Amazon Textract vs Google Document AI
Detailed side-by-side comparison to help you choose the right tool
Amazon Textract
Document Processing
AWS document processing service that extracts text, tables, forms, and structured data from scanned documents and images using machine learning. Pay-per-page pricing starting at $0.0015/page for OCR.
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CustomGoogle Document AI
🔴DeveloperDocument Processing AI
Cloud document processing platform that automates data extraction and classification with industry-leading OCR accuracy. Processes invoices, receipts, forms, and custom document types to optimize document workflows and improve processing efficiency.
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Amazon Textract - Pros & Cons
Pros
- ✓Pay-per-page pricing starting at $0.0015/page with volume discounts makes costs predictable and proportional to usage
- ✓Seamless AWS ecosystem integration with S3, Lambda, SNS, and DynamoDB for automated document processing workflows
- ✓Handwriting recognition accurately extracts mixed printed and handwritten content that many competitors miss
- ✓Specialized extraction models for invoices, IDs, and lending documents understand domain-specific formats without configuration
- ✓Asynchronous processing handles documents up to 3,000 pages as background jobs with automatic scaling
- ✓No infrastructure management required: fully managed service with automatic scaling and high availability
- ✓3-month free tier with 1,000 OCR pages/month lets teams evaluate the service before committing
Cons
- ✗No custom model training: limited to prebuilt extraction models, unlike Azure Document Intelligence which supports custom training
- ✗JSON output with bounding boxes requires significant post-processing for LLM and RAG applications expecting plain text
- ✗Table extraction accuracy for highly complex, nested layouts trails Azure Document Intelligence capabilities
- ✗Synchronous API limited to single-page documents; multi-page processing requires S3 and async workflows
- ✗AWS-only deployment with no on-premises option for organizations with strict data residency requirements
Google Document AI - Pros & Cons
Pros
- ✓Industry-leading OCR accuracy leveraging Google's text recognition technology from Lens and Photos
- ✓Semantic entity extraction that understands document types and field relationships, not just key-value pairs
- ✓Processor-based architecture makes it easy to add specialized document understanding without custom training
- ✓Competitive free tier (1,000 pages/month) for evaluation and small-scale production
Cons
- ✗Google Cloud dependency with significant setup overhead (project creation, API enablement, IAM configuration)
- ✗SDK support is primarily Python and Node.js — less multi-language coverage than Azure's document services
- ✗Documentation organization and example quality has historically lagged behind Azure and AWS equivalents
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