Iteration Layer vs PDF.ai
PDF.ai offers PDF parse and extract APIs; Iteration Layer supports broader document formats and typed extraction workflows.
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Why developers switch from PDF.ai
PDF.ai is focused on PDF workflows. Iteration Layer handles extraction across PDFs, Office files, emails, images, and more.
More than PDF input
Use one extraction API for PDFs, Office documents, EPUB, RTF, LaTeX, emails, images, and other content formats.
Typed business fields
Return typed values for common business fields rather than only schema-shaped JSON.
Multi-file extraction
Process related documents together when a case, invoice, or client packet spans multiple files.
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 | PDF.ai |
|---|---|---|
| Schema-defined extraction |
Yes
Define typed schema fields for extraction |
Yes
Extract endpoint accepts a JSON schema for PDF data extraction |
| Confidence scores |
Per field
Confidence score between 0 and 1 for every extracted field |
Parse-level
Parse output can include confidence values on content segments |
| Source citations |
Yes
Verbatim source citation from the document for every field |
Yes
Extract endpoint returns citations linking fields to source segments |
| Multi-file support |
Up to 20 files
Process related files together in one request |
PDF focused
API endpoints operate on a PDF file, URL, or cached document ID |
| Typed field support |
17 types
Choose from 17 typed schema fields including date, IBAN, currency, address, phone, email, and URL |
JSON schema
Extraction follows a JSON schema, with type handling defined by that schema |
| Supported input formats |
40+ formats
Process 40+ formats — PDF, Office, EPUB, RTF, LaTeX, email, Jupyter, images, and more — in a single API endpoint |
PDF
Extract API is focused on PDF files, PDF URLs, and cached PDF document IDs |
| Document splitting |
No
Each file is processed as a single document in extraction requests |
Built-in
Split endpoint can divide large PDFs into smaller documents |
| Ask / chat endpoint |
No
Extraction API returns structured data rather than chat answers |
Built-in
Ask endpoint supports querying PDFs directly |
| Caching |
No
Requests are processed independently with zero file retention |
docId cache
Parsed documents can be cached and reused by docId to reduce future credits |
| Automation integrations |
API, n8n, MCP
Official n8n node, REST API, webhook-friendly responses, and MCP server for agent workflows |
REST API
REST API and playgrounds are available for PDF parse and extraction workflows |
| EU hosting |
EU only
All processing happens exclusively on EU-hosted servers |
No
No public EU-only processing option documented in the API docs |
| Pricing model |
Per page
Simple, predictable per-page pricing |
Credits per page
Parse and extraction credits vary by quality level and schema size |
| Infrastructure required |
None
Fully managed API with no deployment or infrastructure to manage |
None
Fully managed API with no infrastructure to manage |
| GDPR / Data privacy |
Zero retention
No files or results stored beyond temporary 90-day logs |
Cached documents
docId caching can retain parsed results for reuse unless avoided |
| Data used for training |
Never
Your data is never used to train or improve AI models — guaranteed for all plans |
Unclear
Public API docs describe caching and docId reuse but do not surface a no-training guarantee |
Pricing
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Developer
For individuals & small projects
Startup
Save 40%For growing teams
Business
Save 47%For high-volume workloads
Or pay as you go from $0.022/credit with automatic volume discounts.
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