Open Source (Apache 2.0)
$0
Forever
- Full toolkit + Granite-Docling-258M weights
- Self-hostable on any infrastructure
- DocTags universal markup output
- All conversion features (PDF, DOCX, PPTX, etc.)
Docling — RAG Frameworks Platform
Founded 2024|Zurich, Switzerland (IBM Research)|Part of IBM Research people|Free open-source (Apache 2.0)
What is Docling?
Docling is IBM Research's open-source document conversion toolkit, designed for AI-driven workflows that need clean, structured data from messy documents. It converts PDFs, DOCX, PPTX, HTML, images, and more into JSON or markdown while preserving layout, tables, equations, code blocks, and lists.
In 2026, IBM released Granite-Docling-258M — an ultra-compact open-source vision-language model purpose-built for document conversion under Apache 2.0. Granite-Docling delivers significantly better recognition accuracy than traditional OCR by retaining the original layout structure and identifying complex elements like tables, math, and code blocks. The output uses DocTags, a universal markup format developed by IBM Research that captures every page element and its contextual relationships.
Strategically, IBM has positioned Docling for production use: launched the Docling OpenShift Operator with Red Hat (targeting banks), donated the project to the Linux Foundation's Agentic AI Foundation alongside BeeAI and Data Prep Kit, and is integrating it across Red Hat and IBM Cloud document workflows. Free, fully open-source, and self-hostable.
Key features
Core capabilities this platform advertises.
- Converts PDFs, DOCX, PPTX, HTML, images to structured JSON/markdown
- Granite-Docling-258M VLM model purpose-built for document understanding
- DocTags markup preserves layout, tables, equations, code blocks
- Apache 2.0 — fully open-source and self-hostable
- Production deployment via Docling OpenShift Operator (Red Hat)
Strengths and tradeoffs
What this tool does well, and the limitations to keep in mind.
Pros
- Purpose-built VLM beats general-purpose OCR on complex layouts
- Apache 2.0 license — fully open and self-hostable
- IBM-grade engineering with Linux Foundation governance
- DocTags standardized markup makes output portable across tools
- Production deployment story via Red Hat / OpenShift
Cons
- Setup complexity higher than hosted document APIs
- Granite-Docling-258M still requires GPU for fast inference
- Less polished UX than cloud DocAI services from Google/AWS
- Smaller ecosystem than Unstructured.io for non-IBM stacks
Plans & pricing
What's included in each plan, and how the tiers compare.
Red Hat OpenShift Operator
Bundled with OpenShift
- Production deployment via OpenShift
- Targeted at banking and regulated industries
- Red Hat enterprise support
Common use cases
RAG and AI engineering teams that need accurate, structured ingest of PDFs, DOCX, and complex documents into LLM pipelines
- RAG ingest pipelines that need clean structured text
- Financial and legal document parsing (banking)
- Scientific paper ingestion preserving equations and tables
- Enterprise knowledge base ingestion at scale
- On-prem document conversion for regulated environments
Best Docling alternatives & competitors
Top companies in RAG Frameworks you can use instead of Docling.
RAGFlow
Deep document understanding — tables, images, multi-language
Unstructured
Ingests 65+ file formats: PDFs, DOCX, PPTX, HTML, images, emails
LlamaIndex
Data framework for LLM applications
Haystack
Modular RAG framework
Reducto
Vision parsing
Pathway
Rust-powered streaming engine — millions of data points/sec
Carbon (Perplexity)
Data connectors
Vectara
R2R
RAG engine
Chunkr
Captain
Scalable knowledge search
WhyHow
Compresr
Context compression
Compare Docling
Side-by-side comparisons with other tools in this category.
Best integrations for Docling
Companies from adjacent layers in the AI stack that work well with Docling.
Last verified: April 29, 2026