The only reliable way to tell where an AI feature runs is to check the disclosure for the exact feature and task, then verify any operating-system report available on your device. Look for explicit terms such as on-device, processed locally, cloud-based, server, remote, or a named model and provider. Do not assume an entire app is local or cloud-based: many systems use local inference for some requests and cloud inference for others.
What “runs on your phone” and “sends data to the cloud” mean
When an AI model performs on-device inference, the model processes your prompt or other input on the phone, tablet, or computer itself. The inference step can work without a server request when the feature is designed for offline use. Google describes this distinction directly: “On-device generative AI executes prompts locally, eliminating server calls.”
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Cloud inference means the input is transmitted to a provider’s servers, where a remote model generates the result. A cloud request can require an internet connection and is subject to the provider’s stated handling, retention, and security policies. A privacy-protected cloud service is still cloud processing because your request leaves the device.
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Hybrid inference combines both paths. A local model handles supported or smaller tasks, while a cloud model is used when the local model is unavailable, the request is too complex, or the task needs capabilities that are not installed on the device. Routing can therefore change between two prompts in the same app.
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| Processing route | What it means | Useful clues | Reader trade-off |
|---|---|---|---|
| On-device | The model processes the input on the phone or tablet. | Explicit local or on-device wording; documented offline operation. | Inference may work offline and keep that processing local, but hardware and model size limit capability. |
| Cloud | The input is sent to a provider’s servers for model inference. | Cloud or server wording, or a named cloud model. | Remote models can handle larger inputs or more capable processing, but network dependence and provider data handling apply. |
| Hybrid | Local processing is used when possible, with remote processing for selected tasks or conditions. | Fallback language, “when unavailable,” “complex request,” or named local and cloud models. | You may get offline privacy for some work and cloud capability for other work, but the route varies by task and device. |
Why the app’s overall privacy label is not enough
Routing is usually feature-specific
An app can contain a small local model for summarizing text and call a remote service for image generation. Its general privacy page may describe account, diagnostics, or advertising data without stating where each AI prompt is processed. Read the disclosure for the exact button, mode, and task you intend to use.
The same feature can have different paths
Routing may depend on the prompt, file size, language, device model, operating-system release, account tier, region, or whether a local model is installed. A feature that works without connectivity for a short note may send a longer document to a server. A model update can also change the default path.
“On-device” covers inference, not every network action
When a vendor says a prompt runs on-device, that statement describes the model inference step. The app might still contact servers for sign-in, model updates, crash reports, usage analytics, synchronization, or feature availability. Google’s Gemini Nano documentation says local prompts execute without server calls, while the broader Android architecture can still update the model and route other requests remotely. Treat the local claim as applying to the documented inference path, not as a promise that the whole app is network-silent.
A practical method for checking any AI feature
- Identify the exact context. Record the app or system feature, the task you are performing, the app version, and the operating-system version. “AI assistant” is too broad; “rewrite this paragraph in the keyboard’s proofreading mode” is specific enough to investigate.
- Read the feature’s privacy and processing disclosure. Search the settings, help page, and privacy notice for on-device, local, server, cloud, remote, upload, fallback, and the names of models or providers. Prefer a statement tied to the actual task over a marketing statement about the product as a whole.
- Identify the model or implementation. A named local model, such as Gemini Nano running through Android AICore, points to an on-device path for that implementation. A named cloud model, such as Gemini Flash or Gemini Pro used through a cloud API, points to remote inference. If the documentation names both, look for routing conditions.
- Check the operating-system evidence. Apple devices provide a built-in report for Apple Intelligence requests processed by Private Cloud Compute. Android’s developer documentation explains Gemini Nano and hybrid routing, but it is architecture guidance rather than a universal consumer log for every app.
- Compare the same task online and offline when safe. Turn on airplane mode, try a non-sensitive test prompt, and note whether the feature still returns a result. Offline success is evidence that this particular task can run locally under those conditions; it does not prove the app never sends analytics, setup traffic, or other tasks to a server.
