Google Cloud Document AI vs Nanonets 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.

Nanonets API + MCP

Extraction API returns Markdown, CSV or JSON, with named fields or a JSON schema. No public changelog and no dated release in the last 90 days.

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

Nanonets API + MCP

1. Use the extraction API at extraction-api.nanonets.com, not app.nanonets.com, unless you already have a trained model
2. Pass a JSON schema in json_options when downstream code needs fixed field names
3. Use the async extract endpoints for long documents and poll /api/v1/extract/results/{record_id}
4. On a 429, wait 30 seconds and double the delay each retry
5. Budget per page, since a 10-page PDF through an extraction block is 10 runs