Similar outcome, different starting point
All three can help a person experiment with models on a local computer, but they invite different workflows. Ollama is relatively natural when an application needs a local model endpoint. LM Studio is relatively natural when a person wants a visual desktop environment for downloading and testing models. Jan is relatively natural when the goal is a desktop assistant that can connect local and selected remote model workflows.
Where each is relatively distinctive
Ollama is relatively strong as a simple local serving layer for developers. LM Studio is relatively strong as a hands-on desktop testing experience. Jan is relatively distinctive when users want an assistant-style interface and a project that emphasizes an open local AI workspace. These are different entry points into the same broad category, not interchangeable privacy guarantees.
Boundaries and ownership
A local runtime does not automatically mean every prompt stays local. Check model download sources, provider settings, telemetry, API exposure and the license of every model weight. Measure memory use, response time and concurrent behavior on the hardware you will actually ship or support. Treat the runtime, the model and the calling application as three separate parts of the data path.
A comparison you can run
Use one representative document task and one structured extraction task. Run both with network access disabled, then repeat with the intended provider configuration. Record latency, memory, output quality and whether the application makes any external request. The result should be a deployment decision for your machine, not a universal claim about local AI.