Ollama
Run open-weight LLMs locally, with an optional paid cloud
Overview
Ollama downloads and runs open-weight models such as Qwen, DeepSeek, Gemma, gpt-oss and Mistral on your own machine, with one install on macOS, Windows and Linux, a CLI, a desktop app and an API compatible with OpenAI and Anthropic. Local use is free and MIT-licensed; paid cloud plans (Pro $20, Max $100, Team $500 a month) add hosted models billed per token.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 12 Oct 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- Run models locally
- Starter usage credits and starter models
- 1 concurrent cloud request
- No service fees
- $60 of usage credits a month
- Larger pro models
- 3 concurrent requests
- $300 of usage credits a month
- Early access to newest models
- 10 concurrent requests
- Unlimited users
- $1,000 of shared usage credits a month
- Centralised billing and administration
- Priority support
- Model access controls
- Cost budgets per user and API key
- Private Slack channel and dedicated support
- Custom security questionnaires
Use cases
What you can produce with Ollama
- Run an open-weight model locally with one command, offline, with no account or API key
- Serve a local API compatible with OpenAI and Anthropic formats
- Ask the desktop app about a PDF or image with a multimodal model on your own hardware
- Create a customised model variant with a Modelfile and share it
- Point Claude Code, Codex or OpenCode at Ollama with ollama launch
- Pull models such as Qwen, DeepSeek, Gemma, gpt-oss and Mistral from the Ollama library
- Run larger models in Ollama's cloud with a :cloud tag, billed per token from monthly credits
Ollama remains the easiest way to get an open-weight model running locally: one command pulls and serves a model, the API speaks OpenAI and Anthropic formats, and launch commands wire up Claude Code, Codex and OpenCode. Local use is free, MIT-licensed and private, and since August 2026 a per-token cloud with monthly credits reaches models too large for your hardware.
Local quality and speed are bounded by your GPU or Apple Silicon memory, and critics argue it under-credits llama.cpp. The cloud runs mainly in the US and Europe, so run locally if data must stay in-region.
What we found
Ollama is still the usual first answer to the question of how to run an open-weight model on your own machine. Install it on macOS, Windows or Linux and one command pulls a model and starts a chat. It also serves an API in OpenAI's and Anthropic's formats, so existing tools can point at it, and a desktop app covers macOS and Windows. The code is MIT-licensed on GitHub, version 0.40.3 shipped on 11 October 2026, and running models on your own hardware costs nothing and is not metered.
2026 changed the business around it. On 9 July Ollama Inc., the San Francisco company led by Jeffrey Morgan and Michael Chiang, the pair who built Kitematic, announced a $65 million Series B led by Theory Ventures, taking total funding to $88 million. Morgan told TechCrunch the tool is used by more than 8.9 million developers a month, a company figure. The paid cloud has grown too. Since 31 August, Pro costs $20 a month and includes $60 of usage, Max costs $100 with $300, and Team costs $500 with $1,000 shared across unlimited users, with usage billed per token at published rates. The company says its cloud does not log or train on prompts.
On the engine side, Ollama now runs Apple's MLX on Apple Silicon and uses llama.cpp for GGUF models, and a launch command sets up Claude Code, Codex and OpenCode against local or cloud models.
The criticism is real. In April 2026 a blog essay by Zetaphor, titled Friends Don't Let Friends Use Ollama, argued that Ollama was slow to credit llama.cpp and has been drifting toward the cloud. Earlier versions of this page described that critique as a widely upvoted Reddit post, which we could not confirm.
For developers who want a local model behind an API in minutes, nothing is easier. Ollama's cloud runs mainly in the United States and may route to Europe, so teams that need data kept in-region should run models locally.
Compiled by RECATOOLS from public sources: vendor documentation and pricing, code repositories and published reporting.
About this listing
This entry was compiled from publicly available data including Ollama's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Ollama unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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