Data as of Oct 12, 2026A topic in AI Document Parsing & Extraction APIs.
Reviewed by Dimitry Apollonsky
AI summary
Google Cloud Document AI holds a narrow lead over Amazon Textract for parsing documents under strict data residency and zero retention rules. Buyers frequently compare both providers alongside Azure AI Document Intelligence when evaluating compliance controls for regulated records.
recommended for document extraction requiring data residency and strict retention controls
recommended for cloud infrastructure handling secure document storage and processing boundaries
recommended for parsing workflows needing zero retention and regional data storage
recommended for enterprise parsing with configurable data boundaries and compliance guarantees
recommended for specialized document vision tasks within compliant enterprise environments
ChatGPT SearchOct 3, 2026
Yes. Based on current vendor documentation, there are a few credible options, but “zero retention” is much rarer than regional processing + encryption.
| API | Data residency | Encryption / key control | Zero-retention option | Regulated-record fit |
|---|---|---|---|---|
| Extend Parse | Enterprise/private deployment options; verify required geography contractually | TLS/SSL; enterprise security controls | Yes — dataRetention.mode=zero; deletes ephemeral/cache data after processing |
Google AI ModeOct 3, 2026
Which AI document extraction tools offer zero-retention options and data residency encryption to comply with regulations for sensitive records?
Which AI document extraction tools support zero-retention and data residency for regulated data? I also need to ensure encryption is included.
| Strong candidate for healthcare/financial/legal workflows |
| Google Cloud Document AI | Yes — US/EU multi-regions plus several single regions | Encryption at rest; CMEK supported | Near-zero for synchronous processing — documents are processed in memory and not persisted to disk; batch data can remain up to 1 day | Strong; Document AI is listed as HIPAA-compliant and FedRAMP High |
| Amazon Textract | Regional AWS endpoints | TLS + S3/KMS encryption; customer-managed KMS keys | Conditional, not inherently zero-retention. Async results can be stored for 7 days unless configured otherwise; AWS AI opt-out provides stronger deletion behavior | Strong if you're already operating inside AWS, but retention configuration needs careful review |
| Azure AI Document Intelligence | Yes — processing occurs in the resource's region | AES-256; customer-managed keys/Azure Key Vault | No true default ZDR — analyze data/results are retained for 24 hours, although v4 provides an API to delete sooner | Strong, particularly for Azure-centric regulated environments |
| LlamaParse | Regional hosting needs to be verified for your contract/configuration | Vendor/cloud encryption | No — current docs say uploaded source files and parse cache are retained 48 hours by default; ephemeral files can have an expiration | Better for less stringent records unless its enterprise terms satisfy your requirements |
| ParseAI Parse | Public site doesn't establish sufficiently detailed geographic residency controls | Claims encrypted transport | Yes — vendor says documents are processed in memory, not stored or logged | Interesting for ZDR, but I'd require contractual residency/compliance evidence before regulated production use |
For regulated records, I would separate three requirements:
For example, Azure's 24-hour retention isn't equivalent to ZDR, even though you can explicitly delete the analysis response sooner. learn.microsoft.comlearn.microsoft.com Similarly, Google's synchronous Document AI path is unusually privacy-friendly because its documentation says the submitted document is processed in memory and isn't persisted to disk; batch processing has different behavior.
If ZDR is an absolute requirement:
Extend is the clearest mainstream document-parsing API I found with an explicit API-level ZDR control. Its documentation exposes dataRetention.mode: "zero" and says paid customers can request ZDR.
If residency + regulated-cloud controls matter most:
Google Document AI is particularly compelling: selectable US/EU/single-region processing, CMEK, VPC Service Controls, data residency controls, and documented HIPAA/FedRAMP support.
If you're already standardized on AWS:
Textract gives you regional processing and KMS-controlled encryption, but you'll want to configure its retention/AI-service policies explicitly rather than assuming ZDR.
If you're standardized on Microsoft:
Document Intelligence has excellent regional and encryption controls, including CMK, but its documented 24-hour result retention means it doesn't satisfy a literal zero-retention requirement by itself.
One caveat: “HIPAA compliant,” “SOC 2,” or “GDPR compliant” does not by itself mean that your particular regulated-record workflow is compliant. Contractual terms such as a BAA/DPA, permitted region, subprocessors, logging policy, backups, and deletion SLA still matter.
If you tell me the regulation/jurisdiction (e.g. HIPAA/PHI, GDPR, FINRA/SEC, PCI, CJIS, or FedRAMP) and required residency (US-only, EU-only, specific country), I can narrow this to the APIs whose documented controls actually satisfy those constraints.
When processing regulated records (such as healthcare PHI under HIPAA, financial PII, or GDPR-governed data), standard document AI pipelines that cache or train on payloads present a compliance risk. Several document parsing and extraction APIs provide robust data residency guarantees, encryption (at rest and in transit) , and zero data retention (ZDR) or stateless in-memory processing options.
dataRetention.mode: zero ) to ensure data is purged immediately or within tight minimal operational windows.[](https://docs.extend.ai/security/data-handling)us-east-2 and EU via AWS Ireland eu-west-1 ), alongside a containerized deployment mode that runs inside your own VPC.[](https://docs.landing.ai/ade/zdr)When selecting and implementing any of these options for regulated records, ensure you verify:
If you'd like to narrow this down, please let me know: