All three major cloud OCR APIs charge about $1.50 per 1,000 pages for plain text. The moment you need structured fields, the price jumps roughly twentyfold: about $30 per 1,000 pages for custom extraction on Azure and Google, and about $50 to $70 per 1,000 pages for Forms and Tables on AWS Textract. Every figure below comes from the vendors' own published pricing pages.
Written for US teams costing out a document extraction vendor. Rates vary by region and change over time, so confirm on each vendor's current pricing page. Last updated July 2026.
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Reading text off a page is close to free. Azure Read, AWS Textract Detect Document Text, and Google Enterprise Document OCR all sit at about $1.50 per 1,000 pages, and AWS and Google drop to roughly $0.60 at very high volume. Turning that text into named fields is where the money goes. Azure custom extraction and Google's Custom Extractor and Form Parser are about $30 per 1,000 pages. AWS Textract charges about $50 per 1,000 for Forms and about $70 for Forms, Tables, and Queries in a single call. None of those rates include the classification step, the validation rules, the human review screen, or the engineer who builds and owns the pipeline, and for most US mid-market teams that surrounding work costs more than the API does.
Published rates as of July 2026, taken from each vendor's own pricing page. AWS figures are US West (Oregon). All three vendors vary rates by region and revise them over time, so treat this as a decision aid and confirm the current numbers before you commit.
| What you need | Azure AI Document Intelligence | AWS Textract | Google Document AI | DocuOCR |
|---|---|---|---|---|
| Plain OCR (text only) | About $1.50 per 1,000 | About $1.50 per 1,000 | About $1.50 per 1,000 | Included in plan |
| Layout and document structure | About $10 per 1,000 (Layout) | Part of Analyze Document | About $10 per 1,000 (Layout Parser) | Included in plan |
| Tables | Included in Layout | About $15 per 1,000 | Included in Form Parser | Included in plan |
| Form fields (key-value pairs) | About $10 per 1,000 (prebuilt) | About $50 per 1,000 (Forms) | About $30 per 1,000 (Form Parser) | Included in plan |
| Forms, tables and queries in one call | Layout $10 plus query fields $10 | About $70 per 1,000 | About $30 per 1,000 | Included in plan |
| Custom trained field extraction | About $30 per 1,000 | Not offered, no custom training | About $30 per 1,000 (Custom Extractor) | Included, no training needed |
| Document classification | About $3 per 1,000 | Not offered | About $5 per 1,000 | Included in plan |
| Invoices and receipts | About $10 per 1,000 (prebuilt) | About $10 per 1,000 (Analyze Expense) | About $10 per 1,000, billed in 10-page blocks | Included in plan |
| Identity documents | About $10 per 1,000 (prebuilt ID) | About $25 per 1,000 (Analyze ID) | About $0.10 per document | Included in plan |
| Add-on features | About $6 per 1,000 | Priced inside each API | About $6 per 1,000 | None |
| Idle hosting fee | None | None | About $438 a year per deployed custom processor version | None |
| Volume discount begins | Above 1M pages a month | Above 1M pages a month | Above 1M pages (extraction), 5M (OCR) | At each published plan tier |
| Free tier | F0: 500 pages a month, first 2 pages per request | 3 months only, 100 pages a month of Analyze features | No standing free tier, trial credit only | Test on your own files, no signup |
| Human review of low-confidence fields | You build the screen | You build it, or add A2I, billed separately | You build the screen | Included review screen |
| Classification of a mixed batch | You build the routing | You build the routing | You build the routing | Built in |
| Effective all-in cost per 1,000 pages | Rate plus engineering and Azure services | Rate plus engineering and AWS services | Rate plus hosting, engineering and GCP services | About $14 to $20, pipeline included |
Read that table across a row, not down a column. The row that decides your bill is the one that matches the job you actually have. If you only need the characters on the page, every cloud API is cheap and DocuOCR is not the value pick. If you need a mixed batch classified, the right fields pulled from each document type, low-confidence values checked by a person, and clean records landing in your ERP, then the cloud rate is only the first line of the invoice. Go deeper on any single vendor in our Azure Document Intelligence pricing, AWS Textract pricing, and Google Document AI pricing guides.
