Azure AI Document Intelligence and AWS Textract are both cloud OCR and data-extraction services you call by API. Azure adds custom model training and an on-premises container; Textract adds natural-language Queries and an Analyze Lending workflow for mortgages. Both still leave you to classify, review, validate, and export in code. DocuOCR is a ready-to-use alternative to both that does that workflow for you.
Built for US teams choosing between the two big cloud OCR services: see where each one fits, what both make you build, and how a finished product compares. Last updated June 2026.
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Two of these are cloud OCR services a developer assembles into a workflow. The third is the workflow, finished. Here is the honest version of each.
Microsoft's cloud OCR and document-extraction service, formerly Azure Form Recognizer. It offers Read and Layout OCR, prebuilt models for invoices, receipts, IDs, and W-2s, and custom extraction and classification models you train in Document Intelligence Studio. You reach it through a REST API or SDK, and it can also run on-premises in a container.
Amazon's cloud OCR and data-extraction API. It has Detect Document Text for OCR, Analyze Document for forms, tables, Queries, and signatures, plus the specialized Analyze Expense, Analyze ID, and Analyze Lending APIs. It uses generalized models, integrates natively with S3, Lambda, and IAM, and returns text and data with bounding boxes you map in code.
A ready-to-use intelligent document processing product, not a raw cloud API. It classifies a mixed batch, reads any layout, extracts the fields you define, validates them, routes low-confidence reads to a built-in review screen, and exports clean data through a dashboard and one REST API, with no AWS or Azure account, IAM, or pipeline to build.
All three read documents. The difference is how much you build around the engine before you have usable, validated data. Sourced from the AWS and Microsoft documentation, June 2026.
| Factor | Azure Document Intelligence | AWS Textract | DocuOCR |
|---|---|---|---|
| Type of tool | Cloud OCR service, API and SDK | Cloud OCR service, API | Ready-to-use product, plus REST API |
| Who it is for | Azure-native developer teams | AWS-native developer teams | Business teams and developers |
| Getting started | Azure subscription, SDK, or Studio | AWS account, IAM, and a pipeline you build | Sign in and process a document |
| Custom model training | Yes, train on your own forms in Studio | No, generalized models only | Define a schema, no model to train |
| Classify a mixed batch | Build your own routing | Build your own routing | Built in, sorts the file for you |
| Human review of low-confidence reads | Build your own screen | Build your own screen | Included review screen |
| Natural-language queries | No, label-based fields | Yes, the Queries feature | Define the fields you want by name |
| Specialized models | Invoice, receipt, ID, W-2, health, custom | Expense, ID, Lending for mortgages | Any document, by the schema you set |
| On-premises option | Yes, runs in a container | No, AWS cloud only | Ask us about deployment |
| What you get back | Text, fields, tables, coordinates to map in code | Text, key-value pairs, tables, bounding boxes to map in code | Named fields mapped to your schema |
| Validation and export | Code it yourself | Code it yourself | Configurable rules and export built in |
| Free to test | 500 pages per month, ongoing | 1,000 text pages per month for 3 months | Free on your own files, no signup |
| Pricing model | Per page by model, commitment tiers, varies by region | Per page by API and feature, tiered by volume, varies by region | Per page, the pipeline included, no seats or setup fees |
Pricing for both cloud services changes by region and volume, so confirm exact rates on the current AWS Textract and Azure Document Intelligence pricing pages before you commit. If you want the recognition engine alone, either is a solid choice. If you want a working process today, DocuOCR is built on intelligent document processing that classifies, reads, extracts, validates, and exports, so your team reviews data instead of assembling it.
Each cloud service has real advantages. The point of a comparison is to match those to your stack and documents, not to crown a winner.
Trade-off: you still build the classification, review, validation, and export layers, and a custom model is a small training project of its own.
Trade-off: there is no custom model training, it is cloud-only inside AWS, and the workflow around the API is yours to build and run.
Whichever cloud API you pick, recognition is the first 20 percent. These are the pieces a finished product includes that a raw API does not.
Both return data for one document at a time. Sorting a stack of different document types and routing each to the right extraction is code you write yourself.
Neither ships a screen where a person corrects a low-confidence value before it lands in your system. You build the review interface and the queue.
Checking that a total adds up, a date is valid, or an ID matches a pattern happens in your application logic, not in the OCR call.
Both hand back text, key-value pairs, and coordinates. Turning that into the named fields your system expects is mapping code you maintain.
Getting clean data into a spreadsheet, database, or downstream system is an integration you write and host on top of the API.
You run the pipeline: the storage, the retries, the monitoring, the IAM or subscription, and the maintenance as volumes and formats change.
DocuOCR includes all six. It classifies the file, reads any layout, extracts the fields you define, validates them, routes uncertain reads to a built-in review screen, and exports clean data, so you adopt a workflow instead of building one around a recognition API.
Classify, read, extract, validate. Drop a file in and the whole sequence runs on its own, with no AWS or Azure pipeline behind it.
The engine reads a mixed batch and sorts it by document type, so the right extraction runs on each one without anyone separating the stack first.
