There is no single answer. A hosted parsing API might handle a synchronous upload in memory and discard it, place a batch file in temporary object storage, keep extracted results for a short retrieval window, or retain processing artifacts under a configurable policy. Training use, logging, region, subprocessors and support access also differ by product, endpoint, account tier and contract.
Before sending a confidential document, identify the exact operation you will call and verify its current privacy documentation, data-processing agreement, deployment region, retention settings and deletion behavior. Treat the provider’s statement as a description of that service and configuration—not as a rule for every parsing API.
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What actually leaves your environment
When your application calls a hosted parser, the provider can receive more than the visible document. The payload may include the file bytes, page images, a URL or object-storage pointer, instructions, authentication context and fields that identify your tenant. During processing, the service may create intermediate representations such as rendered pages, OCR text, layout coordinates, tables, embeddings or validation logs. The response can contain extracted fields, confidence scores and error details. Request metadata—such as arrival time, size, IP address, operation name and status—may be logged even when document content is not retained.
These are separate data classes with potentially different lifetimes. A policy that says “documents are deleted” may refer only to the original input, while output JSON, temporary page images, diagnostic traces or backups follow another schedule. Ask for retention of each class explicitly.
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Input content
The original PDF, image or office file is the most obvious transfer. TLS protects it while in transit, but encryption in transit does not tell you whether the service writes it to disk after arrival. Synchronous processing may avoid durable storage; asynchronous workflows generally need a place to hold the input until workers finish.
Intermediate artifacts
Parsers often convert a PDF into page images or normalized text before recognition. These artifacts can be more revealing than the source—for example, an OCR text file is searchable and easy to copy. Determine whether intermediate data is deleted at completion, placed in a customer-controlled bucket, or covered by a separate cache policy.
Results and metadata
Extracted JSON, searchable text and confidence data may remain so a client can retrieve a job later. Metadata can remain for billing, abuse prevention, reliability or analytics. Request a retention statement for results and logs rather than assuming that deleting the upload removes every derivative.
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Synchronous or online calls
An online request returns a result in the same connection. It can be processed in memory, but that is a service-specific implementation detail. Google Cloud Document AI 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.” The statement applies to Google’s online Document AI operations, not to hosted parsers generally. Google also says some request metadata is temporarily logged, including the time a request was received and its size. Google Document AI security documentation
Batch and asynchronous jobs
Batch APIs accept work and return a job identifier. The service needs temporary storage while workers process pages and while the client waits for completion. Google says batch documents are typically deleted immediately after processing, with a failsafe time-to-live of up to one day. That is a Google Document AI behavior; another vendor may use a longer queue, customer-managed storage or a configurable schedule.
Microsoft Foundry Content Understanding describes a different asynchronous pattern: inputs and intermediate representations are deleted when processing completes, while output results may be retained for up to 24 hours so a client can retrieve them. The 24-hour period is a maximum stated for that service, not an industry average. Microsoft Content Understanding privacy documentation
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Callback, queue and replay behavior
A webhook or queue can extend the data path. The provider may retain a result until your callback acknowledges it, and your own queue, logs and failed-message store can then hold copies. Document the provider’s retry period and purge your receiver’s logs and dead-letter queue. If you need a second attempt, prefer an idempotent job identifier over uploading the same sensitive file repeatedly.
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Compare input, intermediate, output and metadata retention separately. A useful contract review records whether a period is automatic, configurable, customer-controlled or merely a provider target.
| Service or documentation scope | Input and intermediate handling | Output handling | Controls and qualifications |
|---|---|---|---|
| Google Cloud Document AI | Online content is processed in memory and not persisted to disk, according to Google. Batch documents are typically deleted after processing, with a failsafe TTL of up to one day. | Retention details depend on the operation and related storage; verify the selected workflow. | Google describes residency, access approvals and temporary request-metadata logging. The statements apply to Document AI documentation, not all Google APIs. |
| Microsoft Foundry Content Understanding | Inputs and intermediate representations are kept during processing and deleted when the operation completes. | Results may remain for up to 24 hours for asynchronous retrieval. | Processing geography depends on deployment type; confirm the region and endpoint you choose. |
| Extend | Documents are stored in encrypted object storage in the deployment region; ephemeral and cached run data can be deleted after processing in its zero-data-retention mode. | Retention policies are tied to billing tier and can be configured; on-demand deletion is described. | Check the tier, enabled mode and current documentation version (the page identifies v2026-02-09 as its default). |
Do not read “up to 24 hours,” “typically deleted” or “configurable” as guarantees for every account. The selected endpoint, region, plan and contractual addendum can change the result.
Are documents used to train AI models?
Training-use language is product-specific. Google says it does not use Document AI customer content to train its models. Microsoft says organizational data in its described Microsoft 365 document-processing services is not used to train AI models. The Microsoft 365 statement covers that documented service scope; it is not a blanket promise about every Microsoft API, Foundry feature or deployment.
Some providers offer a zero-data-retention arrangement only for eligible features. Anthropic’s API documentation describes zero-data-retention for qualifying features under an arrangement and emphasizes that retention is feature-specific. Read the eligibility conditions rather than assuming that every endpoint receives the same treatment. Anthropic API data-retention documentation
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A “not used for training” promise does not mean “never stored.” A provider may retain content to deliver the service, prevent abuse, debug an incident or satisfy a legal obligation while excluding it from model training. Ask separately about training, human review, automated quality analysis and support access.
