Iteration Layer vs OlmOCR
OlmOCR is a strong open-source OCR model — but English-only, GPU-dependent, and strips headers and footers by design.
Why developers switch from OlmOCR
OlmOCR requires a GPU, only supports English, and intentionally strips headers and footers.
Image description field
When you convert an image file, we return both the OCR-extracted markdown and a natural language description of the image content. OlmOCR returns text extraction only — no semantic understanding of what the image shows.
EU hosting with GDPR compliance
OlmOCR runs on your infrastructure — data residency is your responsibility. We process all documents on EU servers with zero data retention and a Data Processing Agreement available for every customer.
No GPU infrastructure to manage
OlmOCR requires an NVIDIA GPU with 12–20 GB of VRAM for local inference. We are a managed API: one HTTP call, no GPU procurement, no model downloads, no Python environment.
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 | OlmOCR |
|---|---|---|
| Markdown output |
Clean markdown
Returns well-structured markdown with preserved headings, tables, and lists from any document |
Markdown
Outputs markdown with tables as HTML and math as LaTeX, but strips headers and footers by design |
| Image description |
Yes
Returns a natural language description of image content alongside OCR markdown for image files |
No
Text extraction only — no semantic description of visual image content |
| 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 image files (PNG, JPEG) only |
| MCP server |
Yes
MCP server available for integration with AI agents and assistants |
No
No MCP server available |
| Open source |
Proprietary
Closed-source managed SaaS platform |
Apache 2.0
Fully open source including model weights, training data, and code under Apache 2.0 |
| Math and tables |
Standard
Standard table extraction and document structure preservation |
Strong
Strong handling of tables (as HTML) and math equations (as LaTeX) within the markdown output |
| EU hosting |
EU only
All processing happens exclusively on EU-hosted servers |
Your choice
Runs on your infrastructure, so data residency depends on where you deploy |
| Pricing model |
Per page
Simple, predictable per-page pricing |
Free
Open source and free — costs are limited to GPU compute infrastructure |
| Infrastructure required |
None
Fully managed API with no deployment or infrastructure to manage |
GPU required
Requires an NVIDIA GPU with 12–20 GB VRAM, Python 3.11, and poppler-utils |
| GDPR / Data privacy |
Zero retention
No files or results stored beyond temporary 90-day logs |
Your responsibility
Data privacy depends entirely on your deployment and infrastructure choices |
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
Try Iteration Layer with 100 credits
Start with one shared trial pool before choosing subscription or pay-as-you-go billing.
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