Iteration Layer vs Azure Document Intelligence
Azure Document Intelligence uses pre-built models and custom training where a schema is enough.
Why developers switch from Azure Document Intelligence
Azure requires training custom models before you can extract data from new document types.
Schema-defined extraction, no training
Azure requires choosing a pre-built model (invoice, receipt, ID) or training a custom one on labelled documents. We skip the model entirely: define the fields you want, send your document, get typed JSON back.
17 typed field types, not raw text
Pre-built Azure models return field values as strings. We return typed results — dates as ISO dates, currencies as amount-plus-symbol objects, IBANs validated, addresses structured. No post-processing needed.
No Azure subscription required
Azure Document Intelligence requires an Azure subscription, resource groups, and managed identity configuration. Iteration Layer gives you a direct HTTP API with shared credits, EU processing, n8n, and MCP.
Feature-by-feature comparison
We went through the docs so you don't have to. Here's how every feature compares — including the ones where we're not the better choice.
| Feature | Iteration Layer | Azure Document Intelligence |
|---|---|---|
| Schema-defined extraction |
Yes
Define the fields you want in a schema and receive typed JSON results immediately |
Model-dependent
Requires choosing a pre-built model or training a custom one on labelled documents |
| Typed field support |
17 types
Choose from 17 typed schema fields including date, IBAN, currency, address, phone, email, and URL |
Model-dependent
Available field types vary by which pre-built model is selected |
| Confidence scores |
Per field
Confidence score between 0 and 1 for every extracted schema field |
Per field
Confidence scores provided per extracted field value |
| Source citations |
Yes
Verbatim source citation from the document for every extracted field |
No
No source citation linking extracted values back to document text |
| Multi-file support |
Up to 20 files
Process up to 20 files in a single API request with merged extraction results |
1 file
Each API request processes a single file |
| Supported input formats |
40+ formats
Process 40+ formats — PDF, Office, EPUB, RTF, LaTeX, email, Jupyter, images, and more — in a single API endpoint |
PDF, images, Office
Supports PDF, images, and Office document formats |
| Training required |
No
Schema-based extraction works immediately without any training step |
For custom types
Custom document types require uploading labelled training documents and training a model |
| Azure ecosystem required |
No
Direct HTTP API with one API key, shared credits, EU processing, n8n, MCP, and no cloud vendor dependency |
Yes
Requires an Azure subscription, resource groups, and managed identity configuration |
| Pre-built models |
Any document
Schema-based approach works with any document type without pre-built models |
5+ models
Pre-built models for invoices, receipts, identity documents, W-2, and 1099 forms |
| Azure integration |
Standalone
Independent API with no cloud ecosystem dependency |
Deep
Integrates with Cognitive Services, Logic Apps, and the broader Azure ecosystem |
| Layout analysis |
Standard
Standard document layout analysis for common formats |
Advanced
Strong multi-column and complex layout analysis capabilities |
| EU hosting |
EU only
All processing happens exclusively on EU-hosted servers |
EU available
EU regions are available but the service is not restricted to EU-only processing |
| Pricing |
$0.033
1 credit per page |
Per page
Model-specific pricing |
| Infrastructure required |
None
Fully managed API with no deployment or infrastructure to manage |
None
Fully managed Azure service with no infrastructure to manage |
| GDPR / Data privacy |
Zero retention
No files or results stored beyond temporary 90-day logs |
Azure data policies
Subject to Azure data processing terms with configurable retention policies |
| Data used for training |
Never
Your data is never used to train or improve AI models — guaranteed for all plans |
No
Microsoft does not use customer data submitted to Azure AI services for model training |
Pricing
Start usage-based. Switch to a subscription when your volume becomes predictable.
Usage-based
Graduated pricing. Your effective rate decreases automatically as monthly usage grows.
- No monthly commitment
- Pay only for credits used
- Automatic volume discounts as usage grows
Predictable volume
Fixed recurring credit packs with lower effective credit prices for steady usage.
- Lower effective per-credit prices
- Fixed recurring credit packs
- Predictable monthly budget
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