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Prebuilt models are pretrained extractors for common document types like invoices, receipts, and tax forms — no training required. Custom models are trained on your own documents (5+ samples) for domain-specific formats like internal purchase orders or industry-specific forms. The layout API is a general-purpose endpoint that returns text, tables, and structural elements for any document without semantic field extraction.
Yes. Microsoft offers Docker containers for Document Intelligence that can run in your own datacenter, on edge devices, or in fully disconnected environments. Disconnected containers are billed via Azure commitment plans and are commonly used by healthcare, defense, and financial customers with strict data residency or sovereignty requirements.
Document Intelligence uses pay-as-you-go pricing based on pages processed, with different rates per model type (read, layout, prebuilt, custom). The free tier (F0) includes 500 pages per month at no cost, intended for evaluation and small projects. Production workloads use the S0 standard tier with volume discounts available through Azure Enterprise Agreements.
Microsoft reports accuracy above 95% on prebuilt models for high-quality documents, and custom neural models often exceed that on domain-specific data with adequate training samples. Accuracy is influenced by scan quality, DPI, document skew, handwriting legibility, and language. Image preprocessing and providing diverse training samples for custom models materially improve results.
Document Intelligence is commonly used as the ingestion layer for RAG pipelines: the layout API extracts text, tables, and structure from PDFs, which is then chunked and embedded into Azure AI Search or another vector store. Azure OpenAI models query that index to answer questions, summarize contracts, or generate reports — Microsoft provides reference architectures and starter templates for this pattern.
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Last verified March 2026