Docling vs Pathway

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Docling RAG	Pathway RAG
Tagline	Open-source document parsing for AI: PDFs, Office files, audio and video into clean, structured Markdown/JSON	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Free· Free and open source under MIT License. Hosted by the LF AI & Data Foundation; no paid tiers.	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	GraniteDocling 258M and other vision-language models	Multi-model
Editorial score	—	7.3 / 10
Use cases	RAG document ingestionPDF table extractionScientific paper parsingFinancial filing analysis (XBRL, JATS)Enterprise knowledge base preparationDoc-QA chatbotsAgent-based document workflows via MCPOCR for scanned archivesAudio and video transcription for multimodal RAGConverting Office documents to Markdown for LLMs	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
Pros	Broad format coverage: PDF, Office, HTML, EPUB, images, LaTeX, email, plus audio and video Genuinely strong PDF parsing: layout, reading order, tables, formulas, and code blocks preserved Runs fully local, so sensitive or air-gapped document processing stays on your own infrastructure Native integrations with LangChain, LlamaIndex, CrewAI, and Haystack shorten the path to a working RAG pipeline Multiple delivery modes: Python library, CLI, HTTP API server, and MCP server for agents Purpose-built small VLMs (GraniteDocling 258M) keep GPU/CPU costs modest compared to calling frontier models per page Permissive MIT license and Linux Foundation governance make it safe for commercial adoption	Genuinely live indexing - documents update without rebuild jobs Self-hosted under BSL 1.1, no data leaves your infra Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake) Same pipeline handles batch and streaming 20+ production-ready templates including multimodal and adaptive RAG
Cons	Python-only public library; non-Python stacks must go through the HTTP or MCP server High-fidelity parsing of complex PDFs benefits from a GPU, which raises the bar for self-hosting No hosted SaaS or managed service: teams must run and maintain their own deployment Documentation and examples assume engineering fluency; there is no non-technical UI Extraction quality on unusual layouts (multi-column scans, handwriting) still varies and may need post-processing	Steeper learning curve than prompt-chain frameworks BSL is not OSI-approved - commercial restrictions apply at scale Smaller community than LangChain/LlamaIndex Pricing for Scale/Enterprise tiers not transparent
Website	docling-project.github.io	pathway.com

Pick Docling if

✅ Broad format coverage: PDF, Office, HTML, EPUB, images, LaTeX, email, plus audio and video
✅ Genuinely strong PDF parsing: layout, reading order, tables, formulas, and code blocks preserved
✅ Runs fully local, so sensitive or air-gapped document processing stays on your own infrastructure
✅ Native integrations with LangChain, LlamaIndex, CrewAI, and Haystack shorten the path to a working RAG pipeline

Pick Pathway if

✅ Genuinely live indexing - documents update without rebuild jobs
✅ Self-hosted under BSL 1.1, no data leaves your infra
✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
✅ Same pipeline handles batch and streaming

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