Executive Summary
Cloud IaaS is not a technology decision — it is a business-model decision that shapes how an enterprise innovates, scales, and competes.
Cloud Infrastructure as a Service (IaaS) represents the foundational layer of modern enterprise IT. The choice between AWS, Microsoft Azure, and Google Cloud Platform influences everything from application architecture to talent strategy.
This guide provides a vendor-neutral evaluation framework with weighted scoring, 3-year TCO models, RFP templates, and phased implementation timelines for enterprise procurement teams.
Strategic Importance of Cloud IaaS
Cloud Infrastructure & IaaS decisions matter because they profoundly impact innovation velocity, sovereignty, and financial predictability. A poorly chosen cloud becomes the gravitational center for engineering culture, affecting IAM models, proprietary data services, and analytics pipelines. This choice, co-owned by CIOs, CFOs, CISOs, and data-protection officers, now fuses with AI strategy, with GPU and accelerator supply (Nvidia, AWS Trainium/Graviton, Google TPUs/Axion, Microsoft Maia/Cobalt) being a critical constraint.
IaaS selection impacts speed to market, operational resilience, and cost efficiency. It influences talent acquisition, ecosystem partnerships, and financial flexibility for years to come.
Key trends in 2026 include AI-native infrastructure (GPU/TPU availability), sovereign cloud mandates, FinOps maturity, and sustainability reporting requirements across all three hyperscalers.
Should you build or buy Cloud Infrastructure & IaaS?
Deciding whether to build or buy in cloud infrastructure means determining how deep to buy, workload by workload. While racking your own servers is rarely the question, the real decision is where on the spectrum each workload should sit, balancing portability with the benefits of managed databases, serverless, and AI platforms. Stable systems reward portability, while innovation-led products reward going deep on one provider’s managed services, accepting lock-in for speed.
Before evaluating cloud providers, determine your cloud strategy posture.
| Scenario | Recommendation | Rationale |
|---|---|---|
| Aging on-prem infrastructure with rising maintenance costs | Migrate to Cloud | Cloud migration can deliver meaningful TCO reduction through elastic scaling. ROI typically materializes over the first year or two. |
| Existing cloud commitment with optimization opportunities | Optimize & Expand | Leverage reserved instances, savings plans, and FinOps practices before considering multi-cloud complexity. |
| Regulatory requirements mandating data sovereignty | Evaluate Sovereign Cloud | All three hyperscalers now offer sovereign cloud regions. Assess compliance-specific capabilities carefully. |
| AI/ML GPU workloads requiring specialized compute | Evaluate GPU Availability | GPU supply constraints make cloud provider choice critical for AI workloads. Evaluate reserved GPU capacity and pricing. |
| Stable, predictable workloads with no scaling needs | Assess TCO Carefully | For truly static workloads, on-prem or colocation may offer lower long-term costs. Run a rigorous 3-year TCO comparison. |
How do you evaluate Cloud Infrastructure & IaaS?
To evaluate Cloud Infrastructure & IaaS, weigh key capabilities like Compute, AI Silicon & Scaling (25%), Networking & Data Egress (20%), and Storage & Data Services (20%) against your specific workload mix. Also consider Security, Compliance & Sovereignty (15%), Management, Operations & FinOps (10%), and Ecosystem, Support & Commercial Fit (10%). Prioritize explicit trade-offs between capability depth, portability, cost predictability, and jurisdiction.
Use the following weighted evaluation framework to assess vendors across the dimensions that matter most to your organization.
| Capability Domain | Weight | What to Evaluate |
|---|---|---|
| Compute & Scaling | 25% | Instance variety, auto-scaling, spot/preemptible pricing, GPU/TPU availability, serverless compute options |
| Networking & Security | 20% | VPC architecture, DDoS protection, private connectivity, zero-trust networking, compliance certifications |
| Storage & Data Services | 20% | Object/block/file storage, data lake integration, backup/DR, cross-region replication |
| AI/ML & Analytics | 15% | Managed ML services, GPU availability, data warehouse integration, AI platform maturity |
| Management & Operations | 10% | Console UX, IaC support (Terraform, Pulumi), monitoring, cost management tools |
| Ecosystem & Support | 10% | Partner network, marketplace, training resources, enterprise support tiers, SLA guarantees |
Which vendors lead in Cloud Infrastructure & IaaS?
Consider AWS, Microsoft Azure, and Google Cloud for broad catalogs and AI capabilities, which together take roughly two-thirds of global cloud infrastructure spend. Niche players like Oracle Cloud Infrastructure offer aggressive egress economics, IBM Cloud focuses on regulated hybrid and HashiCorp tools, Alibaba Cloud targets Asia, and DigitalOcean provides flat-rate simplicity. Most shortlists compare a hyperscaler primary with a value or sovereign provider like OVHcloud for specific workloads.
| Vendor | Positioning | Best for |
|---|---|---|
| Amazon Web Services (AWS) | Leader — Broadest Portfolio | Enterprises requiring the broadest service catalog and deepest enterprise ecosystem |
| Microsoft Azure | Leader — Enterprise Integration | Microsoft-centric enterprises and those requiring deep hybrid cloud capabilities |
| Google Cloud Platform (GCP) | Strong Contender — AI/Data | Data/AI-intensive organizations and those with strong Kubernetes/container adoption |
The hyperscale cloud market is a three-player race with distinct strengths and strategic positioning.