Take a realistic US mid-market workload: 35,000 pages a month of mixed business documents, where you need named fields out of each one rather than a wall of text. Here is what each option costs at published rates.
$1,050
per month
Custom extraction at about $30 per 1,000 pages. Add $6 per 1,000 if you need add-ons, and $10 per 1,000 for query fields.
$2,450
per month
Forms, Tables, and Queries at about $70 per 1,000 pages. Drops to about $350 if Analyze Expense alone covers your documents.
$1,087
per month
Custom Extractor at about $30 per 1,000 pages, plus roughly $37 a month to keep one processor version deployed.
$499
per month
The published 35,000 page plan, about $14 per 1,000 pages, with classification, validation, review, and export included.
Change the job from "extract fields" to "give me the text" and the table inverts completely. Those same 35,000 pages run through plain OCR cost about $53 a month on any of the three clouds, against $499 on a DocuOCR plan. If a text dump is genuinely all you need, and you have someone who can wire up an API, use Azure Read, Textract Detect Document Text, or Google Enterprise Document OCR and do not look back. A comparison page that cannot say that out loud is selling, not comparing. The three cloud rates only become expensive when you ask them to understand a document rather than transcribe it, and the products only become cheap when the work they include is work you would otherwise have to do.
Six costs that never appear in a pricing comparison and always appear in a real budget.
Someone stands up the cloud account, configures IAM or service accounts, handles asynchronous jobs for multi-page PDFs, maps raw response blocks to your field names, and owns it forever. This line usually exceeds the API line in year one.
Every one of these APIs will read whatever page you hand it. Working out which document type that page is, so you know which fields to expect and which processor to call, is code you write.
All three return a confidence score. The screen where a person corrects a 68% confident total, and the audit trail of who changed what, is software. AWS bills A2I separately for this.
Google charges about $0.05 an hour, roughly $438 a year, for every deployed custom processor version, used or not. Two versions of three extractors is about $2,600 a year before a single page is read.
Object storage for the documents, serverless compute for orchestration, queues, logging, and egress all land on the same invoice under different line items. Budget 10 to 20 percent on top.
Confirming line items sum to the total, that a date is a real date, and that the result reaches your ERP is application code on every raw API.
You run on Microsoft, you want the broadest set of prebuilt models, and you need custom classification cheaply at about $3 per 1,000 pages. Its Layout model at $10 per 1,000 is the best value structure-aware read of the three.
Your pipeline already lives in AWS, you want deep S3 and Lambda integration, and your documents fit its specialized Analyze Expense, Analyze ID, or Analyze Lending APIs. Avoid it if you need cheap generic form extraction, where it is the most expensive of the three.
You are on Google Cloud, you process millions of pages, or you are feeding documents to a language model where the $10 per 1,000 Layout Parser and its chunking are exactly the right tool. Watch the per-version hosting fee.
You need validated fields this quarter, you do not have an engineer to assign, and the work you actually care about is the classification, review, and export the cloud APIs leave you to build. That is where DocuOCR is priced to win.
If you land on the last box, DocuOCR replaces the whole pipeline rather than the OCR call inside it. It classifies a mixed batch, reads any layout with no template to train, extracts the fields you define, validates them, routes low-confidence values to a built-in review screen, and exports clean records through a dashboard and a single OCR API. It is intelligent document processing sold as a product, with field accuracy of 95 to 99 percent once validation and review are applied. Compare it head to head as an Amazon Textract alternative, an Azure Document Intelligence alternative, or a Google Document AI alternative.
The questions US buyers ask most when they are costing out an OCR or document AI vendor.
Plain OCR costs about $0.0015 per page, or $1.50 per 1,000 pages, on all three major cloud services: Azure AI Document Intelligence Read, AWS Textract Detect Document Text, and Google Enterprise Document OCR. Structured extraction costs far more. Pulling named form fields runs from about $30 per 1,000 pages on Azure and Google to about $50 to $70 per 1,000 pages on AWS Textract.
For plain text, all three are effectively tied at about $1.50 per 1,000 pages, and Google and AWS both drop to about $0.60 per 1,000 at very high volume. For structured field extraction, Azure and Google are cheaper than AWS: custom extraction is about $30 per 1,000 pages on both, while Textract charges about $50 per 1,000 for Forms and about $70 per 1,000 for Forms, Tables, and Queries together.
