AWS, Azure and Google Cloud can all host a serious business application. The useful decision starts with the workload, the people operating it and the consequences when something goes wrong.
01
Start with the workload, not the provider
Imagine a UK field service company replacing a single hosted server with a more resilient platform. Its application includes a customer portal, mobile evidence uploads, background jobs, reports, a relational database and sign in for staff who already use Microsoft 365.
All three major cloud providers can support that application. Each offers computing, managed databases, object storage, identity controls, monitoring, backups and services for running containers. Naming equivalent products does not resolve the decision.
The business first needs to define what the system must do and what would happen if it failed.
How many people use it? When are its busiest periods? How quickly must it recover? Where may its data be stored? Which integrations are business critical? What skills already exist in the team? Does a customer contract require a particular provider or control? Who will respond to an alert at 2am?
These questions turn cloud strategy into an application decision. They also prevent the architecture from being shaped by whichever product happened to look impressive in a demonstration.
02
The comparison that matters to a business
Provider features still matter, but they make more sense when attached to a real constraint. The following table is a stronger starting point than a general list of products.
| Decision area | Question to answer | Why it changes the choice |
|---|---|---|
| Existing technology | Which identity, data, reporting and development tools are already in use? | A platform that connects cleanly to the existing estate may remove custom integration and administration |
| Team capability | Which cloud can the internal team or trusted supplier operate confidently? | An elegant design is fragile when nobody understands its permissions, networking or recovery process |
| Workload shape | Is the application steady, seasonal, data heavy, event driven or dependent on background processing? | Different hosting and database models suit different traffic and processing patterns |
| Customer obligations | Are there contractual, residency, security or procurement requirements? | A technically suitable service may be unusable if it does not meet a customer obligation |
| Reliability | What recovery time and acceptable data loss have actually been agreed? | Higher availability costs more and needs a tested operating process, not only extra infrastructure |
| Total cost | What will production, testing, storage, data transfer, support and staff time cost? | The smallest calculator figure can hide expensive operating work |
| Change and exit | Which parts must remain portable and how would data be exported? | Managed services create value, but they also shape future switching cost |
The purpose of the table is not to produce a provider score out of ten. It is to expose the facts that should influence the decision.
03
Where AWS tends to fit
AWS offers a broad range of services and several ways to solve most infrastructure problems. A conventional business application might use a managed application or container service, a relational database through Amazon RDS, files in Amazon S3 and monitoring through CloudWatch. A larger platform can add queues, event services, data tooling and more specialised controls as needed.
That breadth is useful when an application has varied workloads or a team already knows AWS. It also creates choices. Two competent architects can design very different AWS platforms for the same application, each with different cost and operating implications.
AWS therefore fits well when the team has the knowledge to choose a small, coherent set of services and govern them consistently. It is less convincing when the proposal depends on a long service list but nobody owns day to day operation.
Its Well-Architected Framework encourages teams to assess operational excellence, security, reliability, performance efficiency, cost optimisation and sustainability together. That is a useful reminder that the provider choice is only the start. The quality of the resulting system depends on the decisions made within it.
04
Where Microsoft Azure tends to fit
Azure can be a natural option for organisations already centred on Microsoft technology. Existing use of Microsoft 365, Entra ID, Windows, SQL Server, Power BI or established Microsoft procurement can reduce friction around identity, reporting, licensing and administration.
For the field service example, using the organisation's existing identity setup could be more valuable than saving a small amount on compute. Staff joiners, leavers and access policies may remain part of a familiar process instead of creating a second identity estate.
Azure also provides managed application, container, database, storage and monitoring services, so it is not limited to Windows software. A Laravel, Node or container based application can run there perfectly well.
The Microsoft connection should still be tested rather than assumed. If the development team has little Azure experience, the application has no meaningful Microsoft dependencies and the operating model is unfamiliar, the brand relationship alone is not a technical case.
Azure's Well-Architected guidance explicitly treats reliability, security, cost, operations and performance as trade-offs. Extra resilience, for example, can increase cost and complexity. The right design is the one that meets the business requirement without over-engineering it.
05
Where Google Cloud tends to fit
Google Cloud is often shortlisted for data, analytics, machine learning and managed container workloads. Products such as BigQuery and Cloud Run can be particularly attractive when a system needs strong analytical capability or a simple way to run containerised services without managing a fleet of servers.
That does not make Google Cloud only a data platform. It offers managed compute, databases, storage, networking, identity and operations tooling for mainstream business applications as well.
The decision again comes back to fit. If a product team already works comfortably with Google Cloud, the application will use its data services heavily or the organisation values its managed container model, it can be a sensible home. If local support, recruitment or an established supplier base is stronger around another provider, that operational reality deserves equal weight.
