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Atlas by Comet Lab
Capability layer

Document extraction, compared

Before a model can use a PDF, a scan or an office file, something has to turn it into clean, structured text. Parsing quality often decides answer quality, so this choice matters more than it looks. The first question is whether documents may leave your environment.

Learn the idea Multimodal AI: reading images and documents on Horizon

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At a glance

Every tool in this layer, with where it fits and the facts that usually decide it.

Tool Where it fits Licence Self-hostable Hosted API Maturity
Unstructured Ingestion pipeline The right tool when source content spans many file formats and you need one consistent ingestion path into a RAG pipeline. Open source Yes Yes Established
Docling Document parser A strong fit when you want high-quality, fully local document parsing with no data leaving your environment. Open source Yes No Growing
LlamaParse Hosted parsing API Best when parsing accuracy on genuinely difficult PDFs matters more than keeping the parsing step in-house. Proprietary No Yes Growing
Marker Document converter A strong fit when you need fast, accurate document-to-Markdown conversion and can run it on your own GPU. Open source Yes Yes Growing
MinerU Document parser A fit when source PDFs are technical or scientific and formulas and tables must survive the conversion. Open source Yes No Growing
PaddleOCR OCR toolkit A fit when the core problem is OCR (extracting text from scans and images) across multiple languages. Open source Yes No Mature
Reducto Hosted parsing API A fit when document parsing accuracy is business-critical and a paid, hosted, high-accuracy API is justified. Proprietary No Yes Growing
Surya OCR toolkit A fit when you want modern, multilingual OCR with strong layout and reading-order detection. Open source Yes No Growing

How to choose

Start from your situation. Where a line says “our”, it comes from Comet Lab’s own builds.

  1. Many file formats, one ingestion path

    One consistent route into a RAG pipeline. Our default first stage for a document-heavy project.

  2. Documents cannot leave the building

    Docling parses fully locally, with good table handling; Marker converts to Markdown fast on your own GPU; MinerU keeps formulas and tables intact in technical PDFs.

  3. Accuracy on difficult PDFs comes first, and documents may leave

    Hosted APIs tuned for complex, messy or regulated documents. Strict data residency rules them out.

  4. The problem is scans and images

    OCR across many languages; Surya adds layout and reading order. Each is one stage, not a full pipeline.