December 2025 Update: AWS cut H100 prices 44% in June 2025 (p5 instances now ~$50-55/hr range vs. prior ~$98/hr). H100 purchase prices stabilized at $25-40K, shifting break-even to 12-18 months versus 7-11 months previously. Budget cloud providers like Hyperbolic ($1.49/hr H100) and Lambda Labs further compress the repatriation calculus. Cloud now makes more sense below 60-70% utilization. However, Blackwell allocation constraints and rising on-premise expertise availability still favor owned infrastructure for high-utilization AI-native organizations.
A biotechnology company's AWS bill for GPU instances reached $3.2 million annually before they discovered that building equivalent on-premise infrastructure would cost $3.8 million once but save $12 million over three years.¹ The cloud repatriation movement gains momentum as organizations discover AWS p5.48xlarge instances at $98.32 per hour cost more in 4 months than purchasing the hardware outright.² Data egress fees compound the problem: moving a 500TB training dataset out of AWS costs $23,000 in transfer charges alone, creating a financial barrier that locks organizations into increasingly expensive cloud consumption.³
AWS excels at elastic scaling and rapid experimentation, but the economics break down for sustained GPU workloads running 24/7. Organizations report average cost reductions of 65% after migrating from AWS to on-premise infrastructure, with payback periods under 18 months.⁴ The migration complexity deters many teams who fear service disruption, data loss, or the technical challenges of managing their own infrastructure. Yet those who successfully navigate the transition gain not just cost savings but also performance improvements, complete data control, and freedom from vendor lock-in that constrains innovation.
The economics driving cloud repatriation
AWS GPU pricing creates sticker shock when organizations scale beyond experimentation. A single p5.48xlarge instance with 8 H100 GPUs costs $98.32 per hour on-demand or $58.99 with one-year reserved instances.⁵ Running continuously for a year accumulates $516,763 in charges for compute alone. Storage, networking, and support fees push annual costs past $600,000 per instance. A modest 10-instance deployment burns through $6 million annually.
On-premise infrastructure requires substantial capital investment but delivers superior long-term economics. Building a 10-node cluster with 80 H100 GPUs costs approximately: - GPU hardware: $2,400,000 (80 GPUs × $30,000) - Servers and networking: $500,000 - Power and cooling infrastructure: $400,000 - Installation and setup: $200,000 - Total capital expense: $3,500,000
The on-premise investment pays for itself in 7 months compared to on-demand pricing or 11 months versus reserved instances. After breakeven, organizations save $500,000 monthly. The five-year total cost of ownership shows on-premise infrastructure costing $5.2 million versus $30 million for equivalent AWS consumption.⁶
Hidden AWS costs accelerate repatriation decisions. Data transfer fees reach $0.09 per GB for egress exceeding 10TB monthly.⁷ NAT gateway charges accumulate $0.045 per GB processed. Elastic IP addresses, snapshots, and monitoring add thousands monthly. Organizations discover their "simple" GPU deployment generates 40% additional charges beyond compute costs.
Planning the migration journey
Successful migration requires methodical planning spanning 3-6 months before execution begins. Start by analyzing existing AWS usage patterns to understand actual requirements versus provisioned capacity. CloudWatch metrics reveal true GPU utilization often sits below 60% due to overprovisioning.⁸ Right-sizing on-premise infrastructure based on actual usage rather than peak capacity reduces capital requirements by 30-40%.
Workload assessment identifies migration candidates and dependencies. Training workloads with predictable resource consumption migrate easily. Inference workloads with variable traffic patterns may benefit from hybrid approaches. Development environments can move first as proof of concept. Production systems require careful staging to prevent disruption.
Data inventory cataloging prevents expensive surprises. Organizations often discover petabytes of accumulated data in S3, with 70% being obsolete experiments or redundant backups.⁹ Cleaning data before migration reduces transfer time and costs. Identifying cold data for archival saves active storage requirements. Understanding data relationships prevents breaking dependencies during migration.
