Azure Document Intelligence vs Textract vs Google Document AI

Last updated June 2026.

If you are choosing a cloud service to read data out of documents, you will land on three names: Azure AI Document Intelligence, AWS Textract, and Google Document AI. All three are developer APIs from the big cloud providers, all three return text, tables, and structured fields as JSON, and all three are billed per page. They are close enough that the right pick usually comes down to which cloud you already run on, which document types you process, and how much you process each month. This guide compares them on pricing, accuracy, prebuilt models, and fit, and shows where a finished extraction product makes more sense than any of them.

Azure Document Intelligence vs Textract vs Google Document AI: the short answer

Pick the service that matches the cloud your team already runs on. Azure AI Document Intelligence fits teams on Microsoft Azure, Power Platform, or Dynamics 365 and has the broadest set of prebuilt models. AWS Textract fits pipelines already inside AWS and is strong on forms and tables, though its forms-and-tables rate is the highest of the three. Google Document AI fits Google Cloud shops and gets cheaper than the others at very high OCR volume. All three are services you build software around, not products you sign in and use, so factor in the engineering time before you compare the per-page prices. For a deeper single-vendor breakdown, see our full Azure Document Intelligence pricing guide, or the companion AWS Textract pricing and Google Document AI pricing guides. For every rate on one page, see the OCR API pricing comparison.

Quick comparison table

Published rates as of mid-2026, billed per 1,000 pages on the pay-as-you-go tiers. Always check each provider's current pricing page before you commit, because all three change rates and tier structures over time.

Pricing compared

For plain OCR (reading printed and handwritten text with no field structure), the three are roughly even at about $1.50 per 1,000 pages, and Google Document AI drops below the others once you process millions of pages a month. The gap opens up on forms and tables. AWS Textract's combined forms-and-tables analysis is the most expensive of the three at roughly $0.065 per page, while Azure's Layout model and Google's Form Parser come in lower. For named fields off invoices, receipts, and IDs, all three offer prebuilt models in a similar range, and all three let you train custom models for documents the prebuilt ones do not cover, which costs more per page. The headline per-page number is only part of the bill. The larger cost is almost always the engineering work to call the API, validate low-confidence reads, handle exceptions, and move clean data into your systems.

Which is more accurate, Textract, Azure, or Google Document AI?

No single service wins on accuracy across every document. Independent benchmarks tend to show all three landing in the same high-90s range on clean, typed pages, with results diverging on messy scans, dense tables, and handwriting. Textract is often praised for table structure, Azure for its prebuilt invoice and receipt models, and Google for raw OCR on difficult scans. The honest answer is that accuracy depends on your specific layouts, so the only number that matters is the one you measure on your own documents. Run a representative sample through each service, check the fields you actually care about, and compare confidence scores before you commit.

What is the difference between Azure Document Intelligence and Textract?

The core difference is the cloud and the model lineup. Azure AI Document Intelligence (formerly Azure Form Recognizer) lives in Microsoft Azure and ships a wide set of prebuilt models plus a layout model and custom training, which makes it convenient for teams already in the Microsoft ecosystem. AWS Textract lives in AWS and centers on three operations: detecting text, analyzing forms and tables, and reading expense, invoice, and identity documents, with a Queries feature for asking for specific fields. Functionally they overlap heavily. The practical tiebreaker is which provider already holds your data and your developers' time, since both are services you wrap in software rather than finished apps.

Is Google Document AI better than Textract?

Better depends on your stack and volume, not on a universal ranking. Google Document AI is the stronger choice if you run on Google Cloud, want its specialized processors, or process a very high page volume where its OCR rate pulls ahead. AWS Textract is the stronger choice if your pipeline already runs in AWS and you lean on its forms and tables analysis. On typical mid-volume workloads the two are close on both accuracy and price, so the deciding factors are usually cloud fit, the specific processors you need, and how much custom training each one requires for your documents.

Do these services include a way to review and correct results?

Not as a finished workflow. All three return JSON with confidence scores, but the screen where a person checks a low-confidence read, fixes it, and approves the document is something you build yourself, or bolt on through an add-on like Amazon A2I or a Google human-in-the-loop option. For one-off or fully automated pipelines that is fine. For a process where staff need to catch and correct the occasional bad read before data flows downstream, that review layer is real work that the per-page price does not include. This is the most common reason teams move from a raw cloud API to a ready-to-use product.

When should you use a finished product instead of a cloud OCR API?

Use a product instead of an API when you want the classify, read, extract, validate, and export workflow already built rather than a service to assemble it from. Azure Document Intelligence, AWS Textract, and Google Document AI are excellent engines, but they assume you have developers to call the API, train models, wire up review, and maintain the integration. If you have one or two use cases, a small team, or a timeline measured in weeks, standing up a cloud project can outweigh the benefit. Intelligent document processing software gives you the same extraction the APIs target as something you sign in and start using, with the validation step and export already in place. Developers who still want a single endpoint can pull the same structured result through one OCR API call without managing three model catalogs.

How to choose between them

Start with the cloud you already run on, because cross-cloud data movement and a second set of credentials and bills rarely pay for themselves. Then list the exact document types and fields you need and check which provider has a prebuilt model for them, so you can avoid custom training where possible. Estimate your monthly page volume and price the specific operations you will use, not just the cheapest OCR rate. Finally, run your own documents through each service and compare accuracy and confidence on the fields that matter. If the work of building the surrounding software starts to look bigger than the extraction itself, that is the signal to compare a finished tool. See our deeper writeups on the Azure Document Intelligence alternative, the Amazon Textract alternative, and the Google Document AI alternative for a side-by-side look at each, and read how document data extraction software covers the full pipeline.

Common use cases and where to specialize

Most teams reach for these services to automate data entry from a handful of recurring document types. If your goal is specifically pulling line items and totals off vendor invoices, a dedicated tool such as invoice data extraction software or full accounts payable automation will get you there faster than training a generic model. For converting PDF bank statements into spreadsheets, a purpose-built bank statement to Excel converter handles the layout quirks the general APIs stumble on. The pattern holds across document types: the cloud APIs are general engines, and a focused product or a ready-to-use extraction workflow usually beats them on time-to-value for a specific job. Whichever route you take, test on your real documents first and let measured accuracy, not the brand name, make the call.