Processing 10,000 scanned document pages through Google Document AI costs approximately $150 at their standard tier. The same 10,000 pages processed with Surya OCR on a dedicated RTX 5090 costs $2.60 in amortised GPU time — a 98% reduction. For document-heavy industries like legal, healthcare, and finance, OCR processing cost determines whether AI-powered document workflows are viable at scale.
What Makes OCR Expensive?
Modern OCR goes beyond simple character recognition. Production pipelines include layout detection, table extraction, handwriting recognition, and post-processing with an LLM for structured output. Each layer adds compute time and cost. API providers charge per page regardless of complexity, while self-hosted solutions let you optimise each stage independently. Use the OCR cost calculator to estimate your specific workload.
OCR Cost per 10,000 Pages by GPU
| OCR Engine | GPU | Pages/Hour | Cost per 10K Pages | Accuracy (printed) |
|---|---|---|---|---|
| Surya OCR | RTX 5090 | 3,800 | $0.40 | 97.2% |
| Surya OCR | RTX 6000 Pro 96 GB | 5,200 | $0.80 | 97.2% |
| PaddleOCR | RTX 5090 | 4,500 | $0.34 | 96.1% |
| PaddleOCR | RTX 3090 | 3,200 | $0.38 | 96.1% |
| DocTR | RTX 5090 | 2,800 | $0.55 | 95.8% |
| EasyOCR | RTX 5090 | 2,100 | $0.73 | 93.5% |
| Google Document AI | N/A (API) | N/A | $150.00 | 98.1% |
| AWS Textract | N/A (API) | N/A | $130.00 | 97.5% |
Self-hosted costs at GigaGPU dedicated rates. Accuracy measured on standard English printed document benchmarks.
Adding LLM Post-Processing to the Pipeline
Raw OCR output often needs correction and structuring. Running extracted text through a 7B LLM for error correction and JSON formatting adds approximately $0.15 per 10,000 pages on a shared GPU. This combination — fast GPU OCR plus LLM post-processing — achieves accuracy comparable to premium API services at a fraction of the cost. Deploy the LLM layer with vLLM hosting for maximum throughput on the same server.
The total self-hosted pipeline cost (OCR + LLM correction) for 10,000 pages: approximately $0.55 on an RTX 5090. That is 99.6% cheaper than Google Document AI alone.
Scaling to 1 Million Pages per Month
| Scale | Self-Hosted (RTX 5090) | Google Document AI | Savings |
|---|---|---|---|
| 10,000 pages/month | $180 (full GPU rental) | $150 | -$30 (API cheaper) |
| 50,000 pages/month | $180 | $750 | $570 |
| 200,000 pages/month | $180 | $3,000 | $2,820 |
| 1,000,000 pages/month | $360 (2x RTX 5090) | $15,000 | $14,640 |
The break-even point is around 12,000 pages per month. Above that, self-hosting saves more with every additional page. The key insight: dedicated GPU cost is fixed, so marginal cost per page approaches zero.
GPU Selection for OCR Workloads
OCR models are relatively small — most fit comfortably in 8-12GB VRAM. This makes the cheapest available GPU the right choice for pure OCR. An RTX 5090 is often over-provisioned for OCR alone, but becomes perfectly utilised when you add LLM post-processing, table extraction, and layout analysis to the same GPU. For pure batch OCR at extreme volume, an RTX 3090 offers the best cost-per-page ratio.
Start Processing Documents on GigaGPU
Eliminate per-page OCR charges by moving to GigaGPU dedicated GPU hosting. Process unlimited documents at a flat monthly rate with full control over your OCR pipeline. Run Surya, PaddleOCR, or any open-source engine alongside an LLM for structured extraction.
Estimate your savings with the GPU vs API cost comparison tool, or browse open-source hosting options for combined OCR and LLM deployments. For regulated document processing, private AI hosting ensures your data stays on-premises. More cost breakdowns on the cost blog.
