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Ollama, an open source AI tool, secures $65 million funding and reaches 8.9 million users

Jul 9, 2026
AI Summary

Ollama has raised $65 million in a Series B funding round, bringing its total funding to $88 million. The tool, which allows developers to run open-weight AI models on their PCs, has gained significant traction, now serving nearly 9 million users monthly, including 85% of Fortune 500 companies.

  • Ollama, an open source AI developer tool, raised $65 million in Series B funding led by Theory Venture.
  • The company has now raised a total of $88 million, following a $15 million Series A round led by Benchmark.
  • Launched in 2023, Ollama enables developers to quickly run open-weight AI models on personal computers, receiving positive feedback across various platforms.
  • The tool has achieved 176,000 stars and nearly 17,000 forks on GitHub, indicating strong community engagement.
  • Ollama offers subscription tiers for accessing larger models, with pricing ranging from free to $100 per month, and tracks usage based on GPU time.
  • Founders Jeff Morgan and Michael Chiang previously worked on Docker Desktop, which simplifies cloud application deployment.
  • Ollama is reportedly used by over 8.9 million developers monthly and is present in 85% of Fortune 500 companies, despite having only 14 employees.
  • Benchmark’s Peter Fenton, who led the Series A round, emphasizes the rarity of creating a product that achieves widespread adoption among developers.
  • The startup's growth has been linked to the rising popularity of open models for coding tasks, particularly following the success of OpenClaw.
  • There is a growing trend among enterprises to adopt open models for cost efficiency, which may benefit Ollama's cloud business.
  • Some users have expressed concerns about the company's focus on monetization potentially detracting from its free offerings, a phenomenon referred to as “Enshittification.”
  • Morgan defends the cloud service as an evolution of its mission to assist programmers in utilizing large models that may be impractical to run locally.
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