As of July 2026, in US West (Oregon), Textract charges about $1.50 per 1,000 pages for Detect Document Text, $15 for Tables, $50 for Forms, $70 for Forms with Tables and Queries, $10 for Analyze Expense on invoices and receipts, and $25 for Analyze ID. Rates fall above one million pages a month. Confirm current rates for your region on the AWS pricing page.
On the S0 pay-as-you-go tier, Azure AI Document Intelligence charges about $1.50 per 1,000 pages for Read (OCR), $10 for Layout, $10 for prebuilt models such as invoice, receipt, and ID, $3 for custom classification, and $30 for custom extraction. Add-ons cost about $6 per 1,000 pages and query fields about $10. A free F0 tier covers 500 pages a month.
Google charges about $1.50 per 1,000 pages for the Enterprise Document OCR processor, $10 for the Layout Parser, $30 for the Form Parser and the Custom Extractor, and $5 for a Custom Classifier or Splitter. Prebuilt invoice and expense parsers bill $0.10 per 10 pages, which is $10 per 1,000. Each deployed custom processor version also costs $0.05 per hour to host.
Only partly, and none of them are generous. Azure offers a standing free F0 tier of 500 pages a month, but it reads only the first two pages of each request. AWS Textract gives new accounts three months of free usage, capped at 100 pages a month for the Analyze Document features most buyers want to test. Google Document AI has no standing free tier, only Google Cloud trial credit.
Because it is a harder job. OCR recognizes characters and reports where they sit. Form extraction has to work out that "Invoice Date" is a label, that the text beside it is the matching value, and that the two belong together across a multi-column layout. Table extraction has to rebuild a grid that only exists visually. That inference is what the extra $30 to $70 per 1,000 pages pays for.
The per-page rate is usually the smallest line. Add the engineer who builds and owns the pipeline, the classification step that decides what each page is, the human review screen for low-confidence fields, the validation rules, the export into your ERP, and the cloud storage, compute, and egress around the API. For most US teams the engineering cost exceeds the API cost in year one and recurs every year after.
All three offer a prebuilt invoice model at roughly the same price, about $10 per 1,000 pages. Azure prebuilt invoice and AWS Analyze Expense both bill per page. Google Invoice Parser bills in blocks of ten pages, so an 11-page invoice bills as 20. The bigger decision is not the vendor but whether you want raw JSON fields or a finished, reviewed invoice ready to post.
It depends entirely on what you need. For plain text at volume, no: a cloud API at about $1.50 per 1,000 pages is far cheaper, and you should use one. For structured, validated fields, often yes: the cloud rate climbs to $30 to $70 per 1,000 pages and still returns raw JSON. DocuOCR plans work out to about $14 to $20 per 1,000 pages with classification, review, and export already included.
Yes. Confidence scores do not change the rate on any of the three services. A page read at 60% confidence bills the same as a page read at 99%. Google does not bill requests that fail with a 4xx or 5xx error, but a document it accepts and reads badly is still billed. Low-confidence pages cost twice: once for the API call, and again for the person who checks them.
Three catch people out. Google charges about $0.05 per hour, roughly $438 a year, to host each deployed custom processor version, whether or not you use it. Google also bills prebuilt invoice parsing in 10-page blocks, so page counts round up. Azure add-ons and query fields cost an extra $6 and $10 per 1,000 pages. On every platform, storage, compute, and egress arrive as separate line items.
Every S0 rate, the F0 free tier limits, and the costs the per-page number does not show.
Why Forms costs 33 times what plain OCR does, and how to route pages so it does not.
Per-processor rates, the 10-page invoice rounding rule, and the $438 a year hosting fee.
What intelligent document processing costs across licensing models, beyond the cloud APIs.
The single REST call that replaces a Textract, Azure, or Document AI pipeline.
An honest roundup of the leading document processing tools and the buyer each one fits.
The capability head-to-head behind the price comparison on this page.
Batch processing, SSO, audit logs, and the governance layer around the extraction engine.
The full platform, with a dashboard for teams who want document data without code.
Run the file you were about to send to Textract, Azure, or Document AI through DocuOCR, see the fields come back validated, then decide whether the pipeline is worth building.
