and social media discussions voiced concerns that the nascent cloud business was diverting attention from the beloved free project. Critics even cited Ollama as a case in point for the "Enshittification" of developer tools, a term used to describe the perceived degradation of platforms as they prioritize monetization over user experience.
However, Morgan views the cloud service as a natural evolution of Ollama's open-source mission to facilitate programmers' discovery and effortless use of models. He explained that many 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.’"
Board member Fenton reinforced this position, adding, "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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