Best Cloud Extraction APIs for Developers 2026
A head-to-head comparison of cloud-native document extraction APIs. Accuracy, pricing, and developer experience.
Alex Rivera·Updated March 2026Evaluation Criteria
- 1. Raw extraction accuracy on standardized benchmark
- 2. API documentation quality and SDKs
- 3. Pricing transparency and predictability
- 4. Pre-built vs. custom model availability
- 5. Latency and throughput at scale
Google Document AI
Google's cloud-native document processing. Top-tier ML models, but you'll need a GCP team to use them.
Best raw accuracy from Google's foundation models
Lido
Template-free extraction that works out of the box. No training, no setup, no engineering required.
Highest accuracy with the simplest API integration
Azure Document Intelligence
Microsoft's document AI service. Best for Azure-native shops with Power Platform workflows.
Best choice for Microsoft-stack shops
Amazon Textract
AWS's extraction service. Solid accuracy, infinite scale, but API-only and billing surprises.
Cheapest per-page, infinite AWS scalability
Nanonets
Trainable extraction models with a visual interface. Good once trained, slow to get started.
Visual model training adds flexibility
Score Comparison
| Metric | Google Document AI | Lido | Azure Document Intelligence | Amazon Textract | Nanonets |
|---|---|---|---|---|---|
| Extraction Accuracy | 9.2 | 9.3 | 8.0 | 8.0 | 8.0 |
| Ease of Use | 6.5 | 9.5 | 6.5 | 5.5 | 7.5 |
| Value for Money | 7.5 | 8.8 | 7.0 | 7.5 | 7.8 |
| Integrations | 9.0 | 8.5 | 9.0 | 8.5 | 8.0 |
| Scalability | 9.5 | 9.0 | 8.0 | 9.5 | 7.5 |
| Support & Docs | 7.0 | 8.8 | 7.0 | 5.5 | 7.5 |
| Overall | 8.8 | 9.1 | 7.6 | 7.4 | 7.9 |