AWS Textract vs Google Document AI vs Azure Document Intelligence: A Comparison
AWS Textract, Google Document AI, and Azure Document Intelligence are three leading cloud platforms for extracting structured data and understanding business documents. While these services support a wide range of document types, they are commonly used in accounts payable automation to process invoices, receipts, purchase orders, and other financial documents.
Document AI goes beyond basic OCR by identifying document layouts, tables, key-value pairs, and important business fields. This allows organizations to convert unstructured documents into structured data that can be used in accounting systems, approval workflows, ERP platforms, and other business processes.
However, AWS Textract, Google Document AI, and Azure Document Intelligence differ in their prebuilt models, pricing structures, customization options, cloud integrations, and the amount of development required to build a complete AP automation workflow.
In this comparison, we examine the three platforms across document extraction capabilities, AP-related features, pricing, integrations, and ideal use cases. We also look at the additional workflow components businesses may need to build around these services and when a specialized document extraction platform such as SoceTonAI DoxTract may be a more practical option.
Quick Comparison Table
| AWS Textract | Google Document AI | Azure Document Intelligence | |
|---|---|---|---|
| Basic OCR | ~$1.50 / 1,000 pages | ~$1.50 / 1,000 pages | ~$1.50 / 1,000 pages |
| Forms / structured extraction | ~$50 / 1,000 pages | ~$30 / 1,000 pages | ~$10–$30 / 1,000 pages |
| Prebuilt invoice/receipt models | ~$8–$10 / 1,000 pages | ~$10 / 1,000 pages (invoice/expense parser) | ~$10 / 1,000 pages |
| Custom extraction | Adapter training required | ~$20–$30 / 1,000 pages + hosting | ~$30–$50 / 1,000 pages + training |
| Always-on hosting fee for custom models | No | ~$0.05/hr per deployed version (~$438/yr) | Training billed hourly beyond free hours |
| Free tier | 1,000 pages, 90 days (basic OCR only) | Trial credit, no permanent free tier | 500 pages/month, capped at first 2 pages per request |
| Best fit | Teams already on AWS infrastructure | Teams already on GCP, need Gemini-based layout parsing | Teams already on Microsoft/Azure stack |
Prices vary by region and change often — treat these as directional, not a quote. Each vendor's own pricing page is the source of truth at decision time.
AWS Textract
Amazon Textract extracts printed text, handwriting, tables, forms, and key-value pairs through five separate APIs: Detect Document Text, Analyze Document (Forms/Tables/Queries/Signatures), Analyze Expense, Analyze ID, and Analyze Lending. Each capability is billed separately, and calling more than one feature on the same page means paying for each feature individually.
Pricing: Basic OCR runs about $1.50 per 1,000 pages. Add Tables and the rate jumps to roughly $15 per 1,000 pages; add Forms and it climbs to around $50 per 1,000 pages. The purpose-built Analyze Expense API for invoices and receipts runs closer to $8–$10 per 1,000 pages. There's a 90-day free tier, but it only covers 1,000 pages of the basic Detect Document Text API — tables, forms, and expense extraction are billed from page one.
Where it's strong: If your stack already lives in S3, Lambda, and IAM, Textract slots in natively and scales to millions of pages without you managing infrastructure. It's a solid pick for AWS-native pipelines with high, predictable volume.
Where it struggles for AP automation: Textract gives you raw extraction primitives, not a finished workflow. There's no built-in vendor/PO matching, no approval routing, and no review UI — you build all of that yourself. For teams processing mixed document types (invoices, POs, receipts) across multiple vendor templates, the per-feature billing model gets expensive and hard to predict fast.
Google Document AI
Google's platform is organized around "processors" — Enterprise Document OCR, Layout Parser, Form Parser, and prebuilt parsers for invoices, receipts, and utility bills, plus custom extractors you train yourself. In 2026, Google folded Gemini into its Layout Parser, which noticeably improved table recognition and reading order on multi-column financial documents compared to the legacy parser.
Pricing: Enterprise Document OCR starts around $1.50 per 1,000 pages (dropping to $0.60 past 5 million pages/month). Form Parser and Custom Extractors run roughly $30 per 1,000 pages at standard volume. The prebuilt Invoice and Expense parsers are priced closer to $10 per 1,000 pages. Every deployed custom-processor version also carries an always-on hosting charge of about $0.05/hour — roughly $438 a year per processor, whether you use it or not.
