On-device AI is ideal for private, low-latency tasks: summarising local content, ranking suggestions, personalising lessons, detecting intent or helping the user without sending every interaction to a server.
Cloud models still win when the task needs broad reasoning, large context windows, fresh external data or expensive multimodal processing. The best apps often combine both: local first, cloud when needed.
A good mobile AI architecture makes this choice explicit. Define what data may leave the device, what is cached locally, and how the app behaves when connectivity or quota is limited.