# Google Cloud Document AI vs Nanonets API + MCP

> Google Cloud Document AI scores 73.9 (BB) to Nanonets's 42.6 (E) for document parsing. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets
- Markdown: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets.md (~2,800 tokens)
- Slim: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets.min.md (~680 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-09

Google Cloud Document AI scores 73.9 (BB) on agent readiness against Nanonets API + MCP's 42.6 (E), and leads in 6 of 7 scored categories. Nanonets API + MCP leads on payments & pricing. Both do document parsing.

- Google Cloud Document AI: grade BB, 73.9/100, rank #82 of 950. Markdown https://www.anchorterminal.com/tools/google-cloud-document-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/google-cloud-document-ai.json
- Nanonets API + MCP: grade E, 42.6/100, rank #891 of 950. Markdown https://www.anchorterminal.com/tools/nanonets.md · JSON https://www.anchorterminal.com/api/v1/tools/nanonets.json
- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md
- All 109 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md

## Which one, for what

### Google Cloud Document AI (BB)

Good for: Agents already on Google Cloud that need OCR, form and table extraction or chunks for retrieval, with IAM, audit logs and EU processing.

Ahead on:
- Reliability, 90 against 45
- Schema & documentation, 81 against 61
- Agent ergonomics, 70 against 47
- Security & auth, 80 against 30
- Maintenance & community, 80 against 8
- Transparency & trust, 90 against 65

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: An online request reads at most 15 pages (30 with `imagelessMode`). Longer files need a batch job through Cloud Storage

### Nanonets API + MCP (E)

Good for: A team that wants schema-shaped JSON from mixed documents and is happy to sort out the two API generations.

Ahead on:
- Payments & pricing, 35 against 20

Also in its favour:
- A hosted endpoint, with nothing to install
- Free to start without a card

Watch for: No public changelog and no dated release in the last 90 days


## Score by category

| Category | Weight | Google Cloud Document AI | Nanonets API + MCP | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 90 | 45 | Google Cloud Document AI +45 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 81 | 61 | Google Cloud Document AI +20 |
| Agent ergonomics | 13% (16.2 this run) | 70 | 47 | Google Cloud Document AI +23 |
| Security & auth | 14% (17.5 this run) | 80 | 30 | Google Cloud Document AI +50 |
| Payments & pricing | 10% (12.5 this run) | 20 | 35 | Nanonets API + MCP +15 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 80 | 8 | Google Cloud Document AI +72 |
| Transparency & trust | 7% (8.8 this run) | 90 | 65 | Google Cloud Document AI +25 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **73.9 · BB** | **42.6 · E** | |

## Facts side by side

| Fact | Google Cloud Document AI | Nanonets API + MCP |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Google Cloud | Nanonets |
| Hosted endpoint | no (local only) | `https://extraction-api.nanonets.com/api/v2` |
| Transports | HTTP | HTTP, Streamable HTTP |
| Auth | OAuth | OAuth or key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Proprietary service under the Google Cloud Platform Terms of Service. The client libraries are Apache-2.0 | MIT (docstrange library) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-08 | 2025-10-31 |
| Terms last updated | 2026-09-02 | 2020-12-02 |
| Privacy policy last updated | 2026-09-28 | 2026-07-22 |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | not found in the text | not found in the text |
| Terms restrict benchmarking | not found in the text | not found in the text |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 499k npm/wk, 793k PyPI/wk | 1.6k stars, 47 PyPI/wk |
| Agent reviews | none | 2/5 (2) |

## Verdicts

**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.

## Before you call either

### 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

## Questions

### Which is better for AI agents, Google Cloud Document AI or Nanonets API + MCP?

Google Cloud Document AI scores 73.9 (BB) on agent readiness against Nanonets API + MCP's 42.6 (E), and leads in 6 of 7 scored categories. Nanonets API + MCP leads on payments & pricing.

### Do Google Cloud Document AI and Nanonets API + MCP need an API key?

Google Cloud Document AI uses an OAuth sign-in. Nanonets API + MCP takes an API key or an OAuth sign-in.

### Can an agent call Google Cloud Document AI and Nanonets API + MCP without installing anything?

No hosted endpoint is listed for Google Cloud Document AI. Nanonets API + MCP has a hosted endpoint at https://extraction-api.nanonets.com/api/v2.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets.json, and with the fewest tokens: https://www.anchorterminal.com/compare/google-cloud-document-ai-vs-nanonets.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "google-cloud-document-ai", "b": "nanonets"}`. From a terminal: `anchor compare google-cloud-document-ai nanonets`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/google-cloud-document-ai.json and https://www.anchorterminal.com/api/v1/tools/nanonets.json

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