TL;DR : AWS Textract and Azure Document Intelligence are the leaders in cloud OCR. For a hybrid information system in 2025, Azure stands out with its pre-trained models and HDS compliance. AWS retains an advantage in real-time latency and table accuracy. The choice will depend on your multi-cloud architecture and your security requirements.
Introduction: Two giants of Document AI serving hybrid IT departments
In 2025, IT departments juggle multi-cloud information systems, often split between AWS and Azure. With the increasing volume of documents to process, automatic extraction via OCR has become a strategic pillar of digital transformation. Two services dominate the sector: AWS Textract and Azure Document Intelligence.
Their promise? To convert invoices, contracts, or forms into actionable data with a high level of accuracy. But how to choose between the two when operating in a hybrid AWS-Azure environment, with regional latency requirements, controlled costs, and customizable models?
This comparison guides you through the performance, costs, and technical strengths of each solution— to make an informed choice aligned with your IT strategy.
Comparison of OCR performance and structured extraction
Text recognition: AWS vs Azure on complex forms
AWS Textract achieves an average Character Error Rate (CER) of 1.2% on structured documents, compared to 0.9% for Azure Document Intelligence on printed documents (Microsoft, 2025).
On manuscripts, both tools are close: 3.8% error for Textract versus 4.1% for Azure. This makes AWS a slightly more stable choice for manually filled fields.
Tables, manuscripts, signatures: accuracy and structured formats
Textract offers advanced table detection, with a well-integrated understanding of row/column/cell relationships. Azure recognizes cells but requires post-processing to reconstruct complex structures (Pragmile, 2025).
On signatures, Textract extracts them with their geometric coordinates. Azure has offered a pre-trained model 'prebuilt-signature' since 2024 that is easier to use (Microsoft, 2025).
Multilingual quality: Azure's advantage for European documents
In 2025, Azure supports 12 new European languages, including Dutch, Italian, and Polish. The recognition quality in French is superior to that of AWS, according to several independent benchmarks (Unstract, 2025; G2, 2025).
Textract remains effective in English but sometimes struggles with multi-column documents or mixed formats (scanned PDFs + handwritten OCR).
Latency, scalability, and costs in a distributed environment
Synchronous and batch latency: who can handle the load?
In synchronous processing, AWS Textract shows a speed of 2.1 pages/second, compared to 1.8 pages/second for Azure Document Intelligence. In asynchronous batch processing, AWS reaches 1500 pages/minute, compared to 1200 for Azure (Microsoft, 2025).
If your system relies on real-time processing with low latency, AWS maintains better stability and throughput at scale.
Cost per page: simulation at 10k, 100k, and 1M pages EU-West
In the Europe West region, both services show price parity at low volume: 10,000 pages processed cost $15.
But starting from 100,000 pages, Azure becomes more competitive:
Monthly volume
AWS (Textract)
Azure (Document Intelligence)
10,000
$15
$15
100,000
$90
$85
1,000,000
$600
$580
Annual commitments allow for up to 40% discount on Azure side (Microsoft, 2025).
Discounts and subscription flexibility for large organizations
AWS offers a pay-as-you-go approach with monthly free quotas. It is also possible to use the AWS Pricing Calculator to simulate complex or hybrid scenarios (AWS, 2025).
Azure, on its side, offers a capacity units reservation portal, useful for large batch processes or inter-application pooling. Customers under Enterprise Agreement can directly integrate Azure credits.
If you are still unsure or need specific support, our team is here to help
Summary comparison table AWS vs Azure Document Intelligence
Criteria
AWS Textract
Azure Document Intelligence
OCR Score (printed)
1.2% CER*
0.9% CER*
OCR Score (handwritten)
3.8% CER*
4.1% CER*
Table recognition
Advanced, cell/row/column relationships
Basic, post-processing needed
Signatures
Native extraction with geometry
'prebuilt-signature' model
Multilingual (French)
Good, but limited
Excellent, +12 EU languages added in 2025
Synchronous latency
2.1 pages/sec
1.8 pages/sec
Batch latency
1500 pages/minute
1200 pages/minute
Cost at 100k pages
$90
$85
Volume discounts
By Enterprise negotiation
Up to -40% via reservations
Pre-trained tax models
No
Yes (W-2, 1099, etc.)
Best LLM integration
Comprehend, Bedrock, Step Functions
Azure AI Studio, Logic Apps, Power Automate
Local deployment (EU)
Outposts, Local Zones (Paris)
Azure Stack, sovereign zone France (double encryption)
* The CER stands for Character Error Rate, or character error rate. It is a standard metric to evaluate the quality of OCR (Optical Character Recognition) systems. It measures how faithful the text extracted by the machine is to the actual text, character by character:
-> 0% CER = no errors, perfect recognition.
-> 3% CER = about 3 errors for every 100 characters.
Conclusion: which tool to choose according to your hybrid IT strategy?
In 2025, AWS Textract and Azure Document Intelligence offer two solid visions of cloud OCR. Textract excels in real-time performance, complex table recognition, and native integration into an already deployed AWS ecosystem.
On the other hand, Azure stands out with better multilingual quality, pre-trained models for tax documents, and more competitive pricing at scale. Its native support for sovereign zones in France makes it a relevant choice for organizations sensitive to local governance.
If you operate a multi-cloud AWS + Azure environment, the best reflex is to test both on your real cases (batch vs real-time flow) and optimize your costs through reservations or hybrid orchestrators.
👉 In summary:
Are you processing in real-time and need accuracy on tables? AWS Textract.
Are you handling volume with multilingual or tax documents? Azure Document Intelligence.
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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.
