The "which cloud?" question comes up at the beginning of every serious infrastructure conversation. The honest answer is that for most production workloads, AWS and Azure are both excellent choices and neither is inherently wrong. The decision that matters more is how you will operate the platform once you have chosen it. That said, the choice is not arbitrary and there are factors that meaningfully differentiate the decision for specific organisations.
1. Your existing technology ecosystem
This is the highest-leverage factor for most organisations. If you are already running Microsoft 365, Entra ID (Azure Active Directory), or have a Microsoft Enterprise Agreement, Azure's native integrations reduce friction significantly: single sign-on from your existing directory, cost consolidation under existing agreements, and tooling familiarity for IT teams already in the Microsoft ecosystem.
If your engineers come from a background of open-source tooling, Linux-first infrastructure, or large-scale web environments, AWS will feel more natural. The service breadth, documentation quality, and community knowledge base around AWS are unmatched. Start with the platform your team already knows well.
2. Workload-specific requirements
Certain workload types have a natural home. Containerised applications running Kubernetes are well-served by either platform (EKS on AWS, AKS on Azure), but the experience differs. Data and analytics workloads often favour AWS (Redshift, Athena, EMR) or Azure (Synapse, Fabric) depending on the wider data stack. Machine learning and AI workloads have strong support on both, with different toolsets and partner ecosystems.
Map your specific workload requirements before generalising. The question is not which platform is better overall, but which platform has the strongest native services for your particular technical stack.
3. Data residency and regulatory requirements
UK businesses handling personal data under UK GDPR need to ensure data residency is explicitly configured, not assumed. Both AWS (UK South, London) and Azure (UK South and UK West) have UK-region data centres. The difference lies in how granular your residency controls need to be and whether your regulatory framework imposes specific requirements about which provider you can use.
Public sector and government workloads in the UK often have additional requirements around data sovereignty, accreditation (G-Cloud, NCSC guidance), and supply chain security. Check these requirements before committing to a platform, not after.
4. Support model and incident response
Hyperscaler support tiers are not designed with SMBs in mind. Enterprise support on AWS (formerly Business/Enterprise support plans) and Azure Enterprise support both provide meaningful SLAs for production incidents, but at a cost that is often prohibitive for smaller organisations. Basic and Developer support plans have response times measured in business hours, which is not acceptable for production incidents at 2am.
The managed cloud alternative is to offload this to a provider who operates your environment with their own 24x7 incident response capability. This gives you fast response times without the cost of a hyperscaler Enterprise support plan and adds operational expertise on top of raw support access.
5. Cost model fit
Cloud cost is not just about the on-demand price per vCPU hour. The total cost of ownership includes: compute, storage, networking (particularly egress), managed service fees, support tier costs, and the engineering cost of operating the environment. Some workloads are significantly cheaper on one platform than the other, particularly where there is a big difference in managed service pricing (managed Kubernetes, managed databases, etc.).
Run a cost model for your specific architecture before committing, based on actual projected usage rather than list prices for individual services. Data egress is consistently the cost dimension that surprises people most: both platforms charge for data leaving a region, and these charges accumulate quickly for applications with significant external traffic.
6. Observability and operational maturity of the ecosystem
The best platform to operate is the one you can see clearly. Both AWS and Azure have good native monitoring (CloudWatch, Azure Monitor), but both benefit significantly from a platform-agnostic observability layer like Datadog that can span both environments, correlate signals, and provide a single operational view regardless of which services are running where. If you are already using Datadog or are moving to it, this is effectively platform-neutral.
7. Partner and managed service ecosystem
What you get from the platform is only part of the equation. The quality and availability of specialised expertise, managed service providers, and implementation partners matters for organisations that do not want to build all cloud capability in-house. For the UK specifically, the depth of the partner ecosystem is better for AWS and Azure than for any other provider. Both have strong certification and accreditation programmes that allow you to verify partner expertise objectively rather than taking claims at face value. Our UK Datadog partnership is one example of the kind of specialised, verified capability that is available within this ecosystem.
The factor that matters most after the choice
Whatever platform you choose, the operational model matters more than the platform in the long run. An AWS environment operated well beats an Azure environment operated poorly, and vice versa. The platforms are converging; the quality of cloud operations is what differentiates outcomes. If you are unsure whether your chosen platform is being operated to a high standard, our cloud managed services offer a structured path from wherever you are now to a fully managed, observable environment.