Ollama lets you run large language models directly on your own hardware, without sending data to third-party APIs. It's built for developers, researchers, and anyone who wants the capabilities of modern AI models without giving up control over their data.

The core idea is local-first. Models run entirely on your machine, which means no usage fees per token, no data leaving your network, and full offline capability for sensitive or mission-critical work. When local hardware isn't enough, an optional cloud tier gives access to larger models running on datacenter-grade hardware.

Key capabilities:

• Local model execution runs models on your own CPU or GPU, keeping all data on-device
• Cloud scaling lets you access larger, faster models when local resources hit their limits, with servers in the US, Europe, and Singapore
• App and agent support connects with tools like Open WebUI and coding assistants, so you can build workflows around open models
• Parallel requests are supported in cloud mode, useful when running multiple agents or serving several users at once
• Web access is available for cloud models, giving them real-time information retrieval
• Data privacy guarantees mean your inputs are never used for training, on either local or cloud runs

Ollama fits naturally into setups where you want a local AI assistant or a self-hosted backend for agent-based tools. The free tier covers cloud model access at a basic level, with paid plans unlocking higher concurrency and usage limits for heavier workloads.

Jan is a desktop AI chat app built for people who want the capabilities of ChatGPT without handing their conversations to a cloud service. It runs entirely on your own machine using open-source models, or you can connect it to hosted providers when you need more power. Either way, you stay in control.

The model selection is broad. Out of the box, Jan supports:

• Local models like Llama, Mistral, Qwen, Gemma, and DeepSeek, downloaded and run on your hardware
• Cloud providers including OpenAI, Anthropic (Claude), Google (Gemini), and others, connected via API key
• Switching between them without leaving the app, so you're not locked into one backend

This flexibility is what separates Jan from simpler local-model runners. You get offline capability when you want privacy, and cloud fallback when a task demands it.

Jan is built in public and has crossed 5.7 million downloads, which puts it among the more widely adopted self-hosted AI interfaces alongside tools like Open WebUI and LibreChat. The codebase is open, community-driven, and free.

The interface is clean and focused on conversation. A memory feature is in development that will carry context and preferences across sessions, so you won't need to re-explain your setup or working style each time you start a new chat.

It's a practical fit for developers, designers, researchers, or anyone who handles sensitive work and doesn't want that data leaving their machine. Local inference does require reasonable hardware, but Jan handles model management inside the app, so you don't need to touch a terminal to get started.