Where it's strong: Teams already invested in GCP, BigQuery, and Vertex AI get tight integration and access to Gemini-grade layout understanding. New accounts also get a meaningful trial credit for testing before committing.
Where it struggles for AP automation: Google deprecated its built-in human-in-the-loop review feature, so any exception-handling or reviewer workflow has to be built and maintained by your own team. Pricing complexity — four-plus tiers, hosting fees, training costs — also makes budgeting for a growing AP queue harder than it should be.
Azure Document Intelligence
Formerly Form Recognizer, Azure's service (now under Foundry Tools) offers a Read API for plain OCR, a Layout API, more than a dozen prebuilt models (invoice, receipt, ID, W-2, health insurance card, contract, and more), and custom classification/extraction models you train on your own templates.
Pricing: Read (OCR-only) is about $1.50 per 1,000 pages. Prebuilt models, including the Invoice and Receipt models most relevant for AP automation, run around $10 per 1,000 pages. Custom extraction lands between $30–$50 per 1,000 pages depending on the source, plus training time billed hourly beyond the first free hours. The free tier (F0) allows 500 pages a month but only processes the first two pages of any request — a 10-page invoice would burn five separate free-tier calls, making it impractical for real testing.
Where it's strong: Azure has the broadest set of prebuilt financial-document models out of the box and integrates cleanly with Logic Apps, Power Platform, and Dynamics 365 — a natural fit if your ERP already lives on Microsoft's stack. Commitment tiers can bring per-page costs down significantly at very high volume.
Where it struggles for AP automation: Like the other two, Azure hands you an extraction API, not an accounts payable workflow. Classifying documents before extraction adds its own billing tier, multi-page documents are billed page-by-page (a 40-page contract is 40 billable pages), and building a production-grade review and approval layer is entirely on your engineering team.
AWS Textract vs Google Document AI
AWS Textract is generally the simpler choice for teams already using AWS services such as S3, Lambda, and IAM. Google Document AI offers a broader processor-based platform and can be more suitable for complex document layouts, custom extraction, and GCP-based workflows.
For invoice and receipt extraction, both provide prebuilt models. Textract may be easier to integrate into an AWS-native pipeline, while Google Document AI provides more specialized processors and stronger document-layout tooling. The better choice depends mainly on your cloud environment, document types, and need for custom extraction.
Google Document AI vs Azure Document Intelligence
Google Document AI and Azure Document Intelligence both offer OCR, layout analysis, prebuilt document models, and custom extraction. Google is a strong fit for teams using Google Cloud, BigQuery, or Vertex AI. Azure is often the more natural choice for organizations already using Microsoft Azure, Power Platform, Dynamics 365, or Logic Apps.
For invoice processing, both have prebuilt invoice models. Azure may be easier to adopt in a Microsoft-centered business environment, while Google provides a broader processor-based architecture for teams building document-processing pipelines on GCP.
AWS Textract vs Azure Document Intelligence
AWS Textract is tightly integrated with AWS infrastructure and is often a straightforward option for AWS-native applications. Azure Document Intelligence provides a broad collection of prebuilt models and integrates with Microsoft business tools.
For teams already committed to AWS, Textract usually creates less infrastructure friction. For organizations using Azure, Dynamics 365, Power Automate, or Microsoft business software, Azure Document Intelligence may be the more convenient option.
The Pattern Across All Three
AWS Textract, Google Document AI, and Azure Document Intelligence all share the same shape: strong raw OCR, tiered pricing that escalates sharply once you need structured fields instead of plain text, and no built-in accounts payable workflow. Teams that need review workflows, approval routing, vendor matching, or custom exception handling may need to build those capabilities around the extraction APIs. That gap is usually where the real engineering cost hides, on top of the per-page rate.
Existing IDP and Accounts Payable Automation Providers
Beyond the three hyperscaler APIs above, a number of dedicated IDP and AP automation platforms compete directly on ease of use, pricing simplicity, and built-in workflow — including invoice-specific extraction, vendor matching, and approval routing out of the box.
SoceTonAI DoxTract — Document OCR and structured data extraction API and SaaS built specifically for invoice, receipt, and purchase order automation. Positioned as a simpler, lower-cost alternative to the hyperscaler APIs above, with flat, predictable pricing instead of per-feature billing, a free tier covering 200 pages/month plus 20 lifetime templates, and a developer-friendly API alongside a no-code console.
Nanonets — No-code IDP platform with prebuilt invoice and receipt models, approval workflows, and ERP integrations.
