AWS vs Azure vs Google Cloud: how the platforms compare

The core services are broadly the same everywhere

Before comparing strengths, it is worth being clear about what does not differ: compute, storage, networking, identity and infrastructure as code exist on all three platforms, doing essentially the same job under different names. A virtual server is Amazon EC2 on AWS, an Azure Virtual Machine on Azure, and a Google Compute Engine instance on Google Cloud. Object storage is Amazon S3, Azure Blob Storage, and Google Cloud Storage. Learn the concept once and mapping it onto a second provider’s naming is a fraction of the effort of learning it the first time.

What genuinely differs between the three is not whether a capability exists, but how mature, integrated or opinionated a provider’s version of it is — and that is where real strengths and weaknesses show up. Our explainers on what Microsoft Azure is and what Google Cloud Platform is cover each provider individually in more depth; this article is about comparing them.

Where each provider’s strengths lie

AWS launched first and has the broadest service catalogue of the three, spanning nearly every category of cloud computing along with a large surrounding ecosystem of documentation, community content and third-party tooling. That breadth and maturity is its defining strength — organisations across almost every sector run workloads on AWS, so AWS skills carry broad optionality.

Azure’s defining strength is its integration with the software many large organisations already run — Windows Server, Microsoft 365, Entra ID and Microsoft’s developer tooling — which gives it particular pull in enterprises, government and hybrid-cloud environments where an organisation keeps some infrastructure on its own premises and some in the cloud. If an organisation is already a Microsoft shop, extending into Azure is often the path of least resistance.

Google Cloud’s defining strengths are data, machine learning and Kubernetes. Google originated Kubernetes and its managed offering, GKE, remains a strong reference implementation; its data and analytics services (led by BigQuery) and its Vertex AI platform for building and deploying machine learning models are consistently cited as the platform’s standout areas, making it a common choice for data-heavy and ML-heavy workloads specifically.

A side-by-side view

A brief, qualitative comparison — strengths and typical users, not numbers:

• AWS — broadest service catalogue and the longest track record; largest surrounding ecosystem of tutorials, tooling and community content; used across almost every sector and company size.
• Azure — deep integration with Microsoft software and identity; strong pull in enterprise, government and hybrid-cloud setups; a natural fit for organisations already invested in Microsoft tooling.
• Google Cloud — particular strength in data and analytics, machine learning, and Kubernetes; often chosen specifically for data-heavy or ML-heavy workloads, and by organisations wanting a strong open-source and container-native posture.

How to choose one

The right platform to learn or adopt depends on context, not a universal ranking — work through these in order: what workload are you actually running or planning to run (a data-and-ML-heavy project points toward Google Cloud’s strengths; a Windows-and-Microsoft-heavy environment points toward Azure; a general-purpose application with no strong pull either way often defaults to AWS for its breadth); what does your existing technology stack already use, since extending an existing platform is usually cheaper than introducing a second one; what skills does your team already have; and what does your target job market ask for, which for individuals is often the deciding factor — search live adverts for your intended role rather than guessing.

If you are choosing for career reasons specifically, our guide on whether to learn more than one cloud platform covers how to sequence a second platform once you have a first one solid, which is usually the better question than trying to pick a single "best" cloud from the outset.

Why we won’t quote market-share figures

You have probably seen this comparison settled elsewhere with precise market-share percentages. We deliberately will not reproduce them here, for the same reasons that apply to any such figure: they typically originate in vendor marketing or surveys with unstated methodology, they are recycled from article to article until they read as settled fact, and they go stale within a quarter of publication. Most importantly, a global aggregate tells you nothing about which platform the employers in your market, or the project in front of you, actually need. All three providers are genuinely excellent at what they do — the "best" one is the one that fits your workload, your stack and your goals, not the one with the biggest number attached to it in someone else’s article.

The multi-cloud reality

Many organisations, particularly larger ones, do not pick a single provider at all — they run different workloads on different clouds deliberately, or end up multi-cloud through acquisitions and historical decisions. That reality is part of why the underlying concepts matter more than any one provider’s console: an engineer fluent in the concepts can work across all three with a comparatively small amount of provider-specific relearning. For the certification angle on all this — which track to sit, and in what order — see our full comparison of AWS, Azure and Google Cloud certifications.