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The reliable answer is feature-specific, not brand-specific. Open the privacy or processing disclosure for the exact AI tool and task, then look for terms such as on-device, processed locally, cloud-based, server, remote, or a named model and provider. A single app can use local inference for one action and send another action to a cloud service.
Use the operating system’s own evidence where available. Apple provides an Apple Intelligence & PCC Report that lists requests sent off-device to Private Cloud Compute. On Android, Gemini Nano and Android AICore indicate an on-device path for that implementation, while Gemini Flash, Gemini Pro, Firebase AI Logic, or explicit hybrid and fallback language indicate cloud routing may occur. Settings, model availability, and routing can change with device, region, app, and operating-system releases.
What “runs on your phone” and “sends data to the cloud” actually mean
On-device inference means the model processes the prompt or other input on the phone or tablet itself. The input used for that inference does not need to be sent to a model server. This can enable the feature to work without an internet connection, although the app may still perform unrelated network activity such as account checks, synchronization, crash reporting, advertising, or model updates.
Cloud inference means the input is transmitted to a provider’s servers, where a remote model processes it and returns a result. A named cloud model, server-based service, or explicit statement that your request is uploaded for processing is direct evidence of this route.
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Hybrid inference combines both. The phone handles supported or smaller requests locally, while the service sends larger, more complex, or otherwise unsupported requests to a cloud model. A hybrid feature can therefore be offline for one task and online for another without either description being false.
| Processing route | What happens | Clues in documentation | Main trade-off |
|---|---|---|---|
| On-device | The model runs on the phone or tablet. | “On-device,” “local,” “processed locally,” or a documented offline mode. | Better local control and possible offline use, but hardware, memory, battery, and model capability limit what it can do. |
| Cloud | Your input travels to a provider’s servers for model inference. | “Cloud,” “server,” “remote,” a named cloud model, or an upload statement. | Access to larger or more capable models, with network dependence and the provider’s data-handling policies. |
| Hybrid | Routing changes according to task, device, availability, or complexity. | “Fallback,” “when the local model is unavailable,” “complex requests,” or local and cloud models listed together. | Balances local operation and cloud capability, but you must determine which conditions trigger each path. |
Why a feature’s label is not enough
Marketing names usually describe a product family, not the route used for every request. An app branded as an “AI assistant” may use a small local model for rewriting and a remote model for research-style questions. A phone feature marketed as private may still send selected tasks to a provider’s protected cloud environment.
Even an explicit on-device claim has a limited scope. It normally describes model inference for the named implementation. It does not automatically promise that no telemetry, setup data, account information, diagnostics, or usage statistics leave the device. Treat “on-device” as an answer to where this model inference runs, not as a blanket statement about every network connection made by the app.
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Likewise, an offline test is useful evidence for the task you tested under those conditions. It is not proof that the app never connects for other tasks or background services. Hybrid systems can also change behavior when a local model is missing, disabled, outdated, or unable to handle the request.
A practical method for checking any AI feature
- Name the exact feature and task. Record whether you are summarizing a notification, rewriting text, describing an image, transcribing speech, generating an image, or asking a general question. Routing can differ between these actions.
- Record the software context. Note the phone or tablet model, operating-system version, app version, account or subscription tier, and region. Model availability and fallback rules can vary across these conditions.
- Read the feature-specific disclosure. In settings, help, privacy, or developer documentation, search for “on-device,” “local,” “server,” “cloud,” “remote,” “upload,” “retention,” and the names of models or providers. Prefer a page that describes the exact feature over the app’s general privacy policy.
- Identify the model or implementation. “Gemini Nano,” “AICore,” or another explicitly named local model points toward on-device processing for that path. A named cloud model or a cloud API points toward remote inference. If both appear, look for the conditions that select between them.
- Check for fallback language. Phrases such as “when the local model is unavailable,” “for larger inputs,” “for complex queries,” or “hybrid” mean the request can leave the device under those conditions.
- Use an operating-system report when one exists. Apple’s Apple Intelligence & PCC Report is direct evidence for requests handled by Private Cloud Compute. On Android, the available developer documentation explains architecture but does not provide a universal consumer-facing history for every app.
