AWS vs Azure vs Google Cloud: Choosing a Hyperscaler

These three platforms overlap enough that most teams could build the same system on any of them. The choice is rarely decided by raw capability. It is decided by what your team already knows, what your organisation already runs, and which workloads sit at the centre of your product.

Side by side

AWS: the comprehensive default

Amazon Web Services offers more than 150 services and the broadest global data centre network. For developers, that breadth means you can build almost anything without leaving the ecosystem. For businesses, it means scaling from a single server to a global deployment without changing platform.

The cost is complexity. AWS pricing is granular and hard to predict without careful monitoring, the learning curve is steep, and support is an extra line item. Its free tier has also changed: new accounts get up to $200 in credits for six months rather than the twelve-month free tier people still expect. AWS is the right default when you need range and are willing to invest in understanding the platform.

Azure: the Microsoft-shaped choice

Azure is the natural fit for organisations already invested in Microsoft. Active Directory integration, Windows Server support and hybrid capability through Azure Arc make it strong for enterprises with meaningful on-premises Microsoft infrastructure. Its free account includes a $200 credit for 30 days plus twelve months of free access to popular services.

The difficulty is pricing and licensing complexity, which can make cost management genuinely hard without planning. If your identity layer is already Active Directory, that integration usually outweighs it.

Google Cloud: data and AI strength

Google Cloud excels where data is the product. BigQuery, Vertex AI and Google Kubernetes Engine are strong tools for processing large datasets and building AI-driven applications, and GKE in particular is widely regarded as the most mature managed Kubernetes offering. The free trial is the most generous of the three at $300 over 90 days, with an always-free e2-micro instance behind it.

Two cautions. Pricing is complex, in the same way AWS pricing is complex. And Google has a track record of discontinuing services, which is a real consideration when you are picking a platform to build on for a decade.

How to actually decide

• Already running Microsoft infrastructure? Azure, almost always. The identity and licensing integration is the deciding factor.
• Is large-scale data or machine learning the core of the product? Google Cloud, for BigQuery and Vertex AI.
• Do you need the widest possible service range, or hiring depth? AWS, for catalogue breadth and the largest pool of people who already know it.
• None of the above clearly applies? Pick the one your team already knows. Platform familiarity will save you more than any pricing difference between the three.

A note on cost comparison

We do not publish head-to-head price figures for these three, because a like-for-like comparison depends on instance family, region, commitment term, data transfer and which managed services you use. A number that ignores those is misleading. Price your actual workload in each provider’s own calculator, and include egress, which is where cloud bills most often surprise people.

If budget is the binding constraint

If what you need is a virtual machine rather than a platform, none of these three is the cheapest route. Compare the developer-focused providers instead, where flat pricing and higher specs per unit cost change the picture considerably.