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Reducto vs Azure Document Intelligence
Reducto is the agentic document platform: zero-shot accuracy on documents it has never seen, with no models to train or maintain. Azure Document Intelligence reads documents with prebuilt and custom-trained models.
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- Scale AI
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- Vanta
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Migration credits
Apply for migration creditsIf you're using another document processor, we'll give you up to $5,000 in credits to migrate your workload to Reducto.
At a glance
How Reducto and Azure Document Intelligence compare
Reducto wins on zero-shot accuracy, platform breadth, and deployment flexibility. Azure Document Intelligence wins on Azure ecosystem fit and prebuilt models for common forms.
| 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: 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/BAA and 24-hour API data expiry on Growth+; VPC to air-gapped. | Yes: Azure cloud and containers for supported models; approved disconnected deployments. |
| Platform breadth | Yes: Parse, Extract, Split, Classify, Edit; MCP server, CLI, SDKs, Studio. | Partial: OCR, extraction, classification, and splitting; no document editing API. |
| Pricing | From $0.01/page pay-as-you-go; $150 in free credits. | Per-model page rates and optional add-ons; 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 your document types. That works when your documents are uniform. As document types change, teams may need additional training examples and routing logic. Reducto Extract works from a schema without labeled training examples: 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
- Parse r-1 reads text, tables, figures, and layout together in one pass, with source grounding. In preview, r-1 is faster than Reducto's previous agentic pipeline, with 20% lower error. Legacy parsing and custom agentic processing remain available. Reducto reaches up to 99–100% zero-shot accuracy on complex real-world documents. In an independent benchmark commissioned by Reducto and 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. Azure Document Intelligence was not tested in LongExtractionBench. 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 OCR and structured document analysis. 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 optional per-field citations linking extracted values to their source positions, 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, structured extraction, classification, and splitting; document editing and wider workflow orchestration require additional services and 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 offers a managed Azure service plus containers for supported models, including approved disconnected deployments. Azure remains a procurement advantage for teams already using its compliance framework. Reducto is SOC 2 Type II, with HIPAA/BAA on Growth and Enterprise and documented data-retention policies, 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 5B+ pages.
- Total cost, not sticker price
- Azure Document Intelligence prices by model and optional add-ons. A pipeline that makes multiple billable model calls needs to account for each call, alongside the engineering time to train models and post-process output. Reducto includes $150 in free credits. Parse r-1 is $0.01/page, Extract is $0.02/page, and Deep Extract is $0.04/page, with parsing included in both extraction prices. You can budget by page count instead of estimating variable credit usage, with no separate parsing bill for extraction. See processing rates.
- 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 documented data-retention controls.
- 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 with less post-processing code to maintain.
Azure Document Intelligence may be a fit if…
- Your organization is standardized on Azure and an existing Microsoft enterprise agreement simplifies procurement.
- 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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Read the comparisonFurther reading
Research before you choose
- GuideEvaluating Azure Document Intelligence for PDF Parsing
See how Azure Document Intelligence handles the parsing cases that drive downstream AI quality.
- BenchmarkComplex Document Extraction Benchmark
See how Deep Extract performed on micro1’s independent benchmark of complex document extraction.
- GuideIDP Enterprise Evaluation
Use a practical framework to evaluate document processing platforms for enterprise workloads.
Document work starts here
See the difference
Sign up with $150 in free credits, run your hardest documents through both tools, and compare the output side by side.
