Cloud platform comparison

AWS vs GCP vs Azure

Use this generator to weigh budget, team shape, analytics depth, governance needs, Kubernetes preference, hybrid strategy, and global reach. It does not replace a proof of concept, but it gives you a defensible starting recommendation fast.

AWS: broadest service depth GCP: strong data and Kubernetes posture Azure: enterprise and Microsoft alignment

Tune your priorities

Higher weights push the recommendation more strongly. Scores are normalized to a 100-point scale and rounded to one decimal.

Adds a small provider bonus where the ecosystem is usually strongest.

Helps reflect migration friction and existing identity or tooling habits.

Use total expected spend, not a single project line item.

Larger teams can absorb higher platform complexity more easily.

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Your recommendation

AWS leads for the current settings.

AWS

It balances broad service coverage, strong governance, and global reach for this mix of priorities.

AWS

80.4

Best for broad service depth and large-scale multi-region patterns.

GCP

78.1

Best for data platforms, Kubernetes-native teams, and concise product catalogs.

Azure

79.3

Best for Microsoft estates, governance-heavy shops, and hybrid migration programs.

Decision signal: The top two providers are close, so run a proof of concept around identity, cost visibility, and managed database operations before committing.

Copy-ready summary

AWS tends to win when You want the widest service catalog, mature multi-account patterns, and deep regional coverage for varied workloads.
GCP tends to win when Data engineering, managed analytics, and Kubernetes ergonomics matter more than having the broadest catalog.
Azure tends to win when You already depend on Microsoft identity, productivity, or server tooling and need smoother enterprise alignment.

Planning disclaimer: this tool gives directional guidance only. Real cloud costs depend on architecture, discounts, reserved capacity, data egress, support plans, and operational maturity.

Side-by-side comparison matrix

Use the matrix for a fast qualitative read. The generated score above weights these same themes numerically.

Factor AWS GCP Azure
Breadth of services Usually the deepest catalog across compute, storage, networking, databases, and edge options. More selective catalog, often easier to navigate, especially for teams wanting fewer overlapping choices. Broad enterprise catalog with strong tie-ins to Microsoft products and admin workflows.
Data and analytics Strong end-to-end coverage, but some teams find the product map denser to evaluate. Often attractive for warehouse, analytics, and ML-centered architectures. Good analytics stack, especially where Microsoft BI and data tooling are already standard.
Kubernetes and open source Mature managed Kubernetes and broad ecosystem support. Strong reputation for Kubernetes lineage and cloud-native workflows. Capable managed Kubernetes with increasing enterprise integration value.
Hybrid and migration Solid migration tooling and partner breadth for large transitions. Works well for modernized platforms, though some legacy-heavy estates may need more adaptation. Often a natural fit for hybrid estates tied to Windows Server, Active Directory, and Microsoft licensing.
Governance and enterprise controls Very mature IAM, account segmentation, and policy tooling if you invest in setup discipline. Cleaner in some workflows, but enterprise control patterns may be less familiar to traditional Microsoft shops. Usually strong for centralized governance, enterprise identity, and cross-team policy management.
Global reach Commonly favored for large multi-region and globally distributed designs. Strong backbone and reach, but priorities are often judged more on workload fit than raw catalog scale. Broad reach with especially strong appeal for enterprises standardizing globally on Microsoft tooling.

How it works

  1. The form captures your workload pattern, current stack bias, budget, team size, and six weighted priorities.
  2. Each cloud gets a baseline strength score for pricing, analytics, governance, Kubernetes, hybrid fit, and global reach.
  3. Workload type and stack alignment add small context bonuses, while budget and team size slightly adjust the balance toward simpler or broader platforms.
  4. Weighted totals are normalized to 100, rounded to one decimal place, and turned into a copy-ready recommendation summary.

Assumptions: higher budget and larger teams make broad platforms easier to absorb, while smaller teams or tighter budgets increase the value of simplicity and targeted strengths. Equal scores mean your decision should lean on proof-of-concept results, commercial terms, and staff familiarity.