Cloud comparison · 12 min read

AWS vs Azure vs Google Cloud: which is better for you?

There is no universally best cloud provider. The better choice depends on your existing organisation, workload, team skills, geographic needs, procurement constraints, and the services you will actually operate. A useful comparison starts with the problem, not brand loyalty.

Where AWS is often strong

AWS has a broad service catalogue, mature ecosystem, and many deployment patterns. It can be a good fit when a team needs a wide range of managed services, flexible infrastructure primitives, or a large pool of existing learning material and hiring experience.

Breadth also brings choice. Teams need conventions for accounts, networking, identity, tagging, and cost control so a large catalogue does not become operational sprawl.

Where Azure is often strong

Azure is often attractive for organisations already using Microsoft identity, Windows workloads, Microsoft 365, .NET, or enterprise agreements. Its resource group and subscription model can map naturally to established enterprise ownership structures.

It is not automatically the right answer for every Microsoft-using team. Assess the exact services, regions, governance needs, and skills that the workload requires.

Where Google Cloud is often strong

Google Cloud is frequently considered for data analytics, machine learning, Kubernetes, and teams already invested in Google’s data platforms. It can be an excellent fit when those capabilities are central to the product.

A platform is more than its standout services. Validate identity integration, networking, support model, regional availability, and the team’s ability to operate it day to day.

Put it into practice

Write a one-page decision for a hypothetical web application. Include existing company tools, required services, expected skills, cost drivers, and the reason you did not choose the other two providers.

  1. List your existing identity, data, and operating constraints.
  2. Compare the managed services needed by the workload.
  3. Estimate network, storage, and support costs—not only compute.
  4. Run a small proof of concept before committing a platform.