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    Home - Latest in Tech - Ollama Raises $65M Series B, Now Used by 8.9M Developers Monthly
    Latest in Tech

    Ollama Raises $65M Series B, Now Used by 8.9M Developers Monthly

    Geek PlanetBy Geek PlanetUpdated:July 13, 20263 Mins Read
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    Ollama founders Jeff Morgan and Michael Chiang
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    Ollama, the open-source tool that lets developers run AI models on their own machines, has raised $65 million in Series B funding led by Theory Ventures. The company now has 8.9 million monthly active developers and sits inside 85% of the Fortune 500.

    From Side Project to $88M in Total Funding

    Founded in 2023 by Jeff Morgan and Michael Chiang, who previously built Docker Desktop, Ollama took the same approach Docker used for containers and applied it to AI models. Where Docker made cloud apps portable across hardware, Ollama makes open-weight models runnable on any developer’s laptop.

    Ollama open source AI development tool logo

    The startup previously raised $15 million in a Series A led by Benchmark’s Peter Fenton, bringing total funding to $88 million. Morgan declined to share revenue figures or a new valuation.

    Why the Timing Worked

    Morgan points to January 2026 as the inflection point. That’s when open-weight models became genuinely capable at agentic tasks like coding, powered by the explosion of tools like OpenClaw. “Suddenly open models became able to do real work,” Morgan told TechCrunch.

    Since then, enterprises with high inference costs have been actively shifting routine workloads to open models while keeping closed models like Anthropic’s for specialized tasks. Fenton calls this a “vital existential project” for every company with expensive AI usage.

    How Ollama Makes Money

    The free desktop tool remains unchanged, according to Ollama. Revenue comes from a cloud service that hosts larger models too big to run locally, priced across tiers from free to $100 per month. Usage is tracked by GPU time rather than token limits.

    Some developers criticized the cloud push as “enshittification” when it launched, arguing it pulled focus from the open-source project. Morgan frames it as an evolution: the same mission of helping developers find and use models, just extending to compute they can’t provide themselves.

    The Numbers

    Ollama has 176,000 GitHub stars and nearly 17,000 forks. The team is 14 people. Morgan previously co-founded Kitematic, which Docker acquired, leading to his role building Docker Desktop used by over 10 million developers daily.

    The funding arrives amid a broader surge in open-source AI investment. In the same week, Together AI raised $800 million at an $8.3 billion valuation, and Prime Intellect secured $130 million in Series A funding. North American startup funding hit $392 billion in the first half of 2026, per Crunchbase data.

    What’s Next

    Morgan says the company will use the funding to grow beyond 14 employees and expand cloud infrastructure. The open-source model ecosystem continues to evolve rapidly, with new tools like Inferact (maker of vLLM) and RadixArk (maker of SGLang) also attracting venture capital.

    For developers who want to test open models locally, Ollama remains a single-command install: download the app, pull a model like Llama 3 or Mistral, and start building.

    Frequently Asked Questions

    Is Ollama still free?

    Yes, the desktop application remains free and open source. Ollama’s revenue comes from its cloud service for larger models that won’t run on consumer hardware.

    How do I install Ollama?

    Download the app from ollama.com for macOS, Linux, or Windows. Run a command like “ollama run llama3” to download and start a model in minutes.

    What’s the difference between Ollama and Docker?

    Docker packages cloud applications for portability across servers. Ollama packages AI models for portability across developers’ machines. Same philosophy, different domain.

    Which companies use Ollama?

    Ollama is used in 85% of the Fortune 500, according to CEO Jeff Morgan. The tool is popular across training sites, YouTube tutorials, and developer communities.

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