- Run a controlled offline comparison if appropriate. Try the same task with connectivity disabled, then repeat it online. Record the exact task and result. A task that works offline supports an on-device conclusion for that test, but does not rule out other network activity or cloud fallback in a different situation.
- Do not enter sensitive material while the route is unclear. If the disclosure does not explain where prompts go, how long they are retained, or who can access them, wait for a clearer statement or use a different workflow.
How to check Apple Intelligence requests
Find the Apple Intelligence & PCC Report
Apple documents a built-in transparency report for Apple Intelligence requests processed by Private Cloud Compute (PCC). 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 the report shows requests sent off-device and processed by PCC, including requests originating from watchOS. That makes it stronger evidence than a general statement that Apple Intelligence is “private,” because it records the relevant class of remote processing for the Apple Intelligence system.
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Interpret what the report does and does not prove
A request appearing in the report means that task was sent to PCC rather than completed entirely on the device. Apple gives Priority Notifications as an example of a task that can run entirely on-device. It also says that some requests requiring more computing power may use PCC; Image Playground is Apple’s example of a feature that can send a user’s description for server-side model processing.
Apple’s exact wording is: “For these tasks, Apple Intelligence may send your request to Private Cloud Compute.” This is a statement about Apple’s documented system behavior, not a guarantee that every Apple Intelligence feature uses the same route.
Apple also states that PCC data is used to fulfill the request, is not stored or made accessible to Apple, and is not retained after processing. Present that as Apple’s stated design and policy. It does not change the basic routing fact: a PCC request is sent to Apple servers.
Apple says it may collect limited request metadata, including approximate request and response size, the feature used, and duration, while excluding request and response content. The report may not reflect recent requests when Face ID or Touch ID is temporarily disabled, so an apparently incomplete list should not be treated as proof that no recent request was sent.
Check the feature disclosure as well as the report
The report covers Apple Intelligence requests handled by PCC; it is not a universal network log for every third-party app on the device. If another app embeds an AI service, read that app’s own privacy and processing explanation. Do not use an empty Apple report to conclude that unrelated apps are entirely local.
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How Android’s Gemini documentation distinguishes local and cloud processing
Gemini Nano and Android AICore
Google’s Android developer documentation describes Gemini Nano as an on-device model that runs through the Android AICore system service. Documented ML Kit generative-AI tasks include prompting, summarization, proofreading, rewriting, image description, and speech recognition. Google’s exact statement is: “On-device generative AI executes prompts locally, eliminating server calls.”
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This is developer-facing documentation. It identifies the behavior of an app using Gemini Nano and AICore; it does not prove that every consumer app labeled with “Gemini” uses Nano or that every feature in one app stays local. Confirm the implementation named by the specific app.
Cloud Gemini models
Google’s Android AI selection guidance distinguishes Gemini Nano from Gemini Flash and Gemini Pro used through Firebase AI Logic as cloud models. If an app names one of those cloud models, describes a server request, or identifies Firebase AI Logic for the task, the inference path is remote.
Hybrid APIs and automatic fallback
Google describes hybrid solutions that use a local model for small inputs but a cloud model for larger data or when extra knowledge is required. Its documentation says Gemini Nano hybrid inference can automatically route requests to cloud models if Nano is unavailable or unsupported. Google also writes: “To ensure a consistent user experience across all devices and handle complex queries, you can use the Firebase AI Logic Hybrid API.”
For a real app, look for both halves of the implementation: the local model and the cloud fallback, plus the conditions that trigger switching. A device may complete a short rewrite locally but send a long document or a question requiring broader knowledge to the cloud.
How to interpret offline behavior correctly
What an offline success tells you
If the same feature produces a result with Wi-Fi and cellular data disabled, the tested operation likely had a local execution path under those conditions. This is especially useful when it agrees with an explicit on-device model disclosure.
What it cannot tell you
- It does not show whether the app sent analytics, account, diagnostic, or synchronization data before you went offline.
- It does not establish that a different task, larger input, or later release will remain local.
- It does not rule out cloud fallback when connectivity returns or when the local model is unavailable.
