Choosing Between AWS, Google Cloud and Azure: A Practical Framework

Comparisons of cloud providers often focus on feature checklists. In practice, all three major hyperscalers can run almost any workload well. The better question is which platform best fits your organisation's workloads, skills, existing agreements and future direction.

1. Start with workloads, not vendors

List the systems you intend to run and group them by type: web applications, data and analytics, machine learning, Windows and .NET estates, or latency-sensitive services. Each provider has areas of particular strength, and the shape of your portfolio usually points toward a natural primary platform.

• Heavy Microsoft licensing, Active Directory and .NET workloads often align naturally with Azure.
• Analytics-centred estates frequently benefit from BigQuery and the wider Google Cloud data ecosystem.
• Organisations wanting the broadest catalogue of managed services and a large talent pool often choose AWS.

2. Weigh your team's skills honestly

The cost of a platform includes the time it takes your engineers to operate it well. Existing experience, certifications and hiring markets matter. A technically superior service operated poorly will underperform a familiar one operated expertly.

3. Model cost realistically

List prices rarely decide the outcome. Commitment discounts, enterprise agreements, data egress, support plans and the engineering effort required for each architecture typically have a much larger impact. Build a simple model for your top workloads and include operational effort.

4. Consider data gravity and compliance

Where your data lives today, data residency requirements and the regions each provider offers in your markets can narrow the choice quickly. Moving large datasets later is expensive, so place data-heavy workloads thoughtfully.

5. Decide, document and revisit

Record the decision and its reasoning in an architecture decision record. Revisit it when your workloads, commercial position or the market changes significantly. Infrastructure as code and container-based platforms keep future options open without designing for the lowest common denominator.