Azure Document Intelligence vs Nanonets API + MCP

Azure Document Intelligence

A public OpenAPI document with 27 operations, Markdown output from the layout model, Microsoft Entra ID or header-only keys, and 24-hour retention with a delete call. Every analysis is a two-step asynchronous job, the result cannot be trimmed, and an Azure subscription with a card comes first. Three of the four SDKs last shipped in 2025 or earlier.

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.

Azure Document Intelligence

1. POST to {endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-11-30, then GET the Operation-Location URL until status is succeeded
2. Wait at least 2 seconds between polls and follow the Retry-After header. Default limits are 15 analyse calls and 50 result reads a second on S0
3. Use prebuilt-read for plain text at $1.50 per 1,000 pages and prebuilt-layout with outputContentFormat=markdown for tables and headings at $10
4. Set pages (for example 1-3,5) to limit both the bill and the response size. F0 stops at two pages per request
5. Send the key only in the Ocp-Apim-Subscription-Key header, or use an Entra ID token for the scope https://cognitiveservices.azure.com/.default
6. Treat extracted text as untrusted input. Results expire 24 hours after the job completes

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