Iteration Layer vs DocuPipe
DocuPipe offers zero-shot extraction with custom schemas — but lacks typed fields, source citations, and EU hosting.
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Why developers switch from DocuPipe
DocuPipe does zero-shot extraction, but returns generic string values — not typed, validated fields.
17 typed fields vs. generic strings
DocuPipe accepts a schema and extracts matching data, but all values come back as strings. We return 17 purpose-built typed fields — dates as ISO dates, currencies as amount-plus-symbol objects, IBANs validated, addresses structured — with no post-processing.
Source citations on every field
DocuPipe highlights source regions visually in their review UI, but the API does not return verbatim text citations for each field. We return a source citation for every extracted value, so you can audit results programmatically.
EU hosting with GDPR compliance
DocuPipe is US-based with no EU cloud hosting option — only on-premises deployment on Enterprise plans. We process all documents on EU servers with zero data retention and a Data Processing Agreement available for every customer.
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 | DocuPipe |
|---|---|---|
| Schema-defined extraction |
Yes
Define extraction fields via a purpose-built schema with 17 typed field types |
Yes
Zero-shot extraction with custom schema definitions — no training required |
| Typed field support |
17 types
Choose from 17 typed schema fields including date, IBAN, currency, address, phone, email, and URL |
Generic strings
All extracted values returned as strings requiring manual type parsing and validation |
| Confidence scores |
Per field
Confidence score between 0 and 1 for every extracted schema field |
Per field
Confidence metrics provided per extracted field value |
| Source citations |
Yes
Verbatim source citation from the document for every extracted field |
Visual only
Source highlighting available in the review UI but no verbatim text citations in the API response |
| 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
Supports PDF and scanned document images |
| MCP server |
Yes
MCP server available for integration with AI agents and assistants |
No
No MCP server available for AI agent integration |
| Document classification |
No
Schema-based extraction without built-in document classification |
Built-in
Built-in document classification to categorize documents into predefined classes |
| Document splitting |
No
Each file processed as a single document |
Built-in
AI-based content analysis to split multi-document files into individual documents |
| Human review UI |
Build your own
API-only — confidence scores enable building custom review workflows |
Built-in
Built-in visual review interface with source highlighting for human-in-the-loop workflows |
| EU hosting |
EU only
All processing happens exclusively on EU-hosted servers |
US only
US-based cloud hosting only — on-premises deployment available on Enterprise plans |
| Language support |
Any language
REST API callable from any programming language with HTTP support |
Any language
REST API accessible from any programming language |
| Pricing model |
Per page
Simple, predictable per-page pricing |
Credits per page
3 credits per page with monthly plans ranging from free to $499/month |
| Infrastructure required |
None
Fully managed API with no deployment or infrastructure to manage |
None
Fully managed cloud API with no infrastructure to manage |
| GDPR / Data privacy |
Zero retention
No files or results stored beyond temporary 90-day logs |
US-based processing
Documents processed on US infrastructure — on-premises option available for Enterprise |
| Data used for training |
Never
Your data is never used to train or improve AI models — guaranteed for all plans |
Not documented
No public policy on whether customer data is used for model training |
Pricing
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Developer
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Business
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Still evaluating?
See how we compare — and where the competition still wins. Choosing the right tool shouldn't require a week of research.
Reducto
Reducto uses JSON Schema for field definitions — verbose compared to simple typed field declarations.
Nanonets
Nanonets uses open-source OCR models with generic schema definitions — not purpose-built typed fields.
LlamaParse
LlamaParse outputs markdown — your code still needs to parse it into typed fields.
Mistral OCR
Mistral has best-in-class OCR, but returns raw text — not structured data with typed fields.
AWS Textract
Textract needs five API calls per document and returns raw strings — not typed, structured data.
Azure Document Intelligence
Azure requires training custom models before you can extract data from new document types.
Google Document AI
Document AI is powerful, but requires a GCP project, service account, and storage bucket to get started.
Kreuzberg
Kreuzberg is fast and open source, but you own the deployment, scaling, and monitoring.
Regex & Templates
Regex templates break the moment a document layout changes — even slightly.
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