Jan vs Ollama
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
AI tool · Open source
An open-source desktop assistant that runs models offline or connects to cloud APIs.
Open source · MIT
Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.
Our verdict
Jan is a desktop chat app with a point-and-click style interface that can run models on your computer or connect to cloud APIs, so it suits people who want an open-source assistant they can use without a terminal. Ollama is a command-line tool and local API for downloading and running open-weight models, aimed at developers, students and people who want to build apps or connect coding assistants. They overlap on running local models but differ in audience, so pick based on whether you want a chat app or a developer tool.
Pick Jan if you want an open-source desktop chat assistant that can mix offline local models with cloud APIs.
Pick Ollama if you are comfortable with a terminal and want to run open models locally and call them from your own apps through an API.
| Key difference | Jan | Ollama |
|---|---|---|
| Main purpose | Desktop assistant for chatting with offline or cloud models | Tool for downloading and running open-weight models locally |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose Jan if you need…
- Open-source offline chat
- Mixing local and cloud models
Choose Ollama if you need…
- Developers who want a local model API for their own apps
- People who want to chat with open models without sending prompts to a hosted service
- Users who want to connect a coding assistant to a local or self-chosen model
- Students experimenting with different open models from a terminal
Side-by-side
| Jan | Ollama | |
|---|---|---|
| Type | AI assistants · Local & open models | App |
| Price | Open source | Free and open source |
| Licence | — | MIT |
| GitHub stars | — | 186k |
| Setup difficulty | — | Beginner |
| How you get it | — | Install with a package manager |
| Platforms | macOS, Windows, Linux | macOS, Windows, Linux, CLI, API |
| Works with | — | VS Code |
| Main language | — | Go |
| Last code update | — | 4 days ago |
| Hands-on tested by us | Not yet | — |
| Last checked | 2026-10-29 | — |
What each one is
Jan
An open-source desktop assistant that runs models offline or connects to cloud APIs. Private and fully open source. Less polished than commercial desktop apps.
Ollama
Ollama is a tool for downloading and running open-weight AI language models locally, with a simple command line and a REST API. The README says it also offers cloud access and can connect to coding tools and assistants such as Claude Code, Codex and OpenClaw. People use it to try models privately or to build apps that call a model through a local API.
Best for
Jan
- Open-source offline chat
- Mixing local and cloud models
Ollama
- Developers who want a local model API for their own apps
- People who want to chat with open models without sending prompts to a hosted service
- Users who want to connect a coding assistant to a local or self-chosen model
- Students experimenting with different open models from a terminal
Strengths
Jan
- Offline local models
- Cloud API connections
- Extensions
- Local API server
Ollama
- Simple install and run commands across macOS, Windows, Linux and Docker
- Official Python and JavaScript libraries plus a documented REST API
- Large community with many integrations listed in the README
Not ideal for
Jan
—
Ollama
- People who need the most capable closed frontier models, since this focuses on open models
- Users with limited disk space or memory, because model files can be large and need capable hardware (the README does not give specific requirements)
- Non-technical users who want a polished desktop app with no terminal use, although the README mentions a download option for macOS and Windows
Trade-offs
Jan
—
Ollama
- The README is truncated and does not state hardware requirements or model sizes
- Some features, such as cloud access and specific integrations, depend on Ollama's own services and documentation that is only partly described here
- Model quality and licensing depend on each individual model, not on Ollama itself
Getting started
Ollama
macOS and Linux install
curl -fsSL https://ollama.com/install.sh | shWindows install (PowerShell)
irm https://ollama.com/install.ps1 | iexFrequently asked questions
Should I use Jan or Ollama?
Jan is a desktop chat app with a point-and-click style interface that can run models on your computer or connect to cloud APIs, so it suits people who want an open-source assistant they can use without a terminal. Ollama is a command-line tool and local API for downloading and running open-weight models, aimed at developers, students and people who want to build apps or connect coding assistants. They overlap on running local models but differ in audience, so pick based on whether you want a chat app or a developer tool. Pick Jan if you want an open-source desktop chat assistant that can mix offline local models with cloud APIs. Pick Ollama if you are comfortable with a terminal and want to run open models locally and call them from your own apps through an API.
What is the difference between Jan and Ollama?
Jan: An open-source desktop assistant that runs models offline or connects to cloud APIs. Ollama: Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud. Jan suits open-source offline chat, while Ollama suits developers who want a local model api for their own apps.
Are Jan and Ollama free?
Jan: Open source. Ollama: Free and open source.
What are other alternatives to Jan and Ollama?
Other options people compare them with include LM Studio, llama.cpp, MindsHub, Reasonix, magpie.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.