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Reducto vs Azure Document Intelligence
Azure Document Intelligence reads documents with prebuilt and custom-trained models. Reducto is the agentic document platform: zero-shot accuracy on documents it has never seen, with no models to train or maintain.
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At a glance
How Reducto and Azure Document Intelligence compare
Azure Document Intelligence wins on Azure ecosystem fit and prebuilt models for common forms. Reducto wins on zero-shot accuracy, platform breadth, and deployment flexibility.
| Dimension | Reducto | Azure Document Intelligence |
|---|---|---|
| Category | Full platform: parse, extract, split, classify, and edit in one API. | Cloud OCR service with prebuilt models and custom model training. |
| Parsing accuracy | Yes: Up to 99–100% zero-shot accuracy on complex documents. | Partial: Reliable on standard layouts; degrades on multi-column and irregular structure. |
| Extraction paradigm | Yes: Zero-shot schema extraction with per-field citations; no model training. | Yes: Prebuilt models for invoices, receipts, IDs; custom training for other formats. |
| Table extraction | Yes: 0.90 on RD-TableBench; merged cells, multi-level headers, borderless tables. | Partial: Solid on standard tables; degrades on merged cells and multi-level headers. |
| Handwriting & checkboxes | Yes: Handwriting and checkbox extraction in the standard parse pipeline. | Partial: Cursive handwriting unreliable; checkbox accuracy inconsistent across form styles. |
| Deployment & compliance | Yes: SOC 2 Type II, HIPAA, zero data retention; VPC to air-gapped. | Yes: Azure-only; SOC 2, HIPAA, FedRAMP within Azure compliance framework. |
| Platform breadth | Yes: Parse, Extract, Split, Classify, Edit; MCP server, CLI, SDKs, Studio. | Partial: OCR and extraction only; classification and editing need other Azure services. |
| Pricing | From $0.015/page pay-as-you-go; 15,000 free credits. | ~$0.03/page layout analysis, per page per model; 500 free pages/month. |
Parse one of your hardest documents in Studio and compare the output side by side.
The comparison in depth
Where the differences actually show up
- Zero-shot vs trained models
- Azure Document Intelligence's paradigm is model selection: pick a prebuilt model for common document types, or train and maintain a custom model for each fixed format. That works when your documents are uniform. It breaks down when they aren't, because every new vendor template or layout variation means retraining and rerouting. Reducto orchestrates 12+ models (computer vision, OCR, and VLMs) to handle documents zero-shot: define what you want extracted and run it on any document, no training set required. As document variety grows, one approach compounds maintenance while the other doesn't.
- Accuracy on complex documents, measured
- Reducto's multi-pass pipeline reaches up to 99–100% zero-shot accuracy on complex real-world documents and scores 0.90 on RD-TableBench for table extraction. In an independent benchmark conducted by micro1 on 225 real, human-validated documents, Reducto Deep Extract achieved 100% coverage, 99.6% precision, 99.6% recall, and 99.3% leaf accuracy with zero failed documents. Azure Document Intelligence is reliable on standard layouts, but accuracy degrades on multi-column pages, overlapping regions, dense tables, handwriting, and checkboxes: the exact places where errors are hardest to catch downstream. Benchmarks are a starting point, not a verdict: run both on your own documents.
- Output that's ready for LLM pipelines
- Azure Document Intelligence returns raw OCR output. Element-level bounding boxes are in the API response, but they aren't surfaced as first-class extraction citations, and getting the output into shape for a RAG or agent pipeline typically means post-processing code you write and maintain. Reducto is built for that destination: structured JSON with reading order, block types, and table structure, and every extracted value linked to its bounding-box position in the source, accessible via API and reviewable in Studio. Reducto uses frontier models rather than trying to replace them, so the output is designed to feed them well.
- One platform vs OCR plus Azure glue
- Azure Document Intelligence handles OCR and structured extraction; classification, splitting, document editing, and workflow orchestration mean wiring up additional Azure services and custom integration. Reducto ships the complete toolkit (Parse, Extract, Split, Classify, and Edit) in a single API across 30+ file types and 100+ languages, plus an MCP server, CLI, and SDKs so agents and engineers drive the same tools. Notably, Reducto's Edit API writes data back into PDF forms and DOCX; Azure Document Intelligence is read-only.
- Deployment beyond one cloud
- Azure Document Intelligence deploys only on Azure, a genuine procurement advantage if that's where you already are, with SOC 2, HIPAA, and FedRAMP inherited from the Azure compliance framework. Reducto is SOC 2 Type II and HIPAA compliant (BAA available) with zero data retention, and deploys hosted, in your VPC on AWS, GCP, or Azure, on-prem, or fully air-gapped. Teams at Harvey, Scale AI, and Vanta run Reducto in production, and the platform has processed 4B+ pages.
- Total cost, not sticker price
- Azure Document Intelligence's layout analysis runs roughly $0.03/page, and pricing is per page per model, so a pipeline that routes documents through layout analysis plus prebuilt or custom models pays more than once per document, before counting the engineering time to train models and post-process output. Reducto starts at $0.015/page pay-as-you-go with 15,000 free credits, and one price covers the entire toolkit.
- Migrating from Azure Document Intelligence
- Most teams migrate by replacing the analyze call and the custom models it routes to with a single Reducto parse or extract call. Reducto returns structured JSON with reading order, block types, and table structure, and the docs cover Python, Node.js, and Go SDKs. Because extraction is zero-shot, the custom-model training sets and per-format routing logic usually get deleted rather than ported, and teams often run both side by side during the cutover.
Which fits your team
Who should pick which
Different tools fit different stages. Here's the honest split.
Choose Reducto if…
- Your documents vary in format or layout, and training and maintaining a custom model per document type doesn't scale.
- Accuracy on complex content (dense tables, handwriting, checkboxes, charts, multi-column scans) gates your downstream LLM output.
- You need deployment flexibility: VPC on any cloud, on-prem, or air-gapped, with SOC 2 Type II, HIPAA, and zero data retention.
- Your workflow extends beyond OCR into classification, splitting, extraction with per-field citations, or writing data back into documents.
- You're feeding LLM or agent pipelines and want structured, citation-linked output instead of raw OCR plus post-processing code.
Azure Document Intelligence may be a fit if…
- Your organization is standardized on Azure and an existing Microsoft enterprise agreement makes procurement effectively free.
- Your documents are uniform, common types (invoices, receipts, IDs) that prebuilt models already cover well.
- You have fixed document formats that rarely change, where training a custom model once is a reasonable one-time cost.
- You need FedRAMP or other certifications that Azure's compliance framework provides out of the box.
FAQ
Common questions
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See the difference
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