Hosted AI vs local AI: where should the model run?

Start with the boundary

Hosted models put inference behind a service interface. Local models run on hardware you control. That distinction affects connectivity, hardware capacity, monitoring and the places where prompts may travel. Neither deployment pattern automatically establishes privacy or quality: an application can still send telemetry, fetch external resources or retain sensitive logs.

Compare the whole workflow

Test a representative task on the actual machines and service configurations you would use. Record response quality, waiting time, setup effort and recovery after failure. Include model downloads, updates and power constraints in a local trial; include network interruptions and service quotas in a hosted trial.

Choose an owner

Local deployment makes sense when an identified team can maintain it and its constraints fit the work. Hosted inference can simplify operations when the service terms and controls meet your requirements. A hybrid design needs explicit routing rules, so confidential inputs do not silently cross the intended boundary.

Sources & notes

An editorial decision framework, not a scored benchmark or hands-on test.

nist.gov — official reference

Sources reviewed for the September 2026 launch edition.