Docling vs Marker
Both are alternatives to AWS Textract. Here's how they stack up — verified facts, no spin.
Also searched as Marker vs Docling — same comparison, one verdict.
Docling
TOP PICKIBM's document converter. Layout-aware PDF to clean Markdown, MIT.
Docling parses PDFs, Office documents, images and HTML into a structured representation that preserves reading order, tables, figures and headings, then exports to Markdown or JSON. It uses purpose-trained layout and table models rather than heuristics, which is why it holds up on multi-column academic papers and financial statements where simpler extractors interleave columns into nonsense. It integrates directly with LlamaIndex and Haystack, is MIT licensed, and runs entirely locally including on CPU.
Marker
The best PDF-to-Markdown quality here — check the licence first.
Marker converts PDFs to Markdown with the highest fidelity of anything in this list: it handles equations, code blocks, tables and multi-column layouts, and optionally uses an LLM pass to improve difficult sections. For academic papers and technical documents the output quality is noticeably ahead. The caveat we would rather state than have you find in a licence review: Marker is GPL-3.0 with an additional revenue condition from its maintainer, so organisations above a revenue threshold need a commercial licence. Free for research and for smaller organisations, but not unconditionally.
Side by side
| Docling | Marker | |
|---|---|---|
| Sovereignty Score | 94 | 70 |
| Open source | Yes | No |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | GPL-3.0 with commercial revenue condition |
| Pricing | Free, MIT. Runs on your own hardware, CPU or GPU. | Free below the maintainer's revenue threshold; a commercial licence is required above it. |
Docling is Macrostack's recommended AWS Textract alternative, so it's our pick here.
Docling
Strengths
- +Layout-aware — preserves reading order, tables and structure
- +Outputs clean Markdown/JSON that drops into a RAG pipeline
- +Direct integrations with LlamaIndex and Haystack
- +MIT, fully local, no per-page cost
Trade-offs
- −Slower per page than cloud OCR on very large batches
- −Handwriting support is weak compared with Textract
- −No specialised invoice or receipt models
Marker
Strengths
- +Best PDF-to-Markdown fidelity of the options here
- +Handles equations, code blocks and complex tables well
- +Optional LLM pass for difficult pages
- +Runs entirely locally
Trade-offs
- −Not unconditionally open source — revenue-gated commercial terms
- −GPL-3.0 copyleft affects how you can distribute derived work
- −GPU strongly recommended for reasonable throughput
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.