Introduction

Reducto is the complete agentic document platform for AI teams that need performance at enterprise scale. Azure Document Intelligence in Foundry Tools (formerly Form Recognizer) is a cloud OCR/IDP service. The right choice depends on whether your roadmap is page-extraction-into-Azure-systems, or agentic document work powering production AI.

This comparison focuses on how Reducto and Azure AI Document Intelligence perform on diverse, messy documents at scale, and when each is the right fit for enterprise AI pipelines.

Harvey, Scale AI, and Vanta use Reducto to power production AI on document-heavy workflows.

Headline comparison (at a glance)

Category	Reducto	Azure AI Document Intelligence
Core approach	Complete agentic document platform — vision-first parsing with vision-language models (VLMs) and multi-pass Agentic OCR self-correction; layout preserved for LLM-ready outputs.	Read, layout, prebuilt, and custom models to extract text, key-value pairs, tables, and structure as a general-purpose OCR/document service.
Complex tables & multi-column layouts	Reducto reports state-of-the-art performance on RD-TableBench, its open complex-table benchmark (1,000 PhD-labeled tables), and is purpose-built for irregular, scanned, merged-cell tables and other long-tail edge cases.	Parses tables and selection marks via layout and prebuilt models; optimized for common document layouts, with performance on very irregular tables depending on document quality and configuration.
Prebuilt forms coverage	Focus on template-free extraction across document types; schema-based extraction for domain fields (no template setup).	Broad catalog of prebuilt models (e.g., US tax forms such as W-2/1098/1099/1040, health insurance cards, ID, contracts, and mortgage forms).
Custom extraction	JSON Schema-based extraction for LLM-ready JSON; retrieval-oriented chunking with page/bounding-box metadata to support citations.	Custom template and custom neural extraction models, plus Query Fields add-on features to pull bespoke fields on top of layout/prebuilt/custom models.
Traceability for RAG/QA	Layout-aware chunks with block- and cell-level bounding boxes and citation metadata designed for precise source tracing.	Bounding regions (with page-level polygons) and spans returned in the REST API and SDKs for text, tables, key-value pairs, entities, and more.
Edit/fill forms	"Edit" endpoint: automatically detect fields and fill PDFs/DOCX (including text fields, checkboxes, radios, dropdowns) without manual templates.	No equivalent end-to-end document editing/fill API within the Document Intelligence service itself (separate Microsoft offerings cover document authoring/editing).
Deployment	Cloud SaaS, customer VPC, on-premises, and fully air-gapped enterprise deployments.	Cloud service, connected containers (on-prem/edge with metering back to Azure), and disconnected containers (offline usage with specific licensing and volume commitments).
Data handling	Zero Data Retention options; on Growth and Enterprise tiers, API data auto-expires within 24 hours and customer data isn't used for model training.	HIPAA-eligible when used under a Microsoft BAA; standard Azure logging and data-handling controls, plus usage reporting for disconnected containers.
Compliance	SOC 2 Type II; HIPAA-compliant processing with BAAs and zero-retention options for Growth/Enterprise.	HIPAA BAA available via Microsoft Product Terms; covered by the broader Azure compliance portfolio.
Scale & SLAs	99.9%+ uptime targets; enterprise rate limits of 100+ calls/sec and over 4 billion pages processed in production.	Page-metered pricing with commitment tiers for high volumes (including connected/disconnected container commitments); batch analysis APIs and container options for large-scale processing.

Notes and sources for this table: Azure DI features/models, containers (connected/disconnected), and prebuilt coverage; DI bounding regions; HIPAA/BAA eligibility; DI pricing/commitment tiers. Reducto accuracy, deployment, ZDR, and SLAs from Reducto's docs and case studies.

Where the agentic document platform fills the gap

Azure DI is a strong fit for teams deep in the Azure stack handling standard forms. Reducto complements Azure where the long tail breaks DI — complex tables, handwriting, charts, mixed-language scans — and displaces it for teams building agent-ready pipelines from scratch.

• Accuracy on complex structure: Reducto's open RD-TableBench benchmark (1,000 complex tables labeled by PhD-level annotators) reports state-of-the-art similarity scores for Reducto and higher complex-table accuracy than several cloud APIs, including Azure Document Intelligence, across merged cells, dense text, handwriting, and multilingual content. These are exactly the conditions where downstream RAG/QA is most sensitive. Azure DI remains competitive on more standard layouts, especially when documents align with its prebuilt schemas. Vendor benchmarks (including ours) are inherently biased. The honest test: run Reducto and Azure DI head-to-head on a sample of your own documents. There is also one fully independent data point: on LongExtractBench — independently audited, validated, and published by micro1 — Reducto ranked first of seven systems with 99.6% precision, 99.6% recall, and zero failures across 225 long documents. micro1 didn't measure Azure DI, so treat that as verification of Reducto's accuracy claims rather than a head-to-head.

• Vision-first with Agentic OCR: Reducto's hybrid, multi-pass "Agentic OCR" framework uses VLMs to review and correct OCR and layout errors, targeting near-human reliability on hard files and reducing manual exception handling in production pipelines.

• LLM-ready structure by design: Parse and Extract outputs include layout-aware chunks, table structures, and bounding boxes, plus optional field-level citations, so RAG pipelines can ground answers in precise page locations. Reducto's own RAG benchmarks show structure-preserving parsing materially improves retrieval accuracy and answer correctness versus text-only/OCR-only baselines.

• Document diversity without templates: Reducto emphasizes template-free extraction for forms and tables—handling rotated scans, handwriting, merged cells, and evolving layouts without per-template setup or maintenance, which is valuable when you face a wide variety of unstandardized documents.

