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An AI aggregator may pass your prompt or file to the model provider that answers it, so the aggregator is not necessarily the only service handling your data. What happens next depends on the aggregator, the selected provider and route, and features such as logging, persistent file storage, memory, bots, or shared chats. Check each layer before sending sensitive information.
How prompts and files move through an AI aggregator
A useful way to picture the usual flow is: you send input to the aggregator, it routes that input to a selected or automatically chosen model provider, and the provider’s response returns through the aggregator. OpenRouter and Poe describe versions of this flow in their official disclosures; it is not a guarantee about every product called an AI aggregator.
That route can involve separate handling by the aggregator and the downstream provider. The aggregator may process account, device, usage, and request metadata, while the provider processes the prompt or file to produce a result. Optional logging, file storage, screening, memory, bot permissions, or shared-chat features can add further copies or access paths. A setting that controls the aggregator’s own logs does not necessarily control the provider’s retention or training practices.
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There is no universal answer. OpenRouter’s documentation says it does not store prompts or responses unless users opt in to one or both of its documented content-logging options. Its documentation also says it retains request metadata, such as token counts and latency, even when prompt and response content is not logged. Its privacy policy separately describes persistent Files API uploads, which remain until deletion or account closure, subject to stated exceptions and applicable deletion timeframes. See OpenRouter’s data collection documentation and its privacy policy.
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Those statements concern OpenRouter’s own handling, not every provider reached through it. OpenRouter says provider retention and training practices vary. Its terms describe the service as an aggregator providing access to third-party model APIs; the terms page reports a last-updated date of August 31, 2026. Read the current terms for the specific model route you use: OpenRouter Terms.
Can an AI aggregator use your prompts to train models?
Training use varies by service, provider, and feature. OpenRouter says some model providers may use inputs and outputs for training or improvement. Its own prompt and response logging and use of that content for product improvement are documented as opt-in rather than on by default; this does not establish what a downstream provider does.
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Poe distinguishes between official bots using listed model providers and third-party developer bots or apps. Its Privacy Center says a fully shaded privacy shield indicates provider sharing and that those interactions will not be used by model providers for training. A half-shaded shield indicates data may also be shared with bot or app developers or external resources, and may be used for training. Poe’s wording is explicit: “Your interactions with the bot or app may be used for training purposes.” Check the shield and details for the bot or app you select in Poe’s Privacy Center; the page reports a last-updated date of April 30, 2026.
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“Upload” can mean sending a file with a request so a model can analyze it, or placing a file in persistent storage for later use. These are different handling paths. OpenRouter says image, audio, and video supplied as inference inputs are sent to the chosen provider and are not persisted by OpenRouter beyond routing, except as needed for operational, security, billing, or legal purposes. The selected provider receives files included with inference requests and applies its own data terms.
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By contrast, OpenRouter says files uploaded separately through its Files API are retained until you delete them or close your account, subject to policy exceptions. The policy also says stored Files API uploads are screened and transmitted as needed to fulfill requests. These are OpenRouter-specific disclosures, not a general rule for all aggregators. Its privacy policy explains the distinction and notes that each model provider processes uploaded files under its own data-handling, retention, and use terms.
Which features can create additional data paths?
- Automatic routing: The aggregator may choose a provider or route a request automatically, so the destination may differ from the one you expected. Confirm the actual route where the service exposes it.
- Prompt and response logging: An optional logging or improvement setting can retain content at the aggregator even if the default is not to log it.
- Persistent file storage: A file saved for reuse may remain until you delete it or close the account, unlike a transient inference attachment.
- Third-party bots and apps: Developers or external resources may receive data in addition to the provider that runs the model.
- Memory: Saved context can be passed to other model providers. Poe says memory summaries may remain in background memory after a chat is deleted until memory is separately deleted.
- Group chats: Other participants may see interactions that would otherwise be private to a one-to-one chat.
Poe says deleting chats removes them from Poe systems, but it distinguishes chat deletion from removing any related memory summaries. For OpenRouter, deletion of persistent Files API uploads is subject to stated exceptions and applicable deletion timeframes. Neither disclosure supports assuming that a deletion action immediately removes data from every downstream provider, backup, or legally retained system.
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What to check before sharing sensitive information
- Identify the service and route. Check the selected model and provider, and whether automatic routing can change the destination. Review the provider’s own current terms as well as the aggregator’s.
- Review content settings. Find whether prompt and response logging or product-improvement use is enabled, optional, or unavailable. Determine whether that control applies only to the aggregator or also constrains the provider.
- Check the file-handling mode. Establish whether the attachment is only an inference input or is being placed in persistent storage; look for retention, deletion, and exception terms.
- Inspect feature access. Review bot or app permissions, privacy indicators, memory settings, and whether the conversation is shared with a group.
- Understand deletion scope. Use the service’s deletion controls, then check whether provider, memory, backup, or legal-retention terms apply separately.
For confidential, personal, client, or regulated data, do not submit it unless you have verified the commitments that apply to both the aggregator and the actual provider route for your use case. The official policies discussed here describe particular services and do not establish a universal retention period or privacy guarantee for AI aggregators.
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