- Make a data decision. If the route or retention period remains unclear, do not enter passwords, health details, confidential work, private photographs, or identifying information. Wait for a clear provider explanation or use a function documented as local for the required task.
How to check Apple Intelligence processing
Use the Apple Intelligence and PCC report
On iPhone, iPad, and Vision Pro, open Settings > Privacy & Security > Apple Intelligence & PCC Report. On Mac, open System Settings > Privacy & Security > Apple Intelligence & PCC Report. Apple says this transparency logging shows requests sent off the device and processed by Private Cloud Compute (PCC), including requests originating from watchOS.
The report is direct evidence that a request used PCC. It is not a network history for every Apple feature or for third-party apps. A request absent from the report should not be interpreted as proof that no network traffic occurred; it means the report did not record that request as PCC processing.
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Understand Apple’s split between local and PCC work
Apple says many Apple Intelligence models run entirely on-device so a task can finish without data leaving the device, and gives Priority Notifications as an example. Apple also says that tasks needing more computing power may use PCC. Its example is Image Playground sending a user’s description for a server model. Apple’s privacy document states: “For these tasks, Apple Intelligence may send your request to Private Cloud Compute.”
Interpret Apple’s stated PCC protections accurately
Apple says PCC data is used to fulfill the request, is not stored or made accessible to Apple, and is not retained after processing. Those are Apple’s stated design and policy commitments, not independent testing. Apple also says it may collect limited request metadata, including approximate request and response size, the feature used, and duration; that metadata excludes the request and response content.
Account for report limitations
Apple notes that the report may not reflect recent requests if Face ID or Touch ID is temporarily disabled. Check that condition before treating a missing recent entry as conclusive.
How to check Android AI processing
Recognize Gemini Nano and AICore
Google documents Gemini Nano as an on-device model that runs through Android’s AICore system service. The documented ML Kit generative-AI tasks include prompting, summarization, proofreading, rewriting, image description, and speech recognition. This is developer-facing documentation: a consumer app must actually identify Gemini Nano or AICore for the description to establish that its implementation uses that local path.
Google’s Android guidance describes cloud Gemini models, including Gemini Flash and Gemini Pro used through Firebase AI Logic, as remote inference. If an app names one of those cloud models, the relevant input is sent to the provider’s servers for that operation.
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Look for hybrid or fallback wording
Google says a hybrid solution can use local models for small inputs and cloud models for larger data or when extra knowledge is required. Its Gemini Nano documentation also says hybrid inference can automatically route requests to cloud models when Nano is unavailable or unsupported. In practice, phrases such as fallback, when the local model is unavailable, complex queries, or larger inputs mean that an apparently local feature may still send some requests off-device.
Google summarizes the purpose of this design as follows: “To ensure a consistent user experience across all devices and handle complex queries, you can use the Firebase AI Logic Hybrid API.” That statement describes an application architecture, not a guarantee that every Android app uses the same routing.
Do not mistake developer documentation for a personal request log
Android’s official guides explain how developers can choose Nano, cloud Gemini models, or a hybrid API. They do not provide one universal, consumer-facing history of every AI request made by every app. For a particular app, its own privacy notice, model label, settings, and network behavior remain necessary evidence.
How to read the strongest and weakest clues
| Clue | What it establishes | What it does not establish |
|---|---|---|
| “Processed locally” or “on-device” tied to the named task | The documented model inference for that task runs on the device. | That the entire app makes no network connections. |
| A named cloud or server model | That the named operation uses remote inference. | That every other feature in the app is cloud-based. |
| Hybrid, fallback, or “when unavailable” language | That routing can switch between local and remote models. | Which path every individual prompt will take without the stated conditions. |
| Offline success in a controlled test | The tested task worked without a connection at that moment. | That other tasks, analytics, updates, or setup requests never use a server. |
| A privacy-preserving cloud design | The provider describes safeguards for remote processing. | That the request stayed on the phone. |
| A general “private AI” or “secure AI” slogan | Very little about processing location unless the policy defines it. | The model’s location, retention, or routing for your task. |
Using an offline test without drawing the wrong conclusion
Set up a controlled, harmless test
Choose a short, non-sensitive prompt whose expected result is obvious. Disable Wi-Fi and mobile data, or enable airplane mode while keeping the device otherwise unchanged. Run the exact task you want to evaluate and record whether it completes, displays an error, or asks for a connection. Repeat after reconnecting because a hybrid feature may choose a different route when online.
