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Record · doclingInfrastructureMIT code; hardware, model and optional service costsRecord checked Oct 28, 2026

Docling

Convert complex documents into structured data with local or configured processing pipelines.

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Documentation reviewed

Can I use this for my work?

Checked · Recheck due

Docling parses document layout and exports structured document data. Local conversion can run offline after initial model setup; remote-model configurations and managed offerings are separate routes.

Availability
Available. Released package/CLI. Docling Serve, remote-model pipelines and IBM's managed offering need separate configuration and terms checks.
How to use it
Library / CLI; API. Python 3.10+ and compatible model/runtime dependencies; API serving is a separate deployment step.
Processing and data
On-device option; Self-hosted option; Local and cloud options. A local pipeline can process files without uploads after model downloads. Selecting remote enrichment or model providers changes the data destination.
Costs and limits
MIT code; hardware, model and optional service costs
Outputs
Structured document objects and exports such as Markdown/JSON, tables and provenance; extraction quality must be checked before downstream decisions.

Requires a supported Python environment and downloaded models. Validate tables, reading order, OCR and source locations on your files. The code license does not determine every model's terms.

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Publisher documentation was reviewed. ASE has not independently tested this workflow, its outputs, billing, privacy controls or accessibility.

Best for

Developers building document extraction, structured-data or retrieval pipelines over their own files.

Avoid if

You need a ready-to-use hosted study chatbot or cannot validate extraction errors in your documents.

About Docling

Docling is a document-conversion package and CLI that parses layout, tables, reading order and other document structure for downstream use. A local pipeline can run offline after its models are downloaded. It requires a supported Python environment; remote-model pipelines, API serving and managed services are distinct choices. Check extracted values and source locations on representative files rather than assuming an accurate-looking result is correct.

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