Transcloud
August 18, 2025
Cloud TCO Breakdown: AWS vs Azure vs GCP — What You’ll Pay for AI & HPC-Ready Infrastructure
In 2025, AI workloads and HPC-ready infrastructure are pushing cloud costs into new territory. Enterprises are no longer just looking at per-second compute pricing or storage tiers—they’re examining total cost of ownership (TCO) across the full lifecycle of infrastructure investments.
This blog breaks down how AWS, Azure, and GCP stack up when it comes to costing AI-optimized, modern infrastructure—covering compute types (especially GPU infrastructure), network egress, storage tiers, and hidden costs like modernization debt and ops automation gaps.
Why TCO Analysis Has Changed in 2025
The rise of AI-native workloads, data-intensive pipelines, and GPU-accelerated computing means that cloud pricing calculators are no longer enough.
Today’s IT leaders need to track:
- Legacy system modernization efforts and their long-term ROI
- The hidden cost of replatforming vs rehosting vs refactoring
- Cost of multi-cloud vs vendor lock-in
- Pricing impacts of carbon-aware computing and energy-optimized regions
Without a true TCO lens, enterprises risk overpaying for underutilized infrastructure, especially when building AI/ML platforms or HPC clusters.
1. Compute Costs: CPU vs GPU vs TPUsProvider GPU Instance (NVIDIA A100 equiv.) On-Demand (per hr) Spot/Preemptible (per hr)
| Cloud Provider | Machine Type | On-Demand Price (per hour) | Spot (per hour) |
|---|---|---|---|
| AWS | p4d.24xlarge | ~$28.97 | ~$5.98 |
| Azure | ND96asr_v4 | ~$27.19 | ~$5.90 |
| GCP | a2-ultragpu-8g | ~$40.11 | ~$11.82 |
GCP’s A100-based instances command the highest hourly rates—both on-demand and preemptible—yet they’re often favored for AI and HPC workloads where raw performance, memory bandwidth, and optimized networking can outweigh cost considerations.
- AWS strikes a strong balance with competitive pricing and a rich ML ecosystem (SageMaker, Bedrock) that can reduce model deployment overhead.
- Azure offers the lowest on-demand pricing among the three and excels in Windows enterprise integration, with tight coupling to Microsoft AI services like Copilot Studio.
- GCP may be pricier, but for teams running large-scale training or inference jobs, its infrastructure can deliver superior throughput—shortening total compute time and potentially lowering overall project costs.
2. Storage Pricing: Cold vs Hot vs AI Training Data
| Storage Type | AWS (S3) | Azure (Blob) | GCP (Cloud Storage) |
| Hot | $0.01/GB | $0.0184/GB | $0.020/GB |
| Cold | $0.002/GB | $0.002/GB | $0.004/GB |
| Archive | $0.00099/GB | $0.00099/GB | $0.0012/GB |
If your AI workloads involve large training datasets, GCP’s coldline storage is often more economical. However, retrieval fees differ, and Azure’s blob lifecycle policies are more automation-friendly for cost optimization.
3. Hidden Costs: Network, Licensing, and Ops Overhead
a. Data Egress Fees
- AWS: ~$0.09/GB
- Azure: ~$0.087/GB
- GCP: ~$0.085/GB (free within same region/zones in some cases)
Cross-region or multi-cloud use cases multiply these costs rapidly, especially when training AI models that ingest real-time multi-source data.
b. License Costs
Azure often includes Windows and SQL Server licensing bundles, which can lower costs if you’re modernizing from Microsoft-based systems. AWS requires separate licensing.
c. Operations and Automation
The cost of cloud operations is often overlooked.
- GCP excels in ops automation (e.g., Autopilot mode for GKE, NetApp Cloud Volumes for HPC)
- Azure offers hybrid orchestration with Azure Arc
- AWS offers Infrastructure as Code depth via CloudFormation, but requires more effort to manage at scale
4. Rehost vs Replatform vs Refactor: TCO Trade-offs
Cloud TCO can vary drastically depending on migration strategy:
| Strategy | Initial Cost | Time to ROI | Ideal For |
| Rehost | Low | Short | Simple VMs |
| Replatform | Moderate | Medium | Databases, Middleware |
| Refactor | High | Long | AI-native, Microservices |
Refactoring legacy applications to be cloud-native (or AI-ready) may cost more upfront but unlocks long-term savings through auto-scaling, serverless architectures, and AI accelerators.
Keywords embedded: Rehost / Replatform / Refactor, legacy system modernization, cloud modernization.
5. Carbon-Aware and Region-Aware Pricing
Modern infrastructure strategies now evaluate energy-efficient zones, especially for AI/HPC which is GPU-intensive and carbon-heavy.
- GCP provides carbon-aware scheduling and location-based emissions data.
- Azure allows energy-aware workload placement with Emissions Impact Dashboard.
- AWS regions like Sweden or Oregon offer lower carbon footprints but limited GPU availability.
This impacts TCO for organizations under ESG mandates or green computing initiatives.
Final Thoughts: Which Provider Wins?
| Factor | Best Option |
| GPU Pricing (Preemptible) | GCP |
| AI/ML Stack Integration | AWS |
| Enterprise + Microsoft Ecosystem | Azure |
| Network Cost Management | GCP |
| Hybrid Infrastructure Orchestration | Azure |
| Carbon-Aware AI Infrastructure | GCP |
The right provider for your AI & HPC-ready infrastructure depends on:
- The depth of AI use cases
- The maturity of your cloud modernization journey
- The balance between capex savings and long-term ROI
A proper cloud TCO analysis requires more than comparing VM prices. It demands context-aware modeling, multi-cloud foresight, and operational discipline to avoid cost creep and unlock true infrastructure transformation.
Want help calculating your AI-ready cloud TCO?
Transcloud’s cloud cost consultants specialize in infrastructure optimization, modernization roadmaps, and multi-cloud architecture assessments.
