For the majority of workloads, all three large cloud platforms will do the job. Virtual machines, object storage, managed databases, load balancers and container orchestration exist on each, and the differences in raw capability are smaller than the marketing suggests.
Where they genuinely diverge is at the edges: the data stack, the machine learning tooling, and how well the platform fits an organisation’s existing systems. Those are the differences worth choosing on.
General compute and storage
This is the commodity layer, and it is close to a tie.
- AWS has the widest service catalogue and the deepest set of instance types. If a specialised capability exists anywhere, it usually exists here first.
- Google Cloud has a cleaner resource model and per-second billing that suits bursty workloads. Sustained use discounts apply automatically rather than requiring commitment planning.
- Azure is competitive on compute and unusually strong if you are already running Windows Server or SQL Server, where existing licensing can carry across.
If your workload is a web application with a database behind it, this layer will not decide anything for you. Pick on price and on which console your team already knows.
Data and analytics
Here the platforms separate meaningfully.
Google Cloud is the strongest of the three if analytics is central to what you do. BigQuery separates storage from compute in a way that makes large ad-hoc queries straightforward, and it requires very little operational management.
AWS offers more assembly required and more control. Redshift, Athena, Glue and EMR cover the same ground, but you are composing a stack rather than adopting one.
Azure fits organisations already invested in the Microsoft data ecosystem, where Synapse and Power BI integration removes real friction.
Machine learning and AI
| Platform | Managed model service | Best fit |
|---|---|---|
| AWS | Bedrock, SageMaker | Teams wanting model choice and deep infrastructure control |
| Google Cloud | Vertex AI | Teams wanting an integrated path from data warehouse to trained model |
| Azure | Azure AI | Teams standardising on Microsoft tooling and identity |
For inference workloads, the practical constraint is usually the requests-per-minute ceiling on the account rather than the model catalogue. Check that before comparing feature lists.
Enterprise fit
Azure’s real advantage is not technical, it is organisational. If a company already runs Active Directory, Microsoft 365 and Windows workloads, Azure inherits identity, licensing and support relationships that already exist. That is often worth more than any individual service comparison.
AWS has the largest ecosystem of third party tooling and the deepest pool of people who already know it. Google Cloud is the smallest of the three by market share, which occasionally shows up as thinner third party integration support.
Cost
Headline instance pricing is close enough between the three that it rarely decides anything. The costs that actually differ are the ones people forget to model:
- Egress. Data leaving the platform is charged, and for content-heavy workloads it can exceed compute cost.
- Managed service premiums. A managed database costs more than the same database on a virtual machine. That is usually money well spent, but it should be a decision.
- Cross-zone traffic. Traffic between availability zones is often billable and easy to generate accidentally with a poorly placed load balancer.
If cost is the primary constraint and your workload is straightforward virtual servers, none of these three will be the cheapest option. Providers like Hetzner, DigitalOcean and Kamatera exist precisely in that gap.
A short decision path
- Analytics or ML is the core of the product? Start with Google Cloud.
- Already a Microsoft organisation? Start with Azure.
- Need breadth, obscure services, or the largest hiring pool? Start with AWS.
- Just need virtual servers cheaply? Look outside the big three entirely.
Common questions
Can I run across more than one platform?
Yes, and many teams do, usually keeping one primary platform and using a second for a specific capability. Deliberate multi-cloud costs operational complexity, so it should be a choice rather than an accident.
Does account tier differ between the three?
Yes. Each platform has its own quota model and its own credit programmes. Compare configurations directly rather than assuming equivalence.