Amazon Web Services (AWS)
Leader — Broadest PortfolioStrengths: Largest service portfolio (200+ services), deepest enterprise adoption, strongest marketplace and partner ecosystem, and most mature operational tooling. Considerations: Pricing complexity; console UX fragmented across services; AI/ML capabilities strong but less integrated than GCP; vendor lock-in risk with proprietary services.
Microsoft Azure
Leader — Enterprise IntegrationStrengths: Deepest Microsoft 365/Dynamics integration, strongest hybrid cloud (Azure Arc), enterprise licensing advantages (EA discounts), and rapidly growing AI capabilities (OpenAI partnership). Considerations: Service reliability has lagged AWS historically; some services less mature; console experience inconsistent; pricing tied to complex EA agreements.
Google Cloud Platform (GCP)
Strong Contender — AI/DataStrengths: Best-in-class data and AI services (BigQuery, Vertex AI, TPUs), strongest Kubernetes (GKE), excellent network performance, and competitive pricing. Considerations: Smaller enterprise market share; fewer services than AWS; enterprise support and partner ecosystem less mature; concerns about Google commitment to enterprise.
How much should you budget for Cloud Infrastructure & IaaS?
Budgeting for cloud infrastructure requires modeling three years of total cost, not just monthly compute. The real cost is driven by data transfer (egress and inter-region), support tiers (often a percentage of spend), idle resources, and commitment levels. Deep discounts are available for three-year commitments with AWS, Azure, and Google Cloud, while OCI and DigitalOcean offer free or bundled egress.
Pricing varies significantly by vendor, deployment model, and scale. Understanding the pricing model is critical for accurate budgeting.
| Vendor | Pricing Model | Relative Cost Tier | Key Cost Drivers |
|---|---|---|---|
| AWS | On-demand + reserved + savings plans | Premium | Instance type/size, storage volume, data transfer (egress), reserved commitment level, support tier |
| Azure | Pay-as-you-go + reserved + EA | Premium | VM size, hybrid benefit credits, EA commitment level, Azure Consumption Commitment (MACC) |
| GCP | On-demand + committed use + SUDs | Premium | Machine type, sustained-use discounts, committed-use discount level, BigQuery consumption |
How long does implementation take for Cloud Infrastructure & IaaS?
Cloud infrastructure implementation is a multi-year transformation, typically spanning 19-24 months for initial optimization and governance. The process involves assessing and designing for 1-3 months, establishing a foundation and migrating a first wave for 4-9 months, and modernizing applications for 10-18 months. Key predictable challenges include building a governed foundation and migrating data efficiently.
Cloud migrations are multi-year transformations that require phased execution and continuous optimization.
Inventory workloads, classify migration strategies (6 Rs), design target architecture, establish landing zone, and negotiate commercial agreements.
Migrate first 20% of workloads (rehost/replatform), establish CI/CD pipelines, implement monitoring/observability, and train operations team.
Refactor key applications for cloud-native, implement auto-scaling, deploy serverless where appropriate, and optimize reserved capacity.
Implement FinOps practices, right-size instances, optimize storage tiers, decommission legacy infrastructure, and establish ongoing governance.
What should you ask vendors about Cloud Infrastructure & IaaS?
Use this checklist during vendor evaluation to ensure comprehensive coverage of critical capabilities.
Frequently asked questions about Cloud Infrastructure & IaaS
When should we consider Oracle Cloud Infrastructure (OCI) over AWS or Azure, especially given OCI’s free egress?
OCI is a strong contender for Oracle Database and applications customers, and for cost- and egress-sensitive workloads. Its free outbound data transfer across commercial regions can significantly change the math compared to the per-GB egress pricing of hyperscalers like AWS and Azure, making it ideal for data-egress-heavy or multicloud-by-design architectures.
Our team is small and focused on shipping a web app. Is a hyperscaler like AWS or Azure overkill, and what are the alternatives?
Yes, for lean teams shipping web apps or SaaS that value simplicity, a developer-first cloud like DigitalOcean is often a better starting point. It offers flat, predictable pricing and a smaller service catalog, avoiding the complexity of 200+ services from hyperscalers until scale, compliance, or AI depth truly demand it.
We’re seeing high data transfer costs in our current cloud setup. Which vendors specifically address this, and how?
Data transfer and inter-region charges can dominate the bill. Oracle Cloud Infrastructure (OCI) directly addresses this with its free outbound data transfer across commercial regions. DigitalOcean also offers bundled data-transfer allowances, which can significantly alter the economics compared to per-GB egress pricing from other hyperscalers.
Our organization has significant existing Microsoft licenses. How does this impact our choice between Azure and other providers like GCP?
For Microsoft-centric estates, Azure is a natural choice due to deep Microsoft 365, Entra, and Dynamics integration, strong hybrid capabilities via Azure Arc, and enterprise-agreement leverage that can bundle cloud into existing licenses. This can offer commercial advantages not available with GCP, which has a smaller enterprise share and different commercial terms.