Network architecture planning ensures connectivity between on-premise infrastructure and remaining AWS services. AWS Direct Connect provides dedicated bandwidth for hybrid operations, costing $0.30 per hour plus port fees.¹⁰ Virtual Private Gateways enable secure VPN connections as backup paths. Transit Gateway simplifies complex multi-region architectures. Plan for 6-12 week lead times for Direct Connect provisioning.
Technical migration execution
Migration execution follows a systematic approach minimizing risk and downtime:
Phase 1: Infrastructure Preparation (Weeks 1-4) Build on-premise GPU infrastructure while maintaining AWS operations. Install servers, configure networking, and validate cooling capacity. Deploy base operating systems and container orchestration platforms. Establish monitoring and logging systems. Create automation scripts for provisioning and configuration. Test infrastructure with synthetic workloads before migration.
Phase 2: Parallel Operations (Weeks 5-8) Establish hybrid connectivity between AWS and on-premise infrastructure. Replicate development and testing environments on-premise. Validate application functionality in the new environment. Benchmark performance to ensure expectations are met. Train operations team on new infrastructure management. Document procedures and troubleshooting guides.
Phase 3: Data Migration (Weeks 9-12) Execute data transfer using optimal methods for dataset sizes. AWS DataSync handles datasets up to 100TB efficiently at $0.0125 per GB.¹¹ AWS Snowball Edge devices transfer petabyte-scale data for $300 per device plus shipping.¹² Direct network transfer works for smaller datasets under 10TB. Implement incremental synchronization to minimize cutover downtime.
Phase 4: Workload Migration (Weeks 13-16) Migrate workloads in priority order starting with non-critical systems. Use blue-green deployment strategies to enable instant rollback. Validate each workload thoroughly before proceeding. Implement canary deployments for production systems. Monitor performance metrics continuously during migration. Maintain AWS infrastructure as fallback until stability is confirmed.
Phase 5: Decommissioning (Weeks 17-20) Gradually reduce AWS footprint as confidence builds. Archive compliance data before deletion. Terminate unnecessary instances and services. Cancel reserved instances or sell remaining terms on AWS Marketplace. Remove Direct Connect circuits if no longer needed. Document final architecture and lessons learned.
Data egress strategies minimize transfer costs
AWS data egress fees create the largest variable cost during migration. Strategic approaches reduce expenses significantly:
Compression and Deduplication: Compress datasets before transfer to reduce volume by 50-70%. Remove duplicate files and obsolete experiments. Use incremental transfers for datasets with minor changes. Archive cold data to Glacier for long-term retention at $0.004 per GB monthly rather than migrating.¹³
AWS DataSync Optimization: Configure DataSync with bandwidth throttling to avoid network saturation. Use scheduling to transfer during off-peak hours when egress rates may be lower. Enable compression and integrity verification. Expect transfer rates of 100-200 Mbps per task depending on file sizes and network conditions.
Snowball Edge for Large Datasets: Order multiple Snowball Edge devices for parallel transfer of petabyte-scale data. Each device holds 80TB and costs $300 plus shipping. Transfer speeds reach 1Gbps when properly configured. The service bypasses network egress fees entirely, saving tens of thousands on large migrations.
Direct Connect Strategic Usage: Establish Direct Connect for the migration period then downgrade or terminate afterward. The monthly port fee of $3,600 for 10Gbps pays for itself by avoiding egress fees on just 40TB of data transfer.¹⁴ Virtual interfaces allow multiple transfers simultaneously.
Introl assists organizations migrating from cloud to on-premise infrastructure across our global coverage area, with expertise managing over 100,000 GPU deployments.¹⁵ Our migration specialists have moved petabytes of AI training data while minimizing egress costs and ensuring zero data loss.
Application and service migration considerations
Migrating applications requires addressing AWS service dependencies:
S3 Replacement: Implement MinIO or Ceph for S3-compatible object storage on-premise. MinIO provides identical APIs enabling code reuse without modification.¹⁶ Performance often improves due to locality and dedicated resources. Cost per TB drops from $23 monthly on S3 to under $2 for on-premise storage.
