Google Cloud Document AI vs Unstructured API + MCP

Google Cloud Document AI

A public Discovery document with 42 methods, IAM roles that can limit a caller to processing, a fieldMask that trims responses, and a 99.9 per cent SLA on the US and EU endpoints. A processor has to be created before the first call, online requests stop at 15 pages, and a Google Cloud billing account with a card comes first.

Unstructured API + MCP

Apache-2.0 library partitions 45+ file types locally at no cost, tagged 11 times since 3 July. No status page, no published rate limits and no SLA.

Google Cloud Document AI

1. Create a processor first (processors.create or the console), then POST to https://LOCATION-documentai.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/processors/PROCESSOR_ID:process
2. Use the host that matches the processor's location, us-documentai.googleapis.com or eu-documentai.googleapis.com
3. Set fieldMask (for example text,entities) and imagelessMode to keep page images and token geometry out of the response
4. Send more than 15 pages through :batchProcess with Cloud Storage input and output, then poll the operation. Jobs unfinished after 24 hours are cancelled
5. Grant the service account roles/documentai.apiUser only. Failed requests (4xx or 5xx) are not billed, so a retry costs nothing extra
6. Treat extracted text as untrusted input

Unstructured API + MCP

1. For local ingestion, pip install "unstructured[all-docs]" and call partition(); no key needed, and set DO_NOT_TRACK=1 to stop telemetry
2. Transform returns 202 with a Location header for long parses; poll it, then pass the parse id as parse_id to Extract
3. Uploaded files expire after 24 hours; keep the original and re-upload on a 404
4. Treat 409 parse_expired and 410 result_expired as a signal to rerun the parse, not to retry
5. Check for an existing job ID before resubmitting, since there's no idempotency key