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Tier 1 — Foundational ITHigh Complexity

Buyer's Guide: Cloud Infrastructure & IaaS

A vendor-neutral evaluation framework for selecting hyperscale cloud providers. Covers AWS, Azure, and GCP with weighted scoring, TCO models, and RFP templates.

18 min read 3 vendors evaluated Typical deal: $500K – $5M+ Updated March 2026
Section 1

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.

$679B Global cloud market, 2026 est.
65% Enterprises using 2+ cloud providers
31% Average cloud budget overrun

Section 2

Strategic Importance of Cloud IaaS

IaaS selection impacts speed to market, operational resilience, and cost efficiency. It influences talent acquisition, ecosystem partnerships, and financial flexibility for years to come.

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Strategic Impact
Cloud provider selection directly affects three strategic outcomes: innovation velocity (native AI/ML services differ dramatically), regulatory compliance (sovereign cloud and data residency), and financial predictability (committed-use discounts vs. on-demand spending).

Key trends in 2026 include AI-native infrastructure (GPU/TPU availability), sovereign cloud mandates, FinOps maturity, and sustainability reporting requirements across all three hyperscalers.


Section 3

Build vs. Buy vs. Migrate

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 delivers 30–50% TCO reduction with elastic scaling. ROI typically materializes within 12–18 months.
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.
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Common Pitfall
Do not underestimate egress costs and data gravity. Moving data between clouds or back on-prem can cost 5–15% of annual cloud spend. Plan data placement strategy before migration.

Section 4

Key Capabilities & Evaluation Criteria

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
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Evaluation Tip
Run a real workload POC on each platform — not just synthetic benchmarks. Deploy your actual application stack and measure performance, operational complexity, and true cost under production-like conditions.

Section 5

Vendor Landscape

The hyperscale cloud market is a three-player race with distinct strengths and strategic positioning.

Amazon Web Services (AWS) Leader — Broadest Portfolio

Strengths: 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.

Best for: Enterprises requiring the broadest service catalog and deepest enterprise ecosystem
Microsoft Azure Leader — Enterprise Integration

Strengths: 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.

Best for: Microsoft-centric enterprises and those requiring deep hybrid cloud capabilities
Google Cloud Platform (GCP) Strong Contender — AI/Data

Strengths: 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.

Best for: Data/AI-intensive organizations and those with strong Kubernetes/container adoption
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Market Insight
The cloud market is increasingly differentiated by AI capabilities. AWS leads in breadth, Azure benefits from the OpenAI partnership, and GCP leads in custom AI infrastructure (TPUs). By 2028, AI workload capability may be the primary cloud selection driver.

Section 6

Pricing Models & Cost Structure

Pricing varies significantly by vendor, deployment model, and scale. Understanding the pricing model is critical for accurate budgeting.

Vendor Pricing Model Typical Enterprise Range Key Cost Drivers
AWS On-demand + reserved + savings plans $500K–$5M+ / year Instance type/size, storage volume, data transfer (egress), reserved commitment level, support tier
Azure Pay-as-you-go + reserved + EA $500K–$5M+ / year VM size, hybrid benefit credits, EA commitment level, Azure Consumption Commitment (MACC)
GCP On-demand + committed use + SUDs $300K–$3M+ / year Machine type, sustained-use discounts, committed-use discount level, BigQuery consumption
3-Year TCO Formula
TCO = (Compute + Storage + Networking + Managed Services) × 36 months + Migration + Training + Operations FTE − On-Prem Savings − Productivity Gains

Section 7

Implementation & Migration

Cloud migrations are multi-year transformations that require phased execution and continuous optimization.

Phase 1
Assessment & Planning (Months 1–3)

Inventory workloads, classify migration strategies (6 Rs), design target architecture, establish landing zone, and negotiate commercial agreements.

Phase 2
Foundation Migration (Months 4–9)

Migrate first 20% of workloads (rehost/replatform), establish CI/CD pipelines, implement monitoring/observability, and train operations team.

Phase 3
Application Modernization (Months 10–18)

Refactor key applications for cloud-native, implement auto-scaling, deploy serverless where appropriate, and optimize reserved capacity.

Phase 4
Optimization & FinOps (Months 19–24)

Implement FinOps practices, right-size instances, optimize storage tiers, decommission legacy infrastructure, and establish ongoing governance.


Section 8

Selection Checklist & RFP Questions

Use this checklist during vendor evaluation to ensure comprehensive coverage of critical capabilities.


Section 9

Peer Perspectives

Insights from technology leaders who have completed evaluations and implementations within the past 24 months.

“We went multi-cloud (AWS primary, Azure secondary) and it was the right strategic decision but operationally expensive. My advice: go multi-cloud for resilience, not for negotiating leverage — the operational overhead is real.”
— CIO, Global Financial Services, $50B AUM
“We chose GCP because our data and ML teams were already on BigQuery and TensorFlow. The migration from AWS was painful for 6 months but our data platform costs dropped 40% and our ML deployment velocity tripled.”
— VP Data Engineering, E-Commerce Platform, 500M+ transactions/year
“The biggest mistake was not investing in FinOps from day one. Our cloud bill grew 3x in the first year because teams were provisioning without cost awareness. Implement cost controls before migration, not after.”
— CFO, SaaS Company, $200M ARR

Section 10

Related Resources

Tags:Cloud InfrastructureIaaSAWSAzureGCPCloud MigrationMulti-CloudFinOps