TL;DR: AWS Textract and Google Document AI compete in 2025 on intelligent document reading. Textract stands out with manuscripts, speed, and costs; Google excels with GDPR compliance and advanced features. Here is our complete comparison to help you choose based on your use cases.
Why this comparison is crucial in 2025
The rise of document AI for businesses
AWS Textract and Google Document AI dominate the automatic document reading market. In 2025, they are at the heart of AI workflows to automate data extraction from invoices, bank statements, or HR files. These tools transform raw text into structured JSON, ready to integrate into processing chains or feed into a RAG model.
The need for precise OCR, reliable structure, controlled costs, and GDPR compliance has exploded with the massive adoption of generative AI in enterprises. Today, you must choose between two leading solutions, each with its strengths:
AWS Textract is appealing for its raw performance, manuscript handling, and native AWS integrations.
Google Document AI excels in multilingual support, data security, and connections with the GCP ecosystem.
This comparison gives you a clear and quantified view to make an informed decision based on your use cases: real-time extraction, bulk processing, or regulatory compliance.
OCR accuracy: who reads your documents better?
Recognition of printed, handwritten, and disorganized text
In 2025, OCR accuracy is a decisive criterion. On real datasets (invoices, forms, scanned documents), AWS Textract achieves an overall accuracy of 99.3%, compared to 98.0% for Google Document AI (AI Multiple, 2025).
Detailed OCR performance comparison:
Document type
AWS Textract
Google Document AI
Printed text
99.8%
99.5%
Scanned documents
98.7%
97.9%
Handwriting
95.2%
93.8%
Disorganized text
96.4%
94.1%
Textract has the advantage on complex documents involving misaligned text, cursive handwriting, or unbordered tables. In April 2025, Google released a new model, AI Native v2, which improves its performance on unstructured text by 15% (Planet AI, 2025).
Note: Textract may still fail on 'broken' or highly noisy documents, although this case remains marginal.
Our analysis
Are you processing printed text in bulk? Both tools are equal. But for handwriting, disorganized multilingual text, or complex forms, Textract maintains a slight edge.
Structure extraction: forms, tables, and JSON output
Comparison of output formats and extracted field types
Character recognition is not enough. To automate document processing, you need to extract a usable structure: key-value fields, table rows, signatures, checkboxes…
In 2025, both AWS Textract and Google Document AI offer a structured JSON format, with spatial positions (bounding boxes) and relationships between elements.
Example JSON output (Textract)
json
→ Blocks are classified by type: line, word, key-value field, signature, etc. (AWS Docs, 2025).
Example Google Document AI
json
→ Each token is annotated with a confidence score, useful for filtering or automatic validation (Google AI, 2025).
Functional comparison:
Function
AWS Textract
Google Document AI
Table detection
✅ Yes
✅ Yes (with scores)
Key-value fields
✅ Yes
✅ Yes
Checkboxes
✅ Yes
✅ Yes
Signatures
✅ (Textract Signatures)
❌ Not native
Hierarchical JSON
✅
✅
Confidence score
❌ No
✅ Yes
Our analysis
-> Textract is richer for complex forms (check-boxes, signatures).
-> Document AI is more suitable if you need automatic validation based on a confidence score per field.
Both tools integrate well into JSON-first pipelines, but Textract's output is often more verbose, while Google's is more precise and weighted.
Costs at scale: which is more cost-effective?
Simulation for 10k, 100k, and 1M pages/month
In a production context, the cost per processed page is central. Invoices, HR files, forms — each scanned page or analyzed PDF increases the monthly bill. In 2025, the prices between AWS Textract and Google Document AI remain competitive, but not equivalent.
→ Textract also offers a free tier of 1,000 pages/month (AWS Pricing, 2025). Google does not offer a sustainable free equivalent, aside from occasional credits for new accounts (Google Pricing, 2025).
Available optimizations
Textract:
Discounts for commitment (Enterprise plan)
Volume-based pricing
EU regions optimized for taxes (e.g. Frankfurt, Paris)
Google Document AI:
More predictable pricing via Cloud Billing
Better cost visibility by document category
Our analysis
Textract remains cheaper at equal volume, especially beyond 100k pages. But Google offers a more transparent cost model, with more granular billing. For startups or public services, the difference may hinge on the level of commitment required.
Technical performance and scalability
Latency, quotas, page limits, and integration into your workflows
In large-scale document processing, speed and infrastructure robustness are crucial. Both AWS Textract and Google Document AI have evolved to meet latency, throughput, and resilience requirements, but with nuances.
Conclusion: which tool to choose based on your needs?
Concrete recommendations for three user profiles
AWS Textract vs Google Document AI: the 2025 duel reveals two mature and powerful approaches to document AI. While their performances are close, their respective strengths make them more suitable for distinct contexts.
🔹 1. Are you processing handwritten or complex documents?
Choose AWS Textract. ✅ Better accuracy on handwriting ✅ High throughput and large files ✅ Native AWS integration (S3, Lambda, Bedrock)
Ideal for HR, notarial archives, handwritten forms.
🔹 2. Are you looking for a GDPR-friendly and multilingual cloud solution?
Choose Google Document AI. ✅ EU storage, advanced compliance ✅ Very good language support ✅ Usable confidence scores
Perfect for banks, insurance, international public services.
🔹 3. Do you have a large volume and a tight budget?
AWS Textract is more cost-effective. 💰 Up to 20% savings on volumes > 100k pages 🆓 Free tier of 1,000 pages/month for testing
Final verdict: Both tools are excellent but do not shine in the same areas. 👉 Textract for complex, large, or hybrid documents. 👉 Document AI for GCP environments, multilingual, or regulated contexts.
If you are still unsure or need specific support, our team is here to help
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Romain DE LA SOUCHÈRE
Tech Lead, CTO AXI Technologies
Expert Data Engineering et Cloud, Romain affiche plus de 11 ans d'expérience, dont plusieurs années comme Lead Developer sur des solutions Smart Building haute performance. Il y a conçu et mis en production des moteurs de traitement capables d'absorber des centaines de milliers de données de capteurs par minute, ainsi que des bases clusterisées gérant plus de 10 millions de données dynamiques. Certifié Microsoft Azure DevOps Engineer Expert, il maîtrise aussi bien le développement back-end (Python, C#) que le DevOps (Docker, Kubernetes, Terraform) et les agents LLM. Formateur en Python, cloud, DevOps et IA générative appliquée, il forme avec une obsession : Amener chaque apprenant à concevoir et déployer des architectures réellement scalables en production.
