Iteration Layer vs LlamaParse
LlamaParse turns documents into markdown. If you need structured data, you still have to parse the markdown.
Why developers switch from LlamaParse
LlamaParse outputs markdown — your code still needs to parse it into typed fields.
Structured data, not markdown
LlamaParse outputs markdown text — useful for feeding into an LLM context window, but not for extracting an invoice total, a date, or an IBAN. We return typed JSON matching the schema you define, ready to use without further parsing.
Confidence scores on every field
When a value is ambiguous or missing, LlamaParse produces text that looks plausible. We return a confidence score between 0 and 1 for every extracted field, so you know which values to trust and which to route to human review.
EU hosting with GDPR compliance
LlamaParse is US-based. 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 | LlamaParse |
|---|---|---|
| Schema-defined extraction |
Yes
Define extraction schemas with 17 purpose-built typed fields for structured output |
No
Prompt-based parsing with unstructured markdown output |
| Typed field support |
17 types
Choose from 17 typed schema fields including date, IBAN, currency, address, phone, email, and URL |
None
No typed field extraction — output is unstructured markdown text |
| Confidence scores |
Per field
Confidence score between 0 and 1 for every extracted schema field |
No
No confidence scoring — ambiguous values appear as plausible-looking text |
| 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 various document formats for markdown conversion |
| Primary use case |
Data extraction
Designed for structured data extraction to power business automation workflows |
RAG parsing
Optimized for document parsing to feed into RAG and LLM pipelines |
| RAG pipeline fit |
JSON output
Returns structured JSON which can be used in RAG pipelines but requires conversion |
Native fit
Markdown output is ideal for direct use as LLM context in RAG pipelines |
| Complex layouts |
Standard
Standard document layout analysis for common formats |
Advanced
Strong handling of multi-column and mixed content layouts |
| LLM integrations |
MCP server
MCP server for integration with AI agents and assistants |
Native
Tight integration with LlamaIndex and LangChain ecosystems |
| EU hosting |
EU only
All processing happens exclusively on EU-hosted servers |
US-based
Processing infrastructure is located in the United States |
| Pricing |
$0.033
1 credit per page |
Per page
Free tier available |
| 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 are processed through LlamaCloud's managed API |
| Data used for training |
Never
Your data is never used to train or improve AI models — guaranteed for all plans |
No
LlamaCloud states that customer data uploaded via the API is not used for 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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