Container Orchestration: Replace EKS with vanilla Kubernetes or alternatives like K3s for lightweight deployments. Import existing pod specifications with minimal changes. Implement Prometheus and Grafana for monitoring replacement of CloudWatch. Deploy Harbor or Nexus for container registry replacement of ECR.
Database Migration: Migrate RDS databases to self-managed instances or consider PostgreSQL/MySQL on Kubernetes. Use AWS Database Migration Service for initial synchronization.¹⁷ Implement automated backups and high availability configurations. Consider managed database services from vendors like Percona or MariaDB.
Load Balancing and Ingress: Replace ALB/NLB with HAProxy, NGINX, or Traefik for load balancing. Implement cert-manager for SSL certificate automation. Configure DNS failover for high availability. Monitor with open-source tools replacing AWS-specific services.
Risk mitigation strategies
Migration risks require proactive mitigation:
Rollback Planning: Maintain AWS infrastructure for 30-90 days post-migration as safety net. Document rollback procedures for each component. Test rollback processes during maintenance windows. Keep data synchronization scripts ready for reverse migration if needed.
Skill Gap Management: Train existing team on on-premise infrastructure management before migration. Hire specialists for critical areas like GPU cluster administration. Partner with vendors for support during transition period. Create knowledge base documenting common issues and solutions.
Performance Validation: Benchmark all workloads before and after migration. Set acceptable performance thresholds triggering investigation. Monitor latency, throughput, and error rates continuously. Implement automated alerts for degradation detection.
Compliance Maintenance: Ensure on-premise infrastructure meets regulatory requirements. Implement encryption at rest and in transit. Configure audit logging and retention policies. Conduct security assessments before production migration.
Real-world migration success stories
Genomics Research Institute: Migrated 800 V100 GPUs from AWS to on-premise, reducing annual costs from $8.4 million to $2.1 million after factoring operational expenses. The migration took 4 months and included 2PB of genomic data. Performance improved by 35% due to optimized networking and storage placement. ROI achieved in 14 months.
Autonomous Vehicle Startup: Moved simulation workloads from 200 AWS instances to on-premise cluster with 400 A100 GPUs. Monthly costs dropped from $680,000 to $95,000 (power, cooling, maintenance). Data locality eliminated S3 access latency, improving simulation speed by 2.4x. The company saved $7 million in the first year alone.
Financial Trading Firm: Migrated ML training infrastructure from AWS to colocation facility near exchanges. Reduced model training costs by 70% while achieving 10x lower inference latency. The $4.5 million infrastructure investment paid back in 11 months. Eliminated $180,000 annual egress fees for market data access.
Post-migration optimization opportunities
On-premise infrastructure enables optimizations impossible in cloud environments:
Hardware Customization: Overclock GPUs for 15-20% performance gains. Optimize cooling for specific workload thermal profiles. Upgrade components incrementally rather than replacing entire instances. Install specialized accelerators without cloud provider constraints.
Network Optimization: Implement InfiniBand for superior GPU-to-GPU communication. Configure RDMA for ultra-low latency operations. Optimize topology for specific communication patterns. Eliminate virtualization overhead present in cloud deployments.
Storage Tiering: Deploy NVMe for hot data, SAS SSD for warm, and HDD for cold storage. Implement caching layers optimized for access patterns. Use parallel filesystems for maximum throughput. Eliminate per-GB storage costs plaguing cloud deployments.
Power Efficiency: Implement dynamic voltage and frequency scaling. Use liquid cooling to reduce PUE below 1.2. Participate in demand response programs for additional revenue. Optimize for regional electricity rate structures.
Organizations successfully migrating from AWS to on-premise GPU infrastructure achieve transformational cost reductions while gaining complete control over their AI destiny. The migration complexity requires careful planning and execution, but the financial returns justify the effort. Companies that master their own infrastructure free themselves from vendor lock-in, eliminate egress fees, and build competitive advantages through optimized deployments impossible in public clouds. The cloud repatriation trend will accelerate as more organizations discover that owning GPU infrastructure delivers superior economics for sustained AI workloads.
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