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Short answer: a hosted parsing API sends your document outside your environment to a provider that may inspect it in memory, place it in temporary or durable storage, create intermediate artifacts, return extracted data, record request metadata, and allow limited personnel or subprocessors to support the service. The exact path depends on the provider, endpoint, request type, region, account tier, and retention settings. There is no industry-wide rule that an uploaded file is either immediately deleted or permanently stored.
Before sending confidential material, map the complete lifecycle: input bytes, intermediate files, output JSON or tables, logs, backups, support copies, and deletion controls. Then confirm the current product documentation, data-processing agreement (DPA), deployment configuration, and contract for the specific operation you will use.
What a hosted parsing API can receive and create
A parsing request normally contains more than the visible document. The provider may receive the original PDF, image, office file, or archive; authentication and routing details; page or file counts; and parameters that select an OCR, layout, table, or form model. During processing it can create page images, OCR text, layout coordinates, thumbnails, normalized files, embeddings, or other intermediate representations. The response may contain extracted text, fields, tables, confidence scores, and links to a result object.
Those data classes do not necessarily share one retention period. A service can discard the source after processing while keeping a result for asynchronous retrieval, or delete content while retaining request metadata for operations and abuse prevention. Support diagnostics, error traces, and backups can have separate controls. Ask about each class rather than accepting a single statement that the “document” is deleted.
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Request type determines the first retention decision
Synchronous (online) requests
A synchronous call keeps the client waiting for a response. Some services process the payload in memory and do not persist the document to disk. Google Cloud’s Document AI security documentation states: “For online (immediate response) operations, the document data (sent in the request) is processed in memory, encrypted in flight, and not persisted to disk.” That statement applies to Google’s online operation, not to every Google endpoint or another provider. The same documentation notes that request metadata, such as time received and request size, may be temporarily logged. See Google Cloud Document AI security documentation.
In-memory processing is not the same as zero data exposure. The bytes still leave your network, exist in service memory while the model runs, and may be visible to authorized systems that handle authentication, monitoring, or support. A timeout or client retry can also result in more than one processing attempt unless you use an idempotency design where the API supports it.
Asynchronous and batch requests
Batch jobs decouple upload from completion. The service needs a place to hold inputs while workers process pages and a place from which your client can collect results. Google says batch documents are typically deleted after processing, with a failsafe time-to-live of up to one day if normal cleanup does not occur. That is a service-specific maximum, not an industry standard. Microsoft’s Foundry Content Understanding documentation describes a different split: inputs and intermediate representations are deleted when processing completes, while output results can remain for up to 24 hours so an asynchronous client can retrieve them. Read the operation’s documentation, not just the provider’s general privacy page.
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Retries, callbacks, and result downloads
Polling a result endpoint, receiving a webhook, or re-running a failed job can extend the practical lifecycle. Your own queue, object storage, observability platform, and dead-letter handling may retain the same file after the provider has deleted it. Define when your application removes uploaded bytes, downloaded results, temporary working directories, and failed-job payloads. Treat provider deletion and customer-side deletion as separate tasks.
How selected services describe storage and deletion
| Service or operation | Input and intermediate handling | Output or artifact retention | Controls and qualifications |
|---|---|---|---|
| Google Cloud Document AI online | Processed in memory; documentation says request content is not persisted to disk. | Online response is returned to the caller; metadata may be temporarily logged. | Applies to online operations described by Google. See Google’s security documentation. |
| Google Cloud Document AI batch | Temporary storage is required for batch processing. | Typically deleted after processing; failsafe TTL can be up to one day. | Timing is Google’s stated behavior for this service and operation. |
| Microsoft Foundry Content Understanding | Inputs and intermediate representations are kept during processing. | Output results may be retained for up to 24 hours for asynchronous retrieval. | Processing geography depends on the selected deployment type. Details: Microsoft Content Understanding privacy documentation. |
| Extend | Documents and processing data are stored in encrypted object storage in the deployment region. | Retention follows the configured policy and billing tier; on-demand deletion is documented. | Its documentation describes a mode that deletes ephemeral and cached run data after processing. The default documentation version shown is v2026-02-09. See Extend Data Handling. |
| Microsoft 365 document processing | Organizational data is handled within the customer-tenant model described by Microsoft. | Storage follows applicable Microsoft 365 guidance rather than the Content Understanding 24-hour result rule. | This scope is not interchangeable with every Microsoft API or deployment. See Microsoft 365 document-processing privacy and security. |
The table illustrates why a vendor name alone is insufficient. “Microsoft” can refer to different products with different storage models, and “Google parsing” can mean online or batch processing. Match the documented behavior to the exact endpoint, operation, region, and account configuration you will deploy.
Are uploaded documents used to train models?
Training-use language is product-specific and can change by feature or contract. Google says it does not use Document AI customer content to train its models. Microsoft says organizational data in the Microsoft 365 document-processing services covered by its privacy page is not used to train AI models. These statements should be read within their stated product scope; they are not a promise about every API sold by either company.
