Azure AI Document Intelligence and Google Cloud Document AI are both cloud OCR and data-extraction services you call by API. Azure adds prebuilt tax and ID models, model composition, and a container you can run on-premises; Google adds a Layout Parser for LLM pipelines and Custom Extractors trained in Workbench. 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 cloud document services: see where each one fits, what both make you build, and how a finished product compares. Last updated June 2026.
Upload a document to extract
Drop files here or click to upload
Up to 50 files
Free plan extracts the first 5, rest can be unlocked after
Uploading...
Drop in the document you were going to test on Azure or Document AI and watch DocuOCR classify it, read it, and return named fields, free, no signup required.
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 document service, formerly Form Recognizer. It has Read for OCR, Layout for structure, prebuilt models for invoices, receipts, IDs, W-2s and other tax forms, and custom template and neural extractors you train and compose in Document Intelligence Studio. Its standout is a container you can run on-premises or disconnected, and hosting in any Azure region.
Google's cloud document platform built around processors. It offers Enterprise Document OCR, Form Parser, and Layout Parser, prebuilt processors for invoices, receipts, W-2s, and IDs, and a Custom Extractor you train on your own samples in Document AI Workbench. You reach it through a REST API or client library on Google Cloud, hosted from US or EU regions, and wire the output into your own pipeline.
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 Azure or Google Cloud account, service account, or pipeline to build.
All three read documents. The difference is deployment, custom training, and how much you build around the engine before you have usable, validated data. Sourced from the Azure and Google Cloud documentation and pricing pages, June 2026.
| Factor | Azure Document Intelligence | Google Document AI | DocuOCR |
|---|---|---|---|
| Type of tool | Cloud OCR service, API and Studio | Cloud document platform, API and processors | Ready-to-use product, plus REST API |
| Who it is for | Azure-native developer teams | Google Cloud-native developer teams | Business teams and developers |
| Getting started | Azure account, resource, and a pipeline you build | GCP project, service account, and processors you wire up | Sign in and process a document |
| Custom model training | Yes, template and neural models in Studio | Yes, train a Custom Extractor in Workbench | Define a schema, no model to train |
| On-premises option | Yes, run the container disconnected or in your network | No, Google Cloud only | Ask us about deployment |
| Region flexibility | Any Azure region | US or EU regions only | US data handling |
| Classify a mixed batch | Custom classification model to train | Splitter and classifier processors to configure | Built in, sorts the file for you |
| Human review of low-confidence reads | Build your own screen | Human-in-the-loop add-on to configure | Included review screen |
| Specialized models | Invoice, receipt, ID, W-2, tax, health card, plus custom | Invoice, receipt, W-2, ID, plus custom | Any document, by the schema you set |
| LLM and RAG support | Layout and Markdown output to map yourself | Layout Parser chunks documents for an LLM | Returns clean fields ready to use |
| What you get back | Text, key-value pairs, tables, fields, coordinates to map in code | Text, entities, tables, coordinates 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 free tier | New-customer Google Cloud credits, no ongoing OCR free tier | Free on your own files, no signup |
| Pricing model | Per page by model, tiered by volume, varies by region | Per page by processor, plus hosting for deployed custom processors | 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 Azure Document Intelligence and Google Document AI pricing pages before you commit. As of June 2026 plain OCR is around $1.50 per 1,000 pages on both, while structured extraction costs more (Azure prebuilt and Layout about $10 per 1,000 pages and custom extraction about $30, Google Form Parser about $30 per 1,000 plus per-hour hosting for deployed custom processors). 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: prebuilt and custom extraction cost more per page than plain OCR, and the classification, review, validation, and export around the API are yours to build and run.
Trade-off: it is cloud-only inside Google Cloud and limited to US or EU regions, custom processors carry training and per-hour hosting costs, and the workflow around the processors is still yours to build.
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 can classify, but sorting a stack of different document types and routing each to the right extraction is a model you train or processors you configure and maintain yourself.
Neither ships a finished screen where a person corrects a low-confidence value before it lands in your system. You build the review interface and the queue, or configure an add-on.
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, fields, and coordinates. Turning that into the named fields your system expects is mapping code you write and maintain.
Getting clean data into a spreadsheet, database, or downstream system is an integration you build and host on top of the API.