- It does not reveal retention or access practices for any data that the app does send.
A safer test procedure
- Write down the exact prompt or input, without using confidential information.
- Force-close and reopen the app if that is practical, then disable both Wi-Fi and cellular data.
- Run the task and record whether it completes, fails immediately, or offers a delayed result.
- Restore connectivity and repeat the identical task.
- Compare the results, response time, and any message that mentions a local model, server, or fallback.
- Use the documentation and any platform report to explain the observation; do not treat the experiment alone as a complete privacy audit.
How to read privacy wording without being misled
Strong evidence
- An explicit statement that the named model processes prompts locally.
- A named cloud model or server service tied to the exact feature.
- A platform report listing a request sent off-device.
- Clear fallback rules that state when local processing changes to cloud processing.
Weak or incomplete evidence
- A broad “private AI” or “secure AI” slogan without a processing description.
- An app’s general claim that it “uses AI on your device” when the page does not identify which tasks or models.
- A successful offline test used to characterize the entire app.
- A statement about encryption or protected cloud infrastructure used as if it meant the request never left the phone.
Privacy protection and local processing are different properties. A provider can describe strong safeguards for a cloud request while still receiving the prompt. Apple’s stated PCC protections are an example: the service is designed not to retain request content, but the request is nevertheless sent to Apple servers for processing.
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What changes the processing route
Task complexity and input size
Hybrid systems commonly keep short or supported operations local and move larger inputs or more demanding requests to a cloud model. A summary of a short notification and a summary of a long document may therefore follow different routes.
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Local models depend on compatible hardware, memory, operating-system components, and sufficient storage. If the local model is unavailable or unsupported, a hybrid API may use the cloud path instead, or the feature may fail.
Software and regional releases
Routing, model availability, report details, and settings labels can change with app and operating-system releases. Apple’s privacy documentation was dated September 14, 2026, and Google’s Gemini Nano documentation was last updated October 8, 2026 UTC; verify the current version on the device being assessed.
Connected accessories and companion devices
Apple’s PCC report includes requests originating from watchOS. If a phone, watch, tablet, or computer shares an AI feature, check which device generated the request and which system’s report or disclosure covers it.
A decision checklist for sensitive prompts
Before submitting medical details, financial records, private communications, confidential work, or another sensitive input, require all of the following:
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- The exact feature and task are identified.
- The documentation names the processing route or clearly describes its fallback behavior.
- You know whether the route is local, cloud, or hybrid for the conditions you will use.
- Cloud retention, access, and deletion practices are stated clearly enough for your risk level.
- Any available operating-system report has been checked for the relevant period.
If one of these points is missing, use non-sensitive sample text or avoid the feature until the provider explains the route. Do not assume that a VPN, privacy screen, or network monitor can reliably identify the model path for a particular AI prompt; the useful evidence is the feature’s own disclosure and the platform’s processing report.
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Common mistakes and their fixes
Mistake: treating a brand as wholly local or wholly cloud
Fix: Identify the model and task. The same product family can contain on-device, cloud, and hybrid implementations.
Mistake: confusing cloud privacy with local processing
Fix: Ask two separate questions: did the input leave the device, and what protections apply after it arrived? A protected cloud service is still a cloud service.
Mistake: using one successful offline test as a universal answer
Fix: Repeat the check for each materially different task and account for fallback conditions.
Mistake: reading only the app’s general privacy policy
Fix: Find the processing disclosure for the exact AI feature, model, and version.
Mistake: assuming an empty platform report means no app sent data
Fix: Understand the report’s scope. Apple’s report covers Apple Intelligence requests processed by PCC, not every third-party app’s traffic.
The shortest reliable answer
To tell whether an AI feature runs on your phone or sends data to the cloud, identify the exact task, read its processing disclosure, look for a named local or cloud model, and check for fallback rules. Use Apple’s Apple Intelligence & PCC Report when the feature is Apple Intelligence. On Android, distinguish Gemini Nano and AICore from cloud Gemini models and hybrid APIs. Confirm with a carefully documented offline comparison, but do not mistake offline operation for proof that the entire app is network-free.
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