Docling vs LlamaIndex

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

	Docling RAG	LlamaIndex RAG
Tagline	Open-source document parsing for AI: PDFs, Office files, audio and video into clean, structured Markdown/JSON	Data framework for connecting LLMs to your data.
Category	RAG	RAG
Pricing	Free· Free and open source under MIT License. Hosted by the LF AI & Data Foundation; no paid tiers.	Freemium· Free open-source; LlamaCloud paid
Model	GraniteDocling 258M and other vision-language models	BYO (Claude / GPT / open)
Editorial score	—	8.7 / 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	RAGdata ingestionindexing
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	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion
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	API surface is large Documentation can be hard to navigate
Website	docling-project.github.io	www.llamaindex.ai

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 LlamaIndex if

✅ Focused on retrieval (not general agent stuff)
✅ Many ingestion connectors
✅ Strong production patterns
✅ LlamaCloud for managed ingestion

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