Jan vs Ollama: pricing, features and which to choose

Jan is a desktop AI assistant developed by Menlo Research, with its open-source repository created in August 2023. The project positions itself as an alternative to ChatGPT and Claude, running language models on a user's computer. Its website reports more than 4 million downloads, while another homepage rendering reports more than 6.5 million downloads, so the exact current total is inconsistent across Jan's pages. The project supports macOS, Windows, and Linux, and its repository lists Apache 2.0 licensing. Jan's current product surface includes desktop chat, local models, remote provider connections, projects, model management, MCP integrations, and a separate Jan Agent. It is aimed at people who want control over model choice, local files, and data location.

A typical Jan workflow begins by installing the desktop application, choosing a bundled or downloadable model, and opening a chat without an account. Users can download compatible GGUF models through Jan’s hub, import local GGUF files, or connect providers including OpenAI, Anthropic, Mistral, and Groq with their own keys. Settings include context size, GPU layers, temperature, sampling controls, grammar files, and JSON schema files, helping users balance output behavior and hardware limits. Jan also supports custom assistants, file attachments, projects, MCP servers, and tool-enabled workflows. Its local server runs an OpenAI-compatible API at localhost:1337, so applications such as Continue and Cline can use local or configured remote models. This setup suits users who want desktop control, local files, and flexible provider choices.

Jan’s main limitation is that local performance depends on the computer, model size, quantization, available memory, and graphics support. Users should check hardware requirements and disk space before downloading large models. Ollama offers a more terminal-oriented model runner, while Jan adds a fuller desktop chat interface, model controls, projects, assistants, MCP integrations, and provider connections. LM Studio is a closer desktop comparison, with a polished local model catalog and server features, while Jan places more emphasis on open-source licensing and local data ownership. ChatGPT is easier for users who want a hosted service without model downloads, configuration, or hardware setup. Choose Jan for free local inference, an OpenAI-compatible local API, flexible model and provider selection, and control over where conversations are stored. Remote providers remain separately billed, while local use avoids Jan subscription charges.

Ollama is an AI runtime maintained by Ollama Inc. for downloading, serving, and integrating open models on personal computers and servers. Its repository was created on June 26, 2023, and uses the MIT License. Ollama documents support for macOS, Windows, Linux, Docker, Python, and JavaScript, with a REST API for model management and chat. The company reports more than 9 million monthly installs, more than 1 billion model downloads, and more than 200 trillion tokens served. Those figures come from Ollama's homepage. Local runs keep prompts and responses on the user's machine, while Ollama Cloud provides hosted inference for models that exceed local hardware capacity. This combination makes the product relevant to developers who want a local runtime with an optional hosted path.

A typical workflow begins by installing Ollama on macOS, Windows, or Linux, then running a model such as Gemma 4 with the command line. Users can chat in a terminal, launch supported coding integrations with commands such as ollama launch claude, or call the local REST API at port 11434. The official Python and JavaScript libraries support application development, and Ollama documents OpenAI-compatible and Anthropic-compatible clients for cloud API access. A Modelfile defines a customized model configuration, while commands manage downloading, listing, copying, and removing models. Cloud models use the same general interface, but require sign-in when accessed through the app or CLI. Direct cloud API requests use an API key and do not require an Ollama installation.

Hardware is Ollama's main local constraint. Large models can require substantial memory and may run slowly on computers without a strong GPU, while cloud use adds sign-in, API-key, quota, concurrency, and token-cost considerations. LM Studio is a closer choice for users who prefer a graphical desktop interface for downloading and chatting with local models. Jan is another local-first runner with an application-oriented interface. GPT4All also focuses on running models locally. Open WebUI is a separate self-hosted interface that connects to Ollama rather than replacing its runtime and API. Choose Ollama if you want a command-line and API layer for local open models, with optional cloud inference for larger models and integrations with coding tools.