Separate inference from supporting traffic
A result produced in airplane mode shows that the model had enough local resources for that prompt. It does not reveal whether the app later uploads usage data, checks an account, downloads a model update, or sends a different task to a server. Conversely, a failure offline does not prove that all successful requests are cloud-based; the feature may require a one-time sign-in or model download while still performing inference locally.
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Test the conditions that matter
If your normal use involves long documents, image attachments, voice input, or a particular language, test that same type of input. A small text prompt can stay local while a larger file exceeds the local model’s supported capacity and triggers a cloud fallback.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Privacy questions to answer before entering sensitive information
- Where is inference performed? Is the answer explicitly local, explicitly remote, or conditional?
- What exactly is transmitted? The prompt may be accompanied by an image, document, audio clip, account identifier, or device metadata.
- How long is content retained? A provider’s statement about processing location does not by itself answer retention.
- Is the policy about this feature? A platform-wide privacy page may not describe a newly added model or a third-party integration.
- Can routing change? Look for device support, region, model availability, input-size limits, and fallback conditions.
- What evidence can you inspect? Use an operating-system report when available, then combine it with the feature’s documentation and a safe offline test.
Apple’s PCC explanation illustrates why processing location and privacy safeguards are separate questions: Apple says PCC requests are sent to its servers, while also describing limits on storage, access, and retention. Decide whether that remote handling fits your requirements rather than treating “private cloud” as synonymous with “local.”
Common mistakes and how to correct them
Assuming one company uses one processing route
Large platforms often combine local and remote models. Replace the question “Is this platform on-device?” with “Where does this specific task run on this device and software version?”
Treating a model name as a blanket promise
Gemini Nano indicates an on-device implementation when the app actually uses Nano through AICore. A hybrid API can still route to a cloud model. Confirm the implementation and fallback behavior instead of relying on the product family name.
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Offline behavior is a useful experiment, not a complete privacy audit. Pair it with the disclosure and, on Apple platforms, the PCC report.
Reading “no content stored” as “nothing left the phone”
A provider can send a request to a server, process it, and state that it does not retain the content. That may be a meaningful privacy protection, but it remains remote processing.
Ignoring software updates
Model availability, routing rules, settings labels, and report details can change with operating-system and app releases. Recheck after a major update or when a feature gains a new mode.
A repeatable checklist
- Name the exact feature, task, app version, and operating-system version.
- Find the feature-specific privacy or processing statement.
- Mark every term that indicates local, cloud, or conditional processing.
- Note the named model, API, or system service.
- Check for fallback triggers such as unsupported devices, large inputs, or complex requests.
- Inspect Apple’s Apple Intelligence & PCC Report when the feature is Apple Intelligence.
- On Android, confirm whether the app identifies Gemini Nano/AICore, a cloud Gemini model, or a hybrid API.
- Run a harmless online/offline comparison for the exact task, without sensitive data.
- Review what the provider says about content, metadata, retention, and account information.
- Keep sensitive input out when the route or handling remains unclear.
What can change over time
The Apple privacy documentation describing Apple Intelligence and PCC was dated September 14, 2026. Google’s Android documentation includes a Gemini Nano page last updated October 8, 2026 UTC. Those documents describe the systems at those points in time; device support, regional availability, model versions, fallback behavior, and settings can change. Verify the labels and paths on the exact release you are using before relying on an old screenshot or a remembered result.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe most dependable conclusion comes from combining three kinds of evidence: a statement for the specific feature, the model or API actually named by the app, and an operating-system report or controlled test where one exists. Any one clue by itself can be incomplete.
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