Mindee — Developer-focused OCR API with prebuilt invoice, receipt, and ID parsers and a generous free tier for testing.
Veryfi — Real-time OCR API aimed at receipts, invoices, and expense automation with a focus on data privacy and speed.
Rossum — AI-driven document processing platform with a strong focus on AP automation, vendor matching, and email-based invoice intake.
Docsumo — No-code intelligent document processing platform aimed at finance and lending teams, with prebuilt AP and bank statement models.
Klippa — European IDP and expense/invoice automation platform with strong OCR for receipts and multilingual documents.
ABBYY — Long-established OCR and IDP vendor (FlexiCapture, Vantage) used widely in enterprise document capture, including AP.
If your team is choosing between building on a hyperscaler API and buying a purpose-built platform, the deciding factor usually isn't raw OCR accuracy — all of the above are competent there. It's whether you want to own the review UI, matching logic, and pricing complexity yourself, or start from a platform that already ships with it.
Where SoceTonAI DoxTract Fits
SoceTonAI DoxTract was built as a direct response to the gap described above: developers and finance teams want strong structured document extraction without complex per-feature billing without Textract-style per-feature billing, and without building an entire AP review workflow from scratch. It combines an invoice/receipt/PO-focused extraction API with a console for non-technical reviewers, flat and predictable pricing, and a free tier generous enough to actually test on real documents before committing.
If you're evaluating AWS Textract, Google Document AI, or Azure Document Intelligence for invoice or AP automation, it's worth comparing the total cost — API rate plus the engineering time to build review, matching, and audit workflows — against a platform that includes those pieces already. Try SoceTonAI DoxTract free and see how it stacks up on your own documents.
Which Document AI Service Should You Choose?
Choose AWS Textract if:
Your infrastructure already runs on AWS
You use S3, Lambda, and IAM
You need OCR, forms, tables, or expense extraction through AWS APIs
Choose Google Document AI if:
Your application is built on Google Cloud
You need specialized document processors
Complex document layouts and custom extraction are important
Choose Azure Document Intelligence if:
Your organization already uses Azure or Microsoft business tools
You need Microsoft ecosystem integration
You want access to a broad set of prebuilt document models
Choose SoceTonAI DoxTract if:
You want predictable extraction pricing
You need structured data from invoices, receipts, purchase orders, or business documents
You want a no-code template editor alongside an Document AI API
You want to avoid building document templates and extraction workflows entirely from scratch
Conclusion
AWS Textract, Google Document AI, and Azure Document Intelligence are all capable Document AI platforms for extracting structured data from invoices, receipts, purchase orders, and other business documents. The best choice often depends on your existing cloud infrastructure, document-processing requirements, and the level of customization your workflow needs.
AWS Textract is a practical option for teams already building on AWS. Google Document AI offers a broad range of document processors and can be a strong choice for organizations using Google Cloud. Azure Document Intelligence integrates naturally with the Microsoft ecosystem and may be well suited to businesses using Azure and Microsoft business tools.
However, document extraction is only one part of AP automation. Businesses may still need to build workflows for document review, approvals, exception handling, accounting integration, and data export around these APIs.
For teams that want a more focused document extraction solution with predictable pricing, structured outputs, and a no-code template builder, SoceTonAI DoxTract can be a practical alternative or complementary tool.
Frequently Asked Questions
Is AWS Textract better than Google Document AI?
Neither is universally better. AWS Textract is often more convenient for AWS-native applications, while Google Document AI provides a broader processor-based platform and may be better suited to teams building document workflows on Google Cloud.
Is Google Document AI better than Azure Document Intelligence?
The better option depends largely on your cloud ecosystem. Google Document AI fits naturally into GCP workflows, while Azure Document Intelligence integrates closely with Azure, Power Platform, and Microsoft business products.
Which is cheaper: AWS Textract, Google Document AI, or Azure Document Intelligence?
Basic OCR pricing is broadly similar, but structured extraction, prebuilt models, custom models, training, and additional features can change the total cost significantly. The cheapest option depends on the document type and extraction features required.
Can AWS Textract extract invoice line items?
AWS Textract provides an expense-analysis capability for invoices and receipts that can extract common expense fields and line-item information.
Do AWS Textract, Google Document AI, and Azure Document Intelligence provide a complete AP workflow?
These services primarily provide document extraction capabilities. Businesses needing approval workflows, vendor matching, exception handling, or audit processes may need to build those components separately or use a specialized AP automation platform.