Google's Well-Architected Framework applies to cloud native, migrated, hybrid and multi-cloud workloads. Like the AWS and Azure frameworks, it makes clear that good architecture is a set of ongoing decisions rather than a one-off product selection.
06
Map one representative workflow across all three
A useful comparison does not need a full production build on every provider. It needs one representative path that includes the difficult parts.
For the field service application, that path could include staff sign in, creation of an installation record, several large customer photograph uploads, background checks, an operational report, a failed job alert and a backup restored into a safe test environment.
The team can then map the responsibilities on AWS, Azure and Google Cloud. Which components are managed by the provider? Which still need patching, configuration or scaling? How are secrets stored? What does a failed deployment look like? Can support connect an error to the customer affected? How is recovery tested?
This exercise exposes more than a price calculator. It shows which platform makes the application understandable to the people who will operate it.
07
Compare total operating cost, not a sample monthly bill
Cloud pricing is detailed because cloud use is detailed. A realistic model needs more than one server size and a database estimate.
Include production and non-production environments, database capacity, storage growth, backups, monitoring logs, network traffic, support plans, security tooling and any minimum commitments. Then include the human cost of deployment, access reviews, incident response, cost checking and recovery tests.
A managed service may look more expensive than a virtual machine while removing patching, failover and maintenance work. Equally, an apparently convenient managed feature may create high usage or data transfer costs if the workload is poorly understood.
The figures should be based on representative usage, then checked after launch against real behaviour. Forecasts are useful, but cloud cost management is an operating discipline. Budgets, alerts, tagging and ownership matter whichever provider is selected.
Avoid building the decision around temporary discounts alone. A credit can make an experiment cheaper. It does not make the underlying architecture efficient after the credit ends.
08
Reliability needs a business definition
Highly available is not a complete requirement.
The business should agree how long the application can be unavailable and how much recent data it could accept losing after a serious incident. Those decisions shape database configuration, backups, deployment design, regional choices and cost.
Using several availability zones can protect against some infrastructure failures. It does not protect against an application bug that deletes records, an incorrect permission change or a release that breaks the main workflow everywhere at once.
Backups need restore tests. Alerts need an owner. Releases need a rollback route. Critical integrations need a plan for slow or unavailable suppliers. These controls matter more than drawing several cloud icons on an architecture diagram.
Each provider publishes regional and service availability information. Check the exact services required in the intended region rather than assuming that every product is present everywhere. Customer commitments and data location requirements should be recorded before the architecture is approved.
09
Multi-cloud is not a free insurance policy
Running across AWS, Azure and Google Cloud can sound like a way to avoid dependence on any one provider. In practice, it often adds another layer of identity, networking, monitoring, security, deployment and skills.
Multi-cloud can be justified. A customer may require a particular platform. An acquisition may bring another estate. A specialist service may offer unusual value. A legal or availability requirement may demand separation.
Without a specific reason, however, duplicating an application across clouds can make it harder to understand and recover. The team must maintain several implementations while still ensuring the application and data behave consistently.
A better default is to choose one primary platform and make deliberate decisions about portability. Keep source code, deployment definitions and operational documentation under the organisation's control. Maintain tested data exports. Avoid provider-specific complexity where it offers little value, but do not reject useful managed services simply to claim that moving would be easy.
Lock-in is not automatically bad. It is a trade-off between present value and future switching cost. The important part is knowing where that trade-off has been made.
10
Use a short decision process
Describe the workload, users, data, integrations and important failure scenarios. Record the business constraints, including skills, customers, security, recovery and location. Remove providers that fail a genuine constraint rather than a personal preference.
Map one representative workflow on the remaining options, estimate total operating cost and the people required to run it, then review the proposed design against the relevant Well-Architected Framework.
Finally, write down the decision, its assumptions and the events that would cause it to be revisited. This does not require months of strategy work. For many established businesses, a focused discovery and a small technical spike are enough to expose the decisive facts.
11
Choose the cloud your team can operate responsibly
AWS, Azure and Google Cloud are all credible platforms. The right choice is the one that fits the workload, existing systems, customer obligations, team capability and operating budget.
AWS may offer the most familiar route for one team. Azure may remove identity and organisational friction for a Microsoft centred business. Google Cloud may make the data or container workload simpler. Another application inside the same company may produce a different answer.
Do not buy a cloud logo. Buy a clear operating model.
If you are deciding where a business application should run, start with the workload and its failure scenarios. A short comparison grounded in real constraints will reveal more than another hundred-row feature matrix.
Useful questions
Before the next software decision, ask:
- Is the technical direction clearly linked to a business goal?
- Do the people building the system understand the commercial priority?
- Are risk, cost, security and delivery trade-offs visible early enough?
- Can the current platform support the next stage of growth?
