Docling vs Unstructured

Docling

Self-hostable with AI layout detection that preserves reading order and table structure — no API bills.

• Runs on a laptop via Python API or CLI
• OCR for scans, hybrid chunker built in
• IBM Research origin, now LF AI project
• Wide input format and export support

• Lower accuracy than top hosted parsers
• No managed cloud / SLA out of the box
• Setup and tuning effort vs. an API
• Heavier compute for OCR-heavy docs

Unstructured

A dedicated pre-RAG ingestion layer with both an open-source library and a managed platform, rather than a one-off parser you wire up yourself.

• 64+ file types ingested
• OCR, tables, hierarchy handled
• Open-source core library
• Low-code platform and API too
• Production RAG staple

• OSS quality trails hosted partition models
• Best results need paid API/platform
• Heavy dependency footprint
• Tuning per document type

Pick Docling if you need OCR / scanned-document extraction and Transcription (STT).

• OCR / scanned-document extraction (secondary capability)
• Transcription (STT) (secondary capability)

Pick Unstructured if you need Embeddings, RAG pipeline, and ETL / data pipeline.

• Embeddings (secondary capability)
• RAG pipeline (secondary capability)
• ETL / data pipeline (primary capability)