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Anthropic documents zero-data-retention arrangements for eligible features, while emphasizing that retention is feature-specific. Eligibility, endpoint, commercial terms, and account configuration determine whether such an arrangement applies. The relevant documentation is Anthropic API data retention.
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When a provider says “not used for training,” ask whether the promise covers inputs, outputs, feedback, abuse-monitoring copies, human review, and derived metadata. Ask whether the term is a default, an opt-out, or a negotiated commitment, and record the answer in the DPA or order form rather than relying on a marketing summary.
Where processing occurs and who can access it
Region and residency
Sending a file to a regional endpoint does not automatically prove that every component stays in that region. Processing workers, object storage, backups, support systems, and subprocessors can have separate locations. Google documents residency and access controls for Document AI. Microsoft says Content Understanding geography depends on the deployment type. Confirm the allowed processing and storage countries for your selected tier, including disaster recovery and support operations.
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Encryption and identity controls
Check encryption in transit, encryption at rest, key-management options, private networking, service identities, and least-privilege roles. Encryption protects bytes from casual interception but does not prevent an authorized service operator, support workflow, or compromised account from accessing plaintext during processing. Determine whether customer-managed keys cover temporary files and result stores, or only durable storage.
Subprocessors and support personnel
A hosted API can rely on cloud storage, compute, monitoring, ticketing, or security vendors. Review the provider’s current subprocessor list, notice period for changes, locations, and purpose restrictions. For example, the OpenAI sub-processor list, last updated July 9, 2026, identifies third-party processing entities, locations, and purposes; it is not, by itself, a complete retention description for a particular parsing workflow. Ask whether support engineers can view payloads, what approval and audit controls apply, and whether you can disable content access for troubleshooting.
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- Define the exact workflow. Record provider, product, endpoint, synchronous or batch mode, model, region, tenant, and enabled add-ons. Retention claims are meaningful only with this scope.
- Separate data classes. Request the documented treatment of source files, page images, intermediate representations, extracted results, metadata, logs, backups, abuse-monitoring records, and support tickets.
- Get time windows in writing. Ask when each class is deleted, what “after processing” means, whether a failsafe TTL exists, and how failed or abandoned jobs are handled.
- Verify customer controls. Look for configurable retention, on-demand deletion, legal-hold behavior, customer-managed keys, private connectivity, and audit logs. Confirm which controls are available on your tier and actually enabled.
- Check model-use terms. Confirm whether content, outputs, feedback, and metadata may train models or be reviewed by humans. Capture any zero-data-retention or no-training commitment in the contract.
- Map geography and subprocessors. Identify processing, storage, backup, and support locations; review the DPA, transfer mechanism, and current subprocessor roster.
- Test deletion. Upload a synthetic document, retrieve its result, invoke the documented deletion process, and verify that result URLs, queued jobs, logs, and your own copies no longer expose the content. A test validates your configuration; it does not replace contractual guarantees.
- Set an internal deletion deadline. Delete local uploads and downloaded results as soon as the business process permits. Configure lifecycle rules on buckets, queues, caches, and observability systems.
Ways to reduce exposure without abandoning hosted parsing
- Minimize the payload: send only pages and fields required for the task; remove unrelated attachments and hidden document properties.
- Redact before upload: mask secrets, account numbers, or personal data that the parser does not need. Keep the mapping locally if the result must be re-associated.
- Use short-lived credentials: isolate the parsing service account, restrict it to the required operation and region, and rotate keys or tokens.
- Prefer ephemeral workflows: where supported, choose synchronous processing or an explicit zero-retention mode, but verify that asynchronous result storage is not silently enabled.
- Control observability: prevent request bodies and full responses from entering application logs, traces, crash reports, or analytics tools.
- Protect result data: extracted text can be as sensitive as the original image. Apply the same access, encryption, retention, and deletion policy to JSON, CSV, search indexes, and human-review queues.
What to do when the provider’s answer is unclear
Pause the upload and ask for the product’s current security and privacy documentation, DPA, retention schedule, subprocessor list, and region matrix. Request a written answer for the exact endpoint and account tier, including whether support or abuse-prevention systems can access content. If the provider cannot distinguish input, intermediate, output, and metadata retention, treat that uncertainty as a risk requiring legal and security review.
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For regulated records, a compliance badge or general statement is not proof that your deployment satisfies a particular law or contract. Confirm the applicable processing purpose, transfer mechanism, deletion obligation, audit rights, incident-notification terms, and customer responsibility in the agreement. Keep a dated record of the configuration and the documentation version you relied on, because hosted-service behavior and terms can change.
The practical bottom line
A hosted parsing API is a data-processing pipeline, not a black box with one universal retention rule. Synchronous processing may be memory-only, batch processing generally needs temporary storage, asynchronous results can outlive the source file, and metadata or support records can follow another schedule. Training use, geography, subprocessors, and deletion controls are equally specific to the product and feature. Upload confidential documents only after you have matched those details to the exact service configuration and put the required promises in your contract.
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