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Where processing occurs and who can access it
Region and residency
Data residency has at least two dimensions: where computation runs and where persistent storage, backups and support systems reside. Google documents residency and access controls for Document AI. Microsoft says Content Understanding geography depends on the deployment type. A region selector may govern primary processing but not every subprocessor or support event, so obtain the provider’s region and transfer description for your exact configuration.
Encryption and keys
Look for encryption in transit and at rest, key-management choices, rotation, and whether customer-managed keys cover temporary files and backups. Encryption reduces exposure but does not answer who can decrypt data during an authorized support session. Verify whether keys are held by the provider, your cloud tenant or a hardware-security module under your control.
Provider and support access
Operational staff may need controlled access to diagnose failed jobs. Check role-based access, approval workflows, session logging, break-glass procedures and whether support access is opt-in. Ask whether your administrators can disable content access for support and whether the provider can provide an access report.
Subprocessors
A parser can rely on cloud storage, OCR engines, queue providers or observability vendors. Review the current subprocessor list, locations, purposes and notice mechanism. OpenAI’s published list, for example, identifies third-party processing entities, locations and purposes and was last updated July 9, 2026; it is a source about subprocessors, not a complete retention description for any particular parsing workflow. OpenAI subprocessor list
A practical due-diligence checklist
Use this sequence before uploading production documents.
- Describe the exact operation. Record provider, API version, endpoint, region, synchronous or batch mode, file type, maximum size and whether you enable human review, search indexing or model-based enrichment.
- Map every data class. List original files, page images, OCR text, intermediate JSON, final results, prompts or instructions, request metadata, logs, backups and support tickets.
- Capture each retention rule. Write the normal period, maximum failsafe period, deletion trigger, backup treatment and whether your account can shorten or disable retention.
- Confirm training and review terms. Require a clear statement covering model training, quality-improvement analysis, human review and abuse monitoring for the selected feature.
- Verify geography and transfers. Identify processing, storage, backup and support locations, plus the legal transfer mechanism that applies to your organization.
- Review access and subprocessors. Check encryption, customer-managed keys, administrative roles, support controls, audit logs and the current subprocessor notice.
- Test deletion. In a non-production account, submit a harmless file, retrieve the result, invoke any deletion API, and confirm what disappears from the customer console, object store and job history. Ask the provider how backups and legal holds are handled.
- Put the answer in the contract. Attach the data-processing agreement, service-specific privacy terms, region selection and retention settings to your records. A marketing page alone is not the whole agreement.
Designing a safer integration
Minimize before upload
Remove pages, annotations and hidden metadata that the parser does not need. Redact unnecessary identifiers locally, split a large case file into the smallest useful units and avoid sending an entire mailbox when one document is sufficient. Keep the original in your controlled storage and send a short-lived object reference only when the API supports customer-managed retrieval.
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Control copies in your system
Disable request-body logging, redact document fields from application traces and set expiration rules on upload buckets, queues, caches and failed-job stores. Encrypt local staging and restrict the service account to the exact objects and operations required. Make deletion idempotent so a retry cannot recreate an old copy unintentionally.
Separate extraction from long-term storage
Persist only the fields your business process needs. If you must retain the source for legal or audit reasons, keep it in your governed repository and store a reference and integrity hash with the extracted result. Do not assume the parser is an archive or records-management system.
Monitor the real configuration
Alert on region changes, newly enabled features, unexpected object-store writes, retention-policy edits and subprocessor updates. Recheck settings after upgrades because a synchronous endpoint and a batch endpoint from the same vendor can have different lifecycles.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common mistakes and how to correct them
“The provider says it deletes files, so everything is gone.”
Correction: ask whether the statement covers outputs, intermediate files, logs, backups and support tickets. Obtain separate deletion times for each class.
“No training” means no human can see the file.
Correction: review support, abuse-monitoring and quality-review access. Training exclusion and personnel access are different controls.
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Correction: verify compute, storage, backups, subprocessors and support geography, including cross-border transfers.
Using a short-lived API token makes the document ephemeral.
Correction: token lifetime controls authentication, not the provider’s storage schedule. Pair credential expiry with a documented retention and deletion policy.
Testing only the happy path
Correction: test failed jobs, retries, timeouts, partial pages, webhook failures and cancellation. These paths often create the copies that ordinary documentation examples omit.
What the published examples establish—and what they do not
Google’s online Document AI statement is evidence that one provider can process a request in memory without persisting document data to disk, while its batch workflow still uses temporary storage and a one-day failsafe. Microsoft’s Content Understanding description shows why output retention can differ from input retention: results may remain for up to 24 hours even after source and intermediate data are deleted. Extend demonstrates that configurable retention and zero-data-retention modes can be tied to deployment and billing choices. None of these examples establishes an industry-wide default.
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For regulated or contractually restricted documents, obtain the current service terms and data-processing agreement for the exact account and endpoint. A compliance badge or a general vendor privacy page cannot, by itself, prove that your selected workflow, region and enabled features satisfy your obligations.
Decision framework
- Choose a memory-oriented synchronous operation when the file is small, an immediate response is acceptable and the provider documents non-persistence for that operation.
- Choose batch processing when throughput or file size requires it, but budget for temporary storage and verify the job and result TTL.
- Choose configurable or zero-retention controls when policy requires customer-defined deletion, and confirm that the mode covers cached and ephemeral artifacts—not just the original upload.
- Keep processing in your controlled environment when contractual, residency or secrecy requirements cannot tolerate a provider-side copy; a hosted API may not be appropriate for that workflow.
The decisive question is not whether an API is “hosted.” It is which data classes your chosen operation creates, where they travel, who can access them and exactly when each copy is deleted.
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