You run the pipeline: the storage, the retries, the monitoring, the account and keys, 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 Azure or Google Cloud 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 coordinates 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 coordinates) { "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 build on Microsoft Azure, your documents must stay on-premises through the container, you need prebuilt tax and ID models, or you want to compose custom models in Studio.
You run on Google Cloud, you chunk documents for an LLM with the Layout Parser, your layouts vary enough to need a custom-trained extractor, or you want prebuilt invoice and receipt processors.
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 Azure Document Intelligence or Document AI 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 document services and a ready-to-use alternative.
Azure AI Document Intelligence and Google Cloud Document AI are both cloud services that read documents and return text, key-value pairs, tables, and custom fields through an API. The biggest practical difference is deployment: Azure can run in a container for on-premises or disconnected use and hosts in any Azure region, while Google Document AI is cloud-only and runs from US or EU regions. Both also support custom-trained extractors, Azure in its Studio and Google in Workbench.
Neither is universally better; it depends on your cloud and your constraints. Pick Azure AI Document Intelligence if you run on Microsoft Azure, need on-premises or private-network processing through its container, or want prebuilt tax and ID models with model composition. Pick Google Document AI if you run on Google Cloud, want a Layout Parser that chunks documents for an LLM, or prefer Workbench for custom extractors. 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 offers a container you can deploy on your own infrastructure, including disconnected and private-network scenarios, which is its standout difference from Google Document AI and AWS Textract. Google Document AI has no on-premises option; it runs only inside Google Cloud, hosted from US or EU regions. If your documents must stay inside your own network, that container is often the deciding factor.
Yes. Google Document AI lets you train a Custom Extractor on your own document layouts in Document AI Workbench, typically with a minimum of around ten labeled samples and fifty recommended. Azure AI Document Intelligence also supports custom training, with template and neural extraction models, custom classification, and the ability to compose several models into one. Both let you tune extraction to documents the prebuilt models do not cover.
It depends on volume, region, and which models you turn on; both price per page, and the cost can flip depending on whether you need plain OCR, forms, or custom extraction. As of June 2026, plain OCR is around $1.50 per 1,000 pages on both. Structured extraction costs more: Azure prebuilt and Layout run about $10 per 1,000 pages and custom extraction about $30, while Google Form Parser runs about $30 per 1,000 pages plus per-hour hosting for deployed custom processors. Confirm the current Azure and Google pricing pages for your exact mix.
Yes. Azure AI Document Intelligence ships prebuilt models for invoices, receipts, IDs, W-2s and other US tax forms, health insurance cards, plus Read for OCR and Layout for structure. Google Document AI offers prebuilt processors for invoices, receipts, W-2s, and IDs, plus Enterprise Document OCR, Form Parser, and Layout Parser. Both cover the common business documents out of the box and let you add custom extractors for the rest.
Yes, in most cases. Both are developer services you reach through a REST API or client library, and turning their raw output into a working process means writing code for classification, review, validation, and export. Azure Document Intelligence Studio and Google Workbench let you label and test without code, but production use on either platform is an engineering project on a cloud account.
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 Azure or Google Cloud account, service account, or pipeline to manage.
The full per-page S0 rate breakdown, from OCR at about $1.50 per 1,000 pages to custom extraction at $30, plus the costs the headline number hides.
Skip the Azure pipeline and get finished data from a product that includes classification, review, and export.
Replace the Document AI processors with a ready-to-use product that classifies, reads, validates, and exports out of the box.
The same head-to-head for Azure and Amazon, with custom training and the on-premises container compared.
The third pairing in the big-three cloud OCR comparison, with Queries, Analyze Lending, and Custom Extractors.
Max file size and pages per request across both clouds, where Azure takes 2,000 pages in one call and Document AI online caps at 15.
The 500 MB and 2,000-page S0 ceilings, the free F0 two-page trap, formats and rate limits, verified from Microsoft docs.
The 15-page online cap that triggers the page-limit error, batch to 1,000 pages, and the quotas that gate throughput.
The end-to-end IDP workflow that classifies, reads, extracts, and validates documents in one pipeline.
The single REST call that replaces an Azure or Document AI pipeline, returning classified type, text, and named fields.
An honest roundup of the leading intelligent document processing tools and the buyer each one fits.
Run the same file you planned to test on Azure or Document AI through DocuOCR, watch it classify, read, and return named fields, then connect the API to process every document that follows on its own.
