When an AI request runs on a device, the app can avoid sending that prompt to a model server, work without a reliable connection, and reduce per-request server costs. But local inference does not, by itself, make an app private or fully local-first: the product still decides what context it reads, what it saves, which actions the model can take, and whether a request can be sent to the cloud instead.
Local inference is one part of a local-first app
On-device inference means that a particular model computation runs on the user’s device. The distinction is about where that computation happens—not automatically where every piece of data lives or how the rest of the app behaves.
A local-first product needs broader rules for its data and behavior: what it stores on the device, whether and how it syncs, what is included in backups, how long records and AI-generated artifacts are retained, and how users recover data. An app may run inference locally but still send context or telemetry elsewhere, or sync its records to a server. The model runtime alone does not establish the app’s storage, synchronization, or privacy policy.
For an AI feature, trace the full path: the local records the app reads, the prompt it constructs, where inference runs, what output it returns, what the app stores afterward, and whether any fallback or telemetry sends information off-device. Treat each as a separate design decision.
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What changes when inference runs on the device?
Data flow and privacy boundaries
Google’s Android Developers documentation describes Gemini Nano prompts executing locally through AICore, without server calls for that on-device execution. That can keep the prompt, inference, and output processing on the device—but only if the app’s own surrounding data flow does not send those inputs or outputs elsewhere.
Review more than the prompt. An app might retrieve personal records to build context, retain summaries or embeddings for later use, collect telemetry, or let the model invoke tools. Those choices affect what information the app assembles and what authority it gives the model. Local computation does not answer who can access that context, what derived data persists, or what happens on a cloud fallback.
Offline behavior and readiness
Android’s documented ML Kit GenAI APIs are designed to work without a reliable internet connection. That is useful only when the relevant model and API are available on the device. Some features may need a model download: Google’s May 2025 Android Developers Blog describes an API feature being downloaded when needed.
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Availability differs on Apple devices. In the cited Firebase AI Logic integration, on-device inference depends on an Apple Intelligence-enabled device. The model download is tied to enabling Apple Intelligence, and the app cannot initiate that system download itself. Design for a model that is still loading, unavailable, or unsupported—not just the successful, connected case.
Latency, device capability, and cost
Local execution removes the network round trip to a model server, but it does not guarantee a faster response. Google’s Android Developers documentation cautions that inference speed depends on device hardware. Memory, processing capability, model readiness, and the task itself all matter to the experience on a particular device.
Running inference locally can reduce cloud inference calls and their per-request infrastructure expense. It is not cost-free in every sense: the device performs the computation, and the app still has engineering, distribution, support, and potentially synchronization costs. Apple describes its Core AI framework as having no per-inference cost to the developer or app user; that statement applies to Apple’s framework, not every on-device implementation.
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How the documented Android and Apple options differ
These are specific documented routes, not a complete comparison of every Android and Apple model or SDK. The platform documentation accessed on October 4, 2026 describes the following capabilities; device support and APIs can change.
| Route | Documented local capability | Requirements and boundaries | Connectivity and fallback |
|---|---|---|---|
| Android ML Kit GenAI with Gemini Nano through AICore | ML Kit GenAI APIs document summarization, proofreading, rewriting, and image description. Android also documents a Prompt API. | Execution uses Gemini Nano through Android’s AICore system service. Inference speed depends on device hardware; model or feature readiness can involve a download. | Documented ML Kit GenAI APIs work without a reliable internet connection. A separate hybrid setup can use a cloud-hosted model when configured to do so. |
| Firebase AI Logic on Apple platforms, on-device integration | The cited integration supports on-device text generation from text-only input. | Requires an Apple Intelligence-enabled device and runs only in the foreground. The documented integration does not support all features available on other paths. | On-device inference can work offline. Cloud use requires connectivity; the SDK can indicate which inference path was used. |
Do not treat “supports on-device AI” as a promise of equivalent features across platforms. Check the exact SDK, OS and device requirements, model availability, input type, output type, and whether a feature is foreground-only before designing around it.
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A hybrid design can route a request to an on-device model when one is available and use a cloud-hosted model as an alternative. Firebase AI Logic documents this pattern for Android and Apple integrations. It can extend feature availability across devices or situations, but the cloud route changes the data boundary: a request that would otherwise stay on the device may leave it.
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Make that route understandable to users and explicit in the app’s behavior. Decide which requests may use the cloud, what prompt context is eligible to leave the device, what consent or disclosure is appropriate, and what the app should do when there is no network. Do not assume users will infer the route from the fact that the app has an offline-capable mode.
On the cited Apple integration, cloud use requires connectivity, and the SDK can report which inference path was used. Use that information to make routing observable in app logic and testing. A fallback should also have a defined failure state: a request may be unsupported locally, a model may not be ready, or the network path may be unavailable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate quality and speed on the devices people use
Model choice should be based on representative tasks and target devices, not a generic claim that local or cloud AI is better. Compare quality on realistic inputs, response time on actual supported hardware, device and OS coverage, offline behavior, model readiness, infrastructure cost, data routing, and what happens when inference fails. Repeat evaluations when model versions, datasets, or evaluation methods change; Apple notes that quality results can shift with a new dataset, judge, or model version.
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Google’s Android Developers Blog published the following benchmark scores in May 2025. These are vendor-published figures, not a vendor-neutral, cross-platform test; the cited material does not establish a general local-versus-cloud winner.
| Task | Gemini Nano base model | ML Kit GenAI API | Publisher and year |
|---|---|---|---|
| Summarization | 77.2 | 92.1 | Google, 2025 |
| Proofreading | 84.3 | 90.2 | Google, 2025 |
| Rewriting | 79.5 | 84.1 | Google, 2025 |
| Image description | 86.9 | 92.3 | Google, 2025 |
The same Google post reports Pixel 9 Pro reference measurements for text-to-text: prefix speed of 510 tokens per second and decode speed of 11 tokens per second. For image-to-text, it reports the 510-token-per-second prefix figure plus 0.8 seconds for image encoding, followed by 11 tokens per second for decoding. These are measurements from Google’s stated test conditions on that reference device, not a performance guarantee for other phones or tasks.
Use published figures to frame questions, not replace your own evaluation. Test representative prompts, expected output formats, difficult cases, and the actual hardware and model versions your app supports. Measure the whole user-visible wait—including readiness or download states where relevant—and define a quality threshold for routing to another model or offering a non-AI alternative.
Quick Recap
Implementation checklist: decide the boundaries before shipping
- Device support: Identify supported devices, OS versions, SDKs, and model capabilities. Confirm input and output types, including any foreground-only restriction.
- Model readiness: Decide how the app handles first use, download or initialization, an unavailable model, and unsupported hardware.
- Context access: Specify which local records the feature may read and how the app limits or explains that access.
- Retention: Define whether prompts, outputs, summaries, embeddings, or other derived data are saved, where they are saved, and how long they remain.
- Telemetry: Document what diagnostic or usage data is collected and whether it can contain prompts, personal context, or generated output.
- Action permissions: Limit the tools and actions a model can trigger, and decide which require user confirmation.
- Fallback routing: State when a request can go to the cloud, what data can accompany it, how the app makes that route clear, and what happens offline.
- Failure handling: Provide a useful state for slow, failed, unsupported, or low-quality results instead of implying that local AI is always available.
- Evaluation: Re-test quality and latency on representative tasks and supported devices when the model, SDK, dataset, or evaluation method changes.
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