Cost Comparison of Clinical AI Training Platforms
Clinical AI training costs vary by architecture, not just by GPU hours.
Cost breakdown per platform
Rapha Protocol (Compute-to-Data)
- Training fee: Per-job USDC escrow. You set your budget per training run. No hourly GPU pricing. No data transfer costs.
- Infrastructure: Zero. Rapha deploys and operates the edge appliance at the hospital. No cloud instances to provision. No GPUs to rent.
- Legal/contracting: Institutional governance approval (varies). No per-job DUA negotiation. No BAA overhead (you never receive PHI).
- Engineering: Zero FL coordination cost. No gradient leakage mitigation engineering. Submit your model, receive trained weights.
Cloud BAA (AWS/GCP/Azure)
- Compute: GPU instances at ~$2-5/hour for medical imaging workloads. LLM fine-tuning: $10-30/hour.
- Storage: PHI at rest in cloud storage. Ongoing cost scales with data volume.
- Data transfer: Egress from hospital to cloud. Ingress for model artifacts. Ongoing cost per GB.
- Legal: BAA, DPA negotiation, DPIA. One-time but substantial legal cost.
- Security: Cloud security configuration, IAM, encryption key management. Engineering overhead ongoing.
Total cost of ownership for a typical radiology AI project (6 months)
Rapha Protocol: $25,000-$250,000 (10-50 training jobs at $2,500-$5,000 each)
Cloud BAA: $150,000-$500,000+ (GPUs + storage + legal + engineering)
Federated learning: $200,000-$750,000+ (FL platform licensing + engineering + coordination)