Where Reducto wins on your hardest documents

Azure DI holds up on simpler layouts and pure digital text, and is procurement-easy when you are already inside the Azure estate. The pattern teams hit in production is narrower: the long tail where DI's primitives go quiet. Across the dimensions buyers actually evaluate, Azure DI shows consistent gaps that compound on real workloads.

• Figures and charts: Cloud OCR services across the board struggle to extract granular data points from charts, plots, and visually rich figures. Reducto's vision-first pipeline treats figures as first-class structure, not pixels to skip.

• Checkboxes: Azure DI returns selection marks but accuracy drifts on dense or visually inconsistent forms. Reducto handles checkbox states with layout context across template-free documents.

• Handwriting: Handwriting is the weakest area for the major cloud providers, and the gap is most visible on real-world scanned forms. Reducto applies frontier vision models selectively for handwritten content and verifies output through the multi-pass Agentic OCR loop.

• Spatial citations: DI returns bounding regions, but not the fine-grained, sub-page citation regions that regulated AI workflows depend on for traceability. Reducto preserves block- and cell-level citation metadata by design.

• Ergonomics: Teams consistently report that working directly with hyperscaler document APIs is harder than it should be — mode selection, packaging, and discoverability create friction. Reducto is built as a developer product first.

Reducto wins where Azure Document Intelligence doesn't reach — figures and charts with verifiable extraction, checkbox handling that doesn't drift, handwriting on real-world docs, spatial citations for traceability, and a product the team actually likes operating. Azure DI keeps its edge on Azure-native procurement, deterministic primitives on simple layouts, and average-latency throughput when documents map cleanly to prebuilt schemas. The honest read: Reducto wins on hard documents, richer extraction, better checkbox/handwriting handling, and a fuller end-to-end product.

Beyond parsing: the complete toolkit

Reducto's platform spans five APIs — Parse, Extract, Classify, Split, Edit — plus the Reducto Studio platform built on top of them, covering 30+ filetypes end-to-end from raw ingestion to agent-ready outputs. That breadth is what separates an agentic document platform from a single-purpose OCR/IDP service: one platform across the document lifecycle, not a parsing call plus a series of downstream stitched services.

Where Azure DI is strongest

• Broad prebuilt catalog: Azure Document Intelligence offers many prebuilt models (for example, US tax forms including W-2/1098/1099/1040, health insurance cards, IDs, contracts, and mortgage forms such as URLA and closing disclosures) that map well to common financial and operational workflows and can accelerate time-to-value when inputs match Microsoft's schemas.

• Enterprise coverage and options: DI supports cloud APIs, connected containers for on-prem/edge scenarios with pay-as-you-go metering, and disconnected containers for fully offline environments under annual commitment tiers. For estates standardized on Azure, this can simplify procurement, security review, and governance.

• Traceability primitives: The service returns bounding regions/polygons and spans for words, tables, key-value pairs, selection marks, entities, and more, enabling page-level evidence, checkbox states, and table geometry to be surfaced in downstream systems.

Deployment and data control

• Reducto: Offers cloud SaaS, VPC, fully on-prem, and air-gapped deployments, with Zero Data Retention options (including immediate deletion via retention=0) and default 24-hour auto-expiry for Growth and Enterprise tiers. Reducto is SOC 2 Type II-audited, supports HIPAA-aligned processing, and signs BAAs and DPAs—fitting organizations that must keep content inside their perimeter, enforce short retention, or self-host the full stack.

• Azure DI: Provides connected containers that must call back to Azure for metering, plus a disconnected-container program for approved customers who need fully offline processing with annual page commitments. Containers themselves do not carry independent compliance certifications; HIPAA BAA coverage is provided at the Azure service level through the Microsoft Product Terms and Data Protection Addendum.

Scale, pricing posture, and SLAs

• Reducto: Documents 99.9%+ uptime and tiered concurrency limits (1/10/100+ QPS) across Standard, Growth, and Enterprise plans, with over 4 billion pages processed in production. Enterprise customers can negotiate custom SLAs and deploy in VPC or on-prem/air-gapped environments without sacrificing throughput. Reducto Standard includes $150 in free usage; r-1 Parse costs $10 per 1,000 pages ($0.01/page) and includes text, layout, tables, and OCR. Batch Parse and Extract jobs receive a 20% usage discount with a 12-hour completion guarantee. Reducto isn't the cheapest per-page option — it's optimized for the accuracy-latency-throughput balance production AI demands.

• Azure DI: Bills primarily per page for Read/Layout/Prebuilt/Custom, with commitment tiers for high-volume usage in both cloud and container modes, plus additional commitment tiers for disconnected containers (up to tens of millions of pages per year). Batch analysis APIs support large jobs backed by Azure Storage. For teams already consolidating spend on Azure, these commitment tiers may be attractive.

Fit-by-use-case guidance

• Choose Reducto when: Your corpus is diverse and "messy" (scans, handwriting, multi-column PDFs, irregular or merged-cell tables); you need LLM-ready chunks with page/bounding-box citations; you require VPC, on-prem, or fully air-gapped deployments; or you want schema-based extraction without templates so you can iterate quickly across many document types.

• Choose Azure DI when: Your inputs align well to Microsoft's prebuilt schemas and you want a general-purpose service tightly integrated into the Azure estate, including containerized options for edge/on-prem, standard Azure compliance coverage, and commitment-tier purchasing constructs.

Bottom line

Both platforms can reliably extract text, tables, and fields from clean, well-structured inputs. The main differentiators are performance on the long tail of real-world documents and the operational model at scale. If your priority is handling a high diversity of complex documents at production throughput with precise, auditable structure for LLMs, Reducto's agentic document platform is typically the right choice; if your workloads map cleanly to prebuilt schemas and you prefer Azure-native procurement and operations, Azure Document Intelligence is a solid fit.