Amazon Textract and Google Cloud Document AI are both cloud OCR and data-extraction services you call by API. Textract adds natural-language Queries and an Analyze Lending workflow for mortgages; Document AI adds custom extractors you train yourself and a Layout Parser for LLM pipelines. 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.
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 with no custom training, integrates natively with S3, Lambda, and IAM, and returns text and data with bounding boxes you map in code.
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 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 AWS or Google Cloud account, IAM, service account, 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 Google Cloud documentation and pricing pages, June 2026.
| Factor | Amazon Textract | Google Document AI | DocuOCR |
|---|---|---|---|
| Type of tool | Cloud OCR service, API | Cloud document platform, API and processors | Ready-to-use product, plus REST API |
| Who it is for | AWS-native developer teams | Google Cloud-native developer teams | Business teams and developers |
| Getting started | AWS account, IAM, and a pipeline you build | GCP project, service account, and processors you wire up | Sign in and process a document |
| Custom model training | No, generalized models only | Yes, train a Custom Extractor in Workbench | Define a schema, no model to train |
| Classify a mixed batch | Build your own routing | 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 |
| Natural-language queries | Yes, the Queries feature | No, processor and schema based | Define the fields you want by name |
| Specialized models | Expense, ID, Lending for mortgages | Invoice, receipt, W-2, ID, plus custom | Any document, by the schema you set |
| LLM and RAG support | Map the output yourself | Layout Parser chunks documents for an LLM | Returns clean fields ready to use |
| On-premises option | No, AWS cloud only | No, Google Cloud only | Ask us about deployment |
| What you get back | Text, key-value pairs, tables, bounding boxes 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 | 1,000 text pages per month for 3 months | New-customer Google Cloud credits, no ongoing OCR free tier | Free on your own files, no signup |
| Pricing model | Per page by feature, 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 AWS Textract 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 forms cost more (Textract Forms about $50 per 1,000 pages, Google Form Parser about $30 per 1,000). 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: there is no custom model training, it is cloud-only inside AWS, 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, 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 work one document or processor at a time. Sorting a stack of different document types and routing each to the right extraction is configuration and code you 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, entities, 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 IAM or service account, 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 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 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 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 run on Google Cloud, your layouts vary enough to need a custom-trained extractor, you chunk documents for an LLM, 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 Textract 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 OCR services and a ready-to-use alternative.
Amazon Textract and Google Cloud Document AI are both cloud services that read documents and return text, key-value pairs, and tables through an API. The main difference is custom training and ecosystem: Document AI lets you train a custom extractor on your own document layouts in Workbench, while Textract relies on generalized models, adds natural-language Queries, and integrates tightly with AWS.
Neither is universally better; it depends on your cloud and your documents. Pick Amazon Textract if you build on AWS, process mortgage packages, or want natural-language Queries. Pick Google Document AI if you run on Google Cloud, need custom-trained extractors, or want a Layout Parser that chunks documents for an LLM. If you want a finished workflow instead of an API, a ready-to-use product like DocuOCR fits better than either.
No. Amazon Textract uses generalized, pre-trained models and does not let you fine-tune a model on your own document layouts; you adapt it with the Queries feature instead. Google Document AI does support custom extractors that you train on a small set of sample documents in Document AI Workbench. If your layouts are unusual or vary by vendor, that training difference often decides the choice.
Yes. Google Document AI includes an Enterprise Document OCR processor that returns text, layout, and structure, the direct counterpart to Textract's Detect Document Text API. Document AI then adds Form Parser, Layout Parser, prebuilt processors for invoices, receipts, W-2s, and IDs, and Custom Extractor. Both are reached through a REST API or client library on their respective clouds.
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 plain OCR, forms, tables, or custom extractors. As of June 2026, plain OCR is around $1.50 per 1,000 pages on both, while forms-style extraction runs higher (Textract Forms around $50 per 1,000, Google Form Parser around $30 per 1,000). Confirm the current AWS and Google pricing pages for your exact mix.
No. Both are cloud-only services: Textract runs inside AWS regions and Document AI runs inside Google Cloud. Neither offers an on-premises deployment the way Azure Document Intelligence does with its container. If processing must stay inside your own network, that rules both out and points you toward a deployment-flexible option instead.
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. You can click through each console to test a sample document, 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 AWS or Google Cloud account, IAM, or service account to manage.
Accuracy depends on your document types, not a single winner. Generalized models like Textract are strong on clean, structured forms and tables, while a custom-trained Document AI extractor can edge ahead on irregular layouts it was tuned for. The reliable answer is to run both, and a ready-to-use option, on your own documents and measure the result rather than trusting a published benchmark.
Skip the AWS 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 other Google option compared: structured document extraction versus Google Cloud Vision's general-purpose image OCR.
The same head-to-head for the third major cloud OCR service, with custom training and an on-premises container.
The Microsoft and Google pairing that completes the big-three cloud OCR comparison, with the on-premises container compared.
The single REST call that replaces a Textract 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.
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 Textract 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.
