All three hyperscalers can run almost any enterprise workload securely and at scale. The best choice depends less on raw features and more on your existing technology, skills, data and commercial relationships. Here is how to think about the decision.
Amazon Web Services (AWS)
AWS has the broadest catalogue of services and a very large ecosystem of partners, tools and skilled engineers. It is strong across compute, storage, serverless (AWS Lambda), containers (EKS, ECS) and data, and offers generative AI through Amazon Bedrock and machine learning through SageMaker.
Often a good fit when: you want maximum service choice, are building cloud-native products, or already have AWS skills in-house.
Microsoft Azure
Azure integrates tightly with the Microsoft stack enterprises already use — Microsoft 365, Entra ID, Windows Server, SQL Server and Dynamics. Licensing benefits for existing Microsoft customers can be significant, and Azure OpenAI Service provides enterprise access to OpenAI models.
Often a good fit when: you are Microsoft-centric, run many Windows and SQL Server workloads, or need virtual desktops with Azure Virtual Desktop.
Google Cloud
Google Cloud is known for data analytics (BigQuery), Kubernetes (GKE — Kubernetes originated at Google) and AI, with Vertex AI and Gemini models. Its networking and serverless containers (Cloud Run) are also widely praised.
Often a good fit when: analytics and AI are central to your strategy, or your teams are container-first.
Comparison at a glance
| AWS | Azure | Google Cloud | |
|---|---|---|---|
| Signature strengths | Breadth, ecosystem | Microsoft integration, hybrid | Data, Kubernetes, AI |
| GenAI platform | Amazon Bedrock | Azure OpenAI, AI Foundry | Vertex AI, Gemini |
| Kubernetes | EKS | AKS | GKE |
| Data warehouse | Redshift | Fabric / Synapse | BigQuery |
| VMware option | Check current availability | Azure VMware Solution | Google Cloud VMware Engine |
Cost is about operations, not list prices
List prices across providers are broadly comparable. What drives your bill is architecture and discipline: right-sizing, commitments and savings plans, auto-scaling, storage tiering, data-transfer patterns and switching off what you don’t use. Enterprise agreements and existing licences (for example Windows Server and SQL Server on Azure) can also shift the economics.
Should you go multi-cloud?
Multi-cloud can make sense for specific reasons — using the best AI or analytics service for a job, meeting regulatory or customer requirements, or acquisitions. But spreading every workload across clouds adds complexity and cost. A common pattern is a primary cloud for most workloads, with targeted use of a second where it adds clear value, all managed with consistent identity, security and infrastructure-as-code.
How to decide
- List your critical workloads and their dependencies.
- Map them against existing skills, licences and contracts.
- Run a short proof of concept on the services that matter most.
- Model three-year total cost including migration and operations.
We are vendor-neutral and deliver on all three — see our AWS, Azure and Google Cloud services.
Frequently asked questions
Which cloud is best for AI?
All three offer strong AI platforms — Amazon Bedrock, Azure OpenAI Service and Google Vertex AI. The best choice usually depends on which models you need, where your data lives and your existing cloud footprint.
Is Azure better for Microsoft shops?
Often, yes. Azure integrates closely with Microsoft 365, Entra ID, Windows Server and SQL Server, and existing Microsoft licences can reduce costs.
Is multi-cloud a good idea?
It can be for specific needs, but it adds complexity. Many organisations choose a primary cloud and use a second selectively where it adds clear value.