Selecting a cloud platform is an architecture decision, not a popularity contest. Amazon Web Services (AWS), Microsoft Azure and Google Cloud can each run business applications, databases, virtual machines and data platforms. The differences that matter are usually in the organisation’s existing identity systems, operational skills, licensing, connectivity, application dependencies and the services required by a particular workload.
A business moving its first application to the cloud does not need to evaluate hundreds of cloud products. It needs a workload inventory, realistic availability requirements and a way to compare the complete operating cost. The goal is to choose a platform that the organisation can deploy, secure and maintain reliably.
Start with the workload, not the provider
Before comparing vendors, describe what is actually moving. Is the workload a public website, a line-of-business application, a Windows-based ERP, a data-processing service or a collection of virtual servers? Record its operating system, runtime, database version, storage consumption, average and peak usage, external integrations and expected growth. Include dependencies that are easy to overlook: file shares, identity providers, scheduled jobs, SMTP relays, firewall rules, certificates and third-party IP allowlists.
Two applications that appear similar in a presentation may behave very differently in production. A customer portal with occasional traffic has a different design problem from an integration service processing millions of events. Likewise, a database bound to a commercial licence may carry constraints that matter more than the nominal price of a virtual machine.
The first useful output is a workload profile: what it does, who uses it, what it depends on, who supports it and how much interruption the business can tolerate. Without that profile, comparisons usually drift toward feature checklists that do not influence the decision.
Where AWS, Azure and Google Cloud tend to fit
AWS offers a broad range of infrastructure, managed databases, networking, security and application services. It is often a natural candidate for heterogeneous Linux-based estates, cloud-native applications and teams already familiar with AWS operations. However, service breadth can introduce governance and cost-management complexity if an organisation deploys resources without consistent ownership and account structures.
Azure deserves particular attention where Microsoft Entra ID, Windows Server, Microsoft 365, SQL Server and enterprise licensing are already central to operations. Familiar identity and management practices can reduce friction, although organisations still need to design network isolation, access controls and monitoring explicitly. Using Microsoft products does not automatically mean Azure will be the cheapest or best-performing choice for every application.
Google Cloud is worth evaluating for data-intensive applications, analytics, containers and organisations working heavily with Google services. Its managed data tools and Kubernetes heritage are relevant to some workloads. Nevertheless, the right choice depends on required regions, team expertise, contractual conditions and how existing software will be deployed and supported.
None of these descriptions is a universal ranking. Providers change services, availability and pricing; architecture decisions should be made against current regional offerings and a measured workload.
Compare identity, networks and access control early
Identity is often where a cloud deployment succeeds or becomes difficult to operate. Identify who needs administrative access, how users authenticate, how service accounts work and how emergency access is maintained. Prefer central identity integration, least-privilege permissions and short-lived credentials where available. Keep application roles separate from infrastructure administrators.
Next, draw the network. Document private subnets, public endpoints, site-to-site connectivity, DNS, outbound internet access and any on-premises systems that must remain reachable. A cloud resource is not automatically private because the application behind it requires a password. Security groups, firewall policies, route tables, secrets storage and audit logging must be considered together.
A common planning mistake is designing production connectivity only after the application has been moved. When a system depends on a local database, warehouse application or corporate directory, connectivity can dictate the migration sequence and costs. It also influences latency and failure behaviour.
Understand the full cost, not just compute
Virtual-machine prices are easy to compare and easy to misinterpret. A monthly estimate should include storage capacity and operations, snapshots, data transfer, public IPs, logging, backup retention, database services, support plans, network gateways and any security tooling. Environments for development, testing and disaster recovery are not free simply because they are not production.
Consumption-based billing makes scaling flexible, but it also requires financial controls. Allocate ownership tags, establish budget alerts and schedule non-production systems where appropriate. Review reserved-capacity or savings options only after usage patterns are understood. Committing early to an unsuitable instance family can exchange flexibility for a discount that never produces value.
Cost comparisons also need a baseline: what does the existing environment cost after including hosting, electricity, hardware refresh, maintenance contracts, staff time, backups and outage exposure? A lift-and-shift migration may initially cost more than expected because the cloud infrastructure has not yet been right-sized.
Reliability is designed, not purchased
Availability claims describe a service under specific conditions, not the uptime of an entire business application. A highly available database does not compensate for a single application server, an untested recovery procedure or a broken external dependency. Document recovery time objective (RTO) and recovery point objective (RPO), then select the architecture that can meet them.
For some systems, reliable backups and a documented rebuild are sufficient. Others need multiple availability zones, automated failover, replication and more sophisticated incident response. Every improvement has a price in infrastructure, operational workload and application complexity. The business should approve that trade-off rather than receiving an architecture based on assumptions.
Cloud-provider architecture guidance is useful here. The AWS Well-Architected Framework considers operational excellence, security, reliability, performance efficiency, cost optimisation and sustainability. Azure and Google Cloud publish comparable architecture frameworks; these can structure reviews without forcing identical implementations.
Assess your team’s operating model
The cloud does not eliminate patching, incident ownership, access reviews or backup testing. Managed services can remove portions of the maintenance burden, but someone still needs to own configurations, credentials, alerts and application-level recovery. Before selecting a platform, answer three questions: who will deploy it, who will operate it at 2 a.m. and who can diagnose a failure across networks, applications and data?
Teams can develop new skills, but a critical migration is a poor moment to discover that nobody understands the chosen logging, networking or access-control model. A sensible pilot can expose capability gaps while the stakes are low.
A practical proof-of-concept method
Choose one representative, non-critical workload. Build a secure landing environment, deploy it using a repeatable method and test authentication, networking, backups, application performance and monitoring. Measure actual resource use and project monthly costs from observed data. Include deployment time and the effort required to investigate a simulated problem.
Keep the comparison fair. Give each candidate equivalent workloads, similar resilience requirements and the same measurement period. Avoid selecting a winner because one provider received more configuration time than the others. Document what was deliberately excluded so the results are not mistaken for a complete production design.
A good proof of concept produces a decision record: tested configuration, expected cost, operational limitations, unresolved risks and why the selected platform fits the business. It may also reveal that keeping a workload on its existing infrastructure is the sensible near-term choice.
Questions to settle before signing off
Check the required data region, vendor and support commitments, software licensing, contractual exit provisions and data-export paths. Assess how deployment will be automated and how infrastructure changes will be reviewed. Plan identity administration, monitoring, incident ownership and lifecycle management from day one.
Cloud selection becomes far simpler when the discussion moves from vendor preference to explicit business and technical requirements. AWS, Azure and Google Cloud are capable foundations. The better choice is the one that meets the workload’s requirements with an operational model the organisation can sustain.