Azure Document Intelligence is Microsoft's cloud-based document processing platform, formerly known as Form Recognizer (renamed in 2023), and part of Azure Cognitive Services. It provides pretrained models for standard document types, query fields for naming ad-hoc fields at analysis time, Document Intelligence Studio for training custom models on labeled data, and NLP capabilities for semantic analysis of document content. If your organization is already deep in Microsoft's stack, it is the path of least resistance.
Key differences at a glance:
- anyformat extracts nested schemas zero-shot; Azure's no-labeling path (query fields) returns flat values, and structure means a trained custom model.
- anyformat includes a visual workflow builder with branching, routing, and human review; Azure provides an extraction API with no native orchestration.
- anyformat is EU-native with GDPR as an architectural constraint; Azure offers configurable regions under US jurisdiction.
- anyformat offers full on-premise deployment including air-gapped environments; Azure's container deployment covers only a subset of features.
- Both return per-field confidence; anyformat's is calibrated and drives review routing, and every value carries a visual citation.
That path is not always the best fit. European data sovereignty, workflow orchestration, and structured extraction without a training cycle are where Azure's approach introduces friction that compounds over time.
Customization and extraction approach
Azure Document Intelligence ships strong pretrained models for standard document types: invoices, receipts, IDs, tax forms. These work without training and extract predefined fields.
Beyond them, Azure gives you two paths. Query fields let you name the fields you want at analysis time and return them with a confidence score, with no labeling and no training cycle, but they are a paid add-on and return flat values. Anything with structure — nested objects, repeated line items — means building a custom model in Document Intelligence Studio: label documents (5 minimum, realistically more) and retrain whenever the schema changes.
anyformat uses zero-shot extraction against a JSON schema you define, nested objects and line-item arrays included. No labeling at either level. Change the schema in Studio and the change applies to the next document, without a training run or a code deploy.
The distinction is not "training versus no training". It is where the ceiling of the no-training path sits.
Workflow builder and orchestration
Azure Document Intelligence extracts data from documents. Everything around it — classification, routing, validation workflows, human review, conditional logic, webhook infrastructure — must be built by your engineering team using Azure Functions, Event Grid, Logic Apps, or custom code.
anyformat includes a visual workflow builder with built-in operators for splitting, routing, extraction, validation, and human-in-the-loop review. Non-technical teams design document pipelines visually, with branching and conditions, without engineering involvement.
This is the difference between buying a document extraction API and buying a document operations platform. The workflow layer is where business logic lives, and Azure leaves it entirely to you.
European sovereignty and data residency
Azure Document Intelligence runs on Microsoft Azure. Data residency is configurable within Azure's region options, and Microsoft offers Virtual Networks, Private Endpoints, and activity logging. These are meaningful controls. The jurisdictional foundation, however, remains US-based.
For European enterprises navigating GDPR, DORA, ViDA, and country-specific e-invoicing mandates, the question is not only where data is stored but under whose legal framework it is governed.
anyformat is EU-native. Our infrastructure runs on AWS with data residency controls purpose-built for European regulatory requirements, and GDPR compliance is an architectural constraint rather than a configuration option. Region selection on a US-governed platform does not change the jurisdiction.
ISO 27001 and compliance
Azure is ISO 27001 certified, alongside SOC 2, HIPAA and FedRAMP High. Certification is parity here, not a gap. What those certificates cover is the Azure platform; the compliance posture of the pipeline you build on top — encryption settings, access controls, retention policies, audit logging — sits on the customer side of the shared responsibility model.
anyformat is ISO 27001 certified with certification scope covering the document processing pipeline end to end, so the controls you are buying and the controls that were audited are the same set.
Zero data retention
Azure's data handling follows the platform's broader retention and storage policies, and Microsoft states that customer data is not used to train Document Intelligence models. Configuring it is the work: storage lifecycle rules, logging retention, and deletion policies across Document Intelligence Studio, the Azure portal and Blob Storage.
anyformat offers zero-retention processing as a native option. Source documents are not persisted beyond the processing window. One toggle, not a configuration exercise spread across several services.
Parse and extract capabilities
Azure's pretrained models handle complex layouts well. Independent benchmarks show it outperforming Textract on multi-column tables and nested structures, with handwriting recognition and selection marks. At 96% accuracy on printed text benchmarks, it leads its category.
Where it gets more involved is structured extraction outside pretrained coverage, which carries the labeling and retraining cycle described above, and long documents, which require manual pagination management. Configuration is spread across Document Intelligence Studio, the Azure portal, and API code.
anyformat supports 100+ formats and adapts to any layout without templates. Our pipeline combines LLMs with deterministic rules and confidence scoring to handle edge cases, long documents are chunked automatically with context preservation, and configuration lives in one place.
On-premise deployment
Azure offers limited container deployment options for Document Intelligence, but the full feature set is cloud-only. Air-gapped environments and strict data perimeter requirements leave you with few options.
anyformat offers full on-premise deployment: private cloud or on-prem, including air-gapped environments. For defense, healthcare, financial services, and government organizations, this is a hard requirement.
Accuracy, confidence and evaluation
Azure's pretrained models are genuinely strong on their target document types, and Azure returns a confidence score per extracted field, not only per model.
Benchmark accuracy and production accuracy are different things, and the deciding property of a confidence score is calibration: whether 0.9 actually means a 90% chance of being correct. anyformat calibrates confidence per field, measured at 99.1% calibration accuracy with an Adaptive ECE of 0.009 (anyformat benchmark, 2026), which is what makes a straight-through threshold safe to set. Every value also carries a visual citation linking it to its exact position on the page. In production, L'Oréal reports 99% extraction accuracy and a 60% reduction in processing time across 1,500+ monthly invoices.
Keeping that accuracy after go-live is the other half. Every anyformat Extract and Classify workflow has a Health tab where you build a dataset with verified ground truth, run an evaluation that re-extracts every in-scope document against a chosen workflow version, and score it field by field. Runs are numbered and immutable, and Health Overview places the dataset benchmark next to live production accuracy and reports the gap (Evals).
Long tables and complex layouts
Azure handles multi-column tables and nested structures better than most cloud providers. Genuine strength.
Tables that span multiple pages, complex merge-cell patterns, and tables embedded in mixed-layout documents still create challenges, and manual pagination management for long documents adds friction.
anyformat's multi-stage pipeline is engineered for table complexity. On tables growing to 50 pages and roughly 2,400 rows it held ~99% row recovery (anyformat benchmark, 2026), and output is structured and ready for downstream consumption without post-processing.
Azure's Layout model detects figures and returns their bounding regions, associated captions, and cropped images of them. What it does not return is what the figure shows. anyformat detects figures, classifies them in context, and generates structured descriptions of the visual content.
If you are looking for an alternative to Azure Document Intelligence, anyformat addresses what surfaces once your schemas move beyond flat fields: structured, nested extraction with no labeling and no retraining cycle. It also provides EU-native data sovereignty, full on-premise deployment including air-gapped environments, calibrated confidence with visual citations, and a visual workflow builder that lets ops teams own document pipelines without engineering dependencies.
When to choose Azure Document Intelligence
If your documents already match Azure's pretrained models, your extra fields are flat enough for query fields, and your team lives in the Microsoft ecosystem, Azure will work.
Choose anyformat when you need structured extraction on custom documents in days, EU-native sovereignty, workflow orchestration with human review, or calibrated confidence you can set a straight-through threshold against — without labeling a training set or writing glue code.
anyformat is the agentic document intelligence platform built for European enterprises. ISO 27001 certified, GDPR-compliant, with zero-retention processing and on-premise deployment. Get started at anyformat.ai
