Amazon Textract vs Google Document AI

Detailed side-by-side comparison to help you choose the right tool

Amazon Textract

🔴Developer

Automation & Workflows

AWS document intelligence service that extracts text, tables, forms, and handwriting from scanned documents using machine learning — with specialized APIs for invoices, IDs, and lending documents.

Was this helpful?

Starting Price

Free tier

Google Document AI

🔴Developer

Document 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.

Was this helpful?

Starting Price

Free

Feature Comparison

Scroll horizontally to compare details.

FeatureAmazon TextractGoogle Document AI
CategoryAutomation & WorkflowsDocument Processing AI
Pricing Plans8 tiers57 tiers
Starting PriceFree tierFree
Key Features
  • Optical Character Recognition (OCR)
  • Table extraction with cell relationships
  • Form key-value pair extraction
  • OCR Text Extraction
  • Layout Analysis
  • Entity Recognition

💡 Our Take

Choose Amazon Textract for tight AWS integration with S3, Lambda, and SNS, plus best-in-class handwriting recognition for cursive and mixed content. Choose Google Document AI if you need custom processors with low-code training, want stronger multilingual support across 200+ languages, or are building on Google Cloud with BigQuery and Vertex AI.

Amazon Textract - Pros & Cons

Pros

  • Deep AWS ecosystem integration with S3, Lambda, SNS, DynamoDB, and Kendra for fully automated pipelines
  • Strong handwriting recognition with 85-90% accuracy that outperforms Azure and Google for cursive text
  • Highly competitive per-page pricing at scale — drops to $0.0006/page after 1 million pages monthly
  • Specialized APIs for invoices, IDs, and lending documents reduce custom development time significantly
  • Fully managed service with automatic scaling — no infrastructure to maintain or capacity planning required
  • Handles documents up to 3,000 pages via async processing with SNS completion notifications

Cons

  • No custom model training — limited to AWS prebuilt extraction models only
  • Complex nested JSON output requires significant preprocessing for LLM and RAG applications
  • Table extraction accuracy trails Azure Document Intelligence on highly complex layouts
  • Synchronous API limited to single pages — multi-page workflows require S3 storage and async processing
  • AWS lock-in — tightly coupled with S3, Lambda, IAM, and other AWS services, making multi-cloud difficult

Google Document AI - Pros & Cons

Pros

  • Industry-leading OCR accuracy across 200+ languages, including strong performance on handwriting, low-resolution scans, and rotated or skewed pages
  • Broad library of pre-trained specialized processors (Invoice, Receipt, W-2, 1099, Identity Document, Bank Statement, Paystub, Mortgage) that work out of the box without custom training
  • Custom Extractor and Foundation Models let teams build domain-specific processors with relatively small labeled datasets via the Document AI Workbench
  • Deep integration with Google Cloud services such as Cloud Storage, BigQuery, Vertex AI, and Gemini, simplifying end-to-end document pipelines
  • Enterprise-grade security and compliance posture including VPC Service Controls, CMEK, data residency, HIPAA, SOC 2, and ISO 27001 coverage
  • Built-in Human-in-the-Loop (HITL) review workflow that surfaces low-confidence fields for human verification before downstream processing

Cons

  • Per-page pricing for specialized processors (up to ~$0.065/page) can become expensive at high volumes compared to running self-hosted OCR
  • Requires Google Cloud familiarity — IAM, billing, project setup, and SDK usage create a meaningful onboarding curve for non-GCP shops
  • Some specialized processors are US/region-specific (e.g., US tax forms, US driver license), limiting their usefulness for global document sets
  • Custom processor training and tuning still requires labeled data and iteration, and accuracy on highly variable layouts can fall short of pre-trained domains
  • Quotas, regional availability, and processor versioning differences can complicate multi-region deployments and require careful capacity planning

Not sure which to pick?

🎯 Take our quiz →

🔒 Security & Compliance Comparison

Scroll horizontally to compare details.

Security FeatureAmazon TextractGoogle Document AI
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA✅ Yes✅ Yes
SSO✅ Yes✅ Yes
Self-Hosted❌ No❌ No
On-Prem❌ No❌ No
RBAC✅ Yes✅ Yes
Audit Log✅ Yes✅ Yes
Open Source❌ No❌ No
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyUS, EU, ASIAUS, EU, ASIA
Data Retentionconfigurableconfigurable
🦞

New to AI tools?

Read practical guides for choosing and using AI tools

🔔

Price Drop Alerts

Get notified when AI tools lower their prices

Tracking 2 tools

We only email when prices actually change. No spam, ever.

Get weekly AI agent tool insights

Comparisons, new tool launches, and expert recommendations delivered to your inbox.

No spam. Unsubscribe anytime.

Ready to Choose?

Read the full reviews to make an informed decision