Choose where inference runs by measuring the task on target devices and mapping exactly where user data travels.
On-device AI runs a model on the user's phone or tablet. Cloud AI sends a request to a remote service for processing. A mobile product can use either approach or combine them, but the decision should follow the task, supported devices, data requirements, and operating budget.
The cloud does not automatically provide the best experience, and local processing does not automatically make an entire app private. Map the full data flow, including analytics, crash reports, synchronization, and logs, before drawing conclusions.
When on-device processing is a good fit
Local processing can be useful for a bounded task that must work without a network connection or should avoid sending its input to a remote inference service. Once the required model and resources are available, the task can run without waiting for a server round trip.
Apple's Core ML provides tools for integrating machine learning models on Apple devices. That is a platform capability, not a promise that every model will fit your supported hardware. Check model compatibility, download size, memory, thermal behavior, and the devices you intend to support.
When cloud processing is a better fit
A remote service can be appropriate when the task requires a model that is impractical to run on target phones, relies on centrally managed information, or benefits from server-side updates. It can also avoid distributing a large model with the application.
The tradeoffs include connectivity, network latency, service availability, and ongoing usage cost. User data may cross additional system boundaries. Define what is sent, which account is allowed to request it, and how the service handles retention before choosing the architecture.
Compare the full request path
| Concern | On-device | Cloud |
|---|---|---|
| Offline use | Can work with local model and data | Needs connectivity for inference |
| Latency | Depends on model and device resources | Includes network and service processing |
| Cost | Engineering, distribution, and device resources | Engineering plus service usage |
| Updates | Needs a model delivery and compatibility plan | Can be managed centrally |
| Privacy | Inference input can stay local | Requires deliberate remote data handling |
A hybrid approach needs explicit boundaries
A hypothetical note-taking app might classify a short note locally, then offer a cloud-powered rewrite when the user requests it. That design can be useful if the difference is clear. It should not silently upload content merely because a local model fails.
Define whether fallback is automatic, user-controlled, or unavailable. Make the data implications understandable at the point of use. Keep authorization checks on the server even if an on-device model decides which action to suggest. A local prediction must not become proof of permission.
Test the oldest device you support
Measure cold and warm runs, memory pressure, battery impact, model download failures, and behavior after the app resumes. Repeat the task enough times to expose thermal or resource issues. Check the actual output quality too; a faster result is not useful if it fails the product requirement.
For cloud inference, test weak connectivity, interrupted requests, timeouts, and duplicate submissions. Record both time to first useful feedback and time to completion. Include rejected or abandoned requests in cost estimates rather than counting only successful demonstrations.
Make the decision per feature
An application does not need a single answer for every AI capability. A small local classifier and a remote language feature can coexist if their responsibilities are clear. Document the reason for each choice and the conditions that would justify changing it.
The AI integration roadmap covers evaluation and production controls. If device integration is central to your product, also consider where native development offers an advantage.