Ollama is the software most people use the first time they run an AI model on their own computer. Type ollama run and a model name, and a few gigabytes later you have a language model answering on your machine with no API key, no account, and no data leaving the building. That single command is the whole product thesis, and it has carried the company to 8.9 million developers a month and a presence in 85 percent of the Fortune 500.
The company was founded by Jeffrey Morgan and Michael Chiang, who previously built Docker Desktop after Docker acquired their first startup. In July 2026 they announced $88 million raised in total, including a $65 million Series B led by Theory Ventures, while running the company with fourteen employees.
What Ollama actually does
Ollama wraps the low-level inference engine llama.cpp in something a working developer can use without reading a research paper. It handles model download and storage, quantization selection, GPU detection across macOS, Linux, and Windows, and exposes a local REST API on port 11434 alongside a command line and desktop apps. Written in Go, licensed MIT.
The design decision that mattered most was API compatibility. The local server speaks the OpenAI API format, so code written against a cloud provider runs against a local model by changing a base URL. There is an Anthropic compatibility layer too. That turned Ollama from a hobbyist toy into the default local backend for tens of thousands of downstream integrations.
Our guide to the best local LLMs you can run at home uses Ollama as the on-ramp for exactly this reason: the model choice matters, but nobody gets to the model choice if installation takes an afternoon.
Scale, and the fourteen people behind it
The main repository carries more than 177,000 GitHub stars and 17,000 forks as of August 2026. Morgan told TechCrunch the tool serves 8.9 million developers monthly and sits inside 85 percent of the Fortune 500, with roughly 100,000 new users arriving each week organically.
Fourteen employees is the number worth sitting with. Ollama is a case study in what a small team can hold when the distribution is the open-source project itself and the product is a single well-chosen abstraction. There is no sales motion in the free tier, and the company has never disclosed revenue or valuation.
The cloud pivot, and the criticism it drew
Ollama now sells hosted inference. Pro is $20 a month, Max is $100, and a Team tier runs $25 per seat with a five-seat minimum and zero data retention. Billing tracks GPU time rather than token limits. The rationale is that the strongest open models, including Kimi K3 from Moonshot AI, DeepSeek, GLM, and MiniMax, are too large for consumer hardware, so the company hosts what your desk cannot.
That move drew accusations of enshittification on Hacker News and r/LocalLLaMA from users who read a paid cloud tier as a betrayal of a local-first tool. Morgan's answer is that nothing changed in the free desktop product, a point Benchmark's Peter Fenton repeated on the record: the premise that Ollama is where you discover and run local models is unchanged. The criticism is worth knowing before you build a business on the free tier, and so is the fact that the core remains MIT-licensed and forkable.
Where it sits against the alternatives
Three layers are often confused. llama.cpp is the inference engine. Ollama and LM Studio are the developer-facing layers on top, Ollama command-line first and LM Studio graphical first. vLLM and SGLang are datacenter serving stacks built for throughput across many concurrent users, not for one person on a laptop.
Picking between them is mostly a question of who the user is. A solo builder wanting a model running in ten minutes wants Ollama. A team serving thousands of concurrent requests wants vLLM. Someone who prefers clicking to typing may prefer LM Studio. Our news desk has covered the open-weight escalation that keeps making all of this more useful, including the Kimi K3 release that put a downloadable model inside the global top three.
Frequently asked questions
Is Ollama free?
The core tool is free and MIT-licensed, and running models on your own hardware costs nothing beyond electricity. Ollama also sells paid cloud tiers for hosted access to larger models: Pro at $20 per month, Max at $100, and Team at $25 per seat. The free local product is unchanged by those tiers.
Who founded Ollama?
Jeffrey Morgan and Michael Chiang, who met at the University of Waterloo and previously founded Kitematic, acquired by Docker in 2015. Their work there became Docker Desktop. Morgan is chief executive.
How much funding has Ollama raised?
$88 million total. A $15 million Series A led by Peter Fenton at Benchmark, then a $65 million Series B led by Tomasz Tunguz at Theory Ventures announced in July 2026. Angel investors include Docker founder Solomon Hykes and ClickHouse CEO Aaron Katz. The company has declined to state its valuation or revenue.
Does Ollama work with existing OpenAI code?
Yes. The local server implements parts of the OpenAI API, so pointing an existing client at http://localhost:11434/v1/ with any placeholder API key usually works without further changes. An Anthropic compatibility layer is documented as well.
Is Ollama the same as llama.cpp?
No. llama.cpp is the underlying inference engine that does the actual model execution. Ollama uses it and adds model management, quantization handling, hardware detection, an OpenAI-compatible server, and desktop apps. Ollama added Apple MLX support in a March 2026 preview.