OCR and ICR convert PDFs, photos, faxes, and scans into machine-readable text, including handwriting and stamps that a raw OCR call can miss.
DocuOCR pulls the values tied to their labels and returns the fields you defined, so you get structured data instead of text and bounding boxes to parse.
Values run through your rules, low-confidence reads route to review, and clean data exports to a spreadsheet or your systems by API, with an audit trail.
# invoice.pdf -> extracted data (not bounding boxes) { "doc_type": "invoice", "vendor": "Lakeside Supply Co", "invoice_number":"INV-20418", "invoice_date": "2026-05-22", "total": "4820.00", "confidence": 0.98 } # classified, read, validated, ready for export
A short decision guide based on your stack, your documents, and whether you want an API or a finished product.
You run on Azure, your layouts vary enough to need a custom-trained model, you process multilingual invoices, or you need on-premises processing in a container.
You build on AWS, your documents are clean structured forms and tables, you want natural-language Queries, or you process mortgage packages with Analyze Lending.
You want finished, validated data instead of an API to build around, business users plus developers both need access, and you would rather test on your own files than wire up a cloud project.
With Textract or Azure you call recognition, then build classification, field mapping, validation, and storage around it on a cloud account. With DocuOCR you post a document to a single endpoint and get back the classified type, the recognized text, and the extracted fields, with a confidence score on every value, ready to use.
# classify + extract in one request curl https://api.docuocr.com/v1/extract \ -H "Authorization: Bearer $KEY" \ -F "file=@scanned_document.pdf" \ -F "classify=true" # -> doc type + named fields + confidence
The questions teams ask most when they compare the two cloud OCR services and a ready-to-use alternative.
Azure AI Document Intelligence and AWS Textract are both cloud services that read documents and return text, key-value pairs, and tables through an API. The main difference is training and ecosystem: Azure lets you train models on your own forms and can run on-premises in a container, while Textract uses generalized models, adds natural-language Queries, and integrates tightly with AWS.
Neither is universally better; it depends on your stack and your documents. Pick Azure Document Intelligence if you run on Azure, process variable invoice layouts, or need custom-trained models. Pick AWS Textract if you build on AWS or process mortgage packages. If you want a finished workflow instead of an API, a ready-to-use product like DocuOCR fits better than either.
Yes. Azure AI Document Intelligence is the current name for the service Microsoft previously called Azure Form Recognizer. It is the same family of capabilities: Read and Layout OCR, prebuilt models for invoices, receipts, IDs, and W-2s, and custom models you train in Document Intelligence Studio. Older documentation and SDKs may still use the Form Recognizer name.
No. AWS Textract uses Amazon's generalized, pre-trained models and does not let you fine-tune or train a model on your own document layouts. Azure Document Intelligence does support custom extraction and classification models trained on a small set of your sample forms. If your layouts are unusual or vary by vendor, that training difference often decides the choice.
It depends on volume, region, and which features you turn on; both price per page, and the cost can flip depending on whether you need forms, tables, queries, or custom models. Compare the current AWS and Azure pricing pages for your exact mix. Azure also gives an ongoing 500-page monthly free tier, while Textract's free tier is limited to the first three months.
Azure Document Intelligence can run on-premises or at the edge through a Docker container, which helps when data cannot leave your network. AWS Textract is a cloud-only service that runs inside AWS regions. If on-premises processing is a hard requirement, that rules Textract out and points you toward Azure or a deployment-flexible product instead.
Yes, in most cases. Both are developer services you reach through a REST API or SDK, and turning their raw output into a working process means writing code for classification, review, validation, and export. A business user can click through Azure's Studio to test a sample, but production use on either platform is an engineering project.
A good alternative to both is a ready-to-use intelligent document processing product that includes the workflow the cloud APIs leave you to build. DocuOCR classifies a mixed batch, extracts the fields you define, validates them, sends low-confidence reads to review, and exports clean data through a dashboard and one REST API, with no AWS or Azure account to manage.
Accuracy depends on your document types, not a single winner. Independent invoice tests have shown Azure edging ahead on irregular or older layouts thanks to custom training, while Textract is strong on clean structured forms and tables. The reliable answer is to run both, and a ready-to-use option, on your own documents and measure the result.
Replace the Azure API with a ready-to-use product that classifies, reads, validates, and exports out of the box.
Skip the AWS pipeline and get finished data from a product that includes classification, review, and export.
The same comparison for Google Cloud Document AI, the third major cloud OCR service developers assemble.
The head-to-head between the AWS and Google Cloud OCR services, with custom extractors and a Layout Parser for LLMs.
The Microsoft and Google pairing, comparing the on-premises container, prebuilt tax models, and a Layout Parser for LLMs.
The single REST call that replaces a Textract or Azure pipeline, returning classified type, text, and named fields.
An honest roundup of the leading intelligent document processing tools and the buyer each one fits.
The full platform behind the comparison, with a dashboard for teams who want document data without code.
Run the same file you planned to test on Azure or Textract through DocuOCR, watch it classify, read, and return named fields, then connect the API to process every document that follows on its own.
