Three starting points for a training decision
These are selection scenarios, not claims that a workload can run on only one provider.
AWS
Start here when the team owns AWS workloads or has a confirmed AWS delivery requirement.
Take away: Practice aligned with the accounts and services the team will operate.
Discuss an AWS-aligned learning scopeAzure
Start here when the organisation’s approved architecture and operating responsibilities centre on Azure.
Take away: Labs aligned with identity, networking and deployment conventions.
Discuss an Azure-aligned learning scopeGoogle Cloud
Start here when a confirmed project uses Google Cloud and its selected managed services.
Take away: A learning path connected to an actual delivery milestone.
Discuss a Google Cloud learning scope
Start with the workload and team responsibility
List what the team must deliver: an API, data pipeline, analytics environment or AI application. Record identity, network, storage, recovery and observability requirements before comparing product names.
A team operating an existing estate needs deeper troubleshooting practice on that platform. A new project may justify a comparative lab before choosing one.
Compare equivalent architectures, not service labels
Map compute, storage, identity, networking and managed data services to the same requirements. Products with similar names do not necessarily have identical behaviour, limits or operating models.
Use each provider’s Well-Architected guidance to review security, reliability, performance, cost and operational concerns. Translate the design principles, then learn the provider-specific implementation.
Check the services and locations the project needs
For an India-based team, verify the selected service’s regional availability, latency needs, support arrangements and organisational data-location requirements. A provider having a region in a country does not mean every service or feature is available there.
Do not treat region choice alone as a compliance conclusion. Route organisational requirements through the responsible security and legal reviewers.
Estimate the complete workload and lab cost
Include compute, storage, requests, network transfer, logs, backups and supporting services. Record assumptions such as traffic and retention so a second design can be compared fairly.
Free-tier eligibility and limits change. Use the account’s current terms and configure spending visibility. Cleaning up a virtual machine may still leave disks, addresses or other chargeable resources.
Worked example: compare a small document service
Design a fictional API that accepts a document, stores it and returns a processing result. Require authentication, logs and a recovery procedure in every candidate design.
Compare setup effort, permission clarity, failure diagnosis and estimated cost with the same workload assumptions. A cheaper estimate is not decisive if the team cannot operate the design reliably.
Teach depth first, portability second
Complete a small application lifecycle on one platform: provision, deploy, observe, recover and remove it. Then map the same responsibilities to another provider.
Training across all three clouds at once can dilute hands-on time. Use multi-cloud training when the team actually has cross-platform responsibilities, not merely because three logos look more comprehensive.
Representative service equivalents
Reviewed 27 September 2026 against provider documentation. These services occupy similar categories, but are not drop-in equivalents. Availability, features, identity integration and pricing differ by service and region.
| Need | AWS | Microsoft Azure | Google Cloud |
|---|---|---|---|
| Virtual machines | Amazon EC2 | Azure Virtual Machines | Compute Engine |
| Object storage | Amazon S3 | Azure Blob Storage | Cloud Storage |
| Managed Kubernetes | Amazon EKS | Azure Kubernetes Service (AKS) | Google Kubernetes Engine (GKE) |
| Functions / event-driven compute | AWS Lambda | Azure Functions | Cloud Run functions |
| Managed relational databases | Amazon RDS | Azure SQL Database / Azure Database for PostgreSQL | Cloud SQL |
| Analytical warehouse | Amazon Redshift | Azure Synapse Analytics / Fabric Warehouse | BigQuery |
| Machine-learning platform | Amazon SageMaker | Azure Machine Learning | Vertex AI |
| Identity and access | AWS IAM | Microsoft Entra ID and Azure RBAC | Cloud IAM |
Certification paths and a practical selection example
AWS Cloud Practitioner and Azure Fundamentals are examples of foundation-level starting points. Google Cloud Digital Leader covers foundational business and cloud concepts; Associate Cloud Engineer is a more operational path. Follow with role-specific architect, administrator, developer or data learning according to your work. Use the official catalogues for current names, objectives and retirement notices, not an old exam code on a comparison chart.
For a fictional internal reporting service, compare identity integration, database access, object storage, deployment, monitoring and recovery on the platform already supported by the team. Then cost the same expected usage, including data transfer and operations. A managed service can reduce infrastructure work while leaving your team responsible for application permissions, data quality and recovery testing.
Microsoft-heavy identity or application environments may make Azure convenient; an established AWS or Google Cloud estate may favour staying with existing skills and controls. None of these is a universal winner. Learn one platform’s core operations well before adding another solely for a badge.
Choose training from the situation
| Situation | Approach | What to check |
|---|---|---|
| An existing production estate | Prioritise its cloud | Operational gaps and upcoming changes |
| A new project with no platform decision | Run an equivalent-workload comparison | Fit, regional services and total cost |
| A genuine multi-cloud responsibility | Teach common principles plus platform labs | Enough depth to operate each environment |
Practice brief · Fictional data
Write a cloud training brief
Use a fictional document-service project and define the skills needed to deliver and operate it.
What to produce
- Workload and team-skill inventory.
- Comparable architecture and cost assumptions.
- Lab completion and cleanup checklist.
Acceptance checks
- The selected services meet requirements.
- Permissions and recovery are demonstrated.
- Cost assumptions are explicit.
- All lab resources can be identified and removed.
This is an illustrative learning exercise, not a client case study or a measured outcome.
Common questions
Which cloud is always cheapest?
None can be declared cheapest for every workload. Compare equivalent designs, usage, support and pricing terms for the actual project.
Should beginners learn all three clouds?
Usually start with one relevant platform and complete a practical lifecycle. Add another when it supports a real learning or work objective.
Continue learning
- Explore cloud training options
- Understand the delivery lifecycle
- Understand cloud-native design
- Compare the platforms for an Indian enterprise
Plan training around your workload
Share the platform, team starting point and delivery objective to discuss a relevant learning scope.
Explore cloud training →Sources and further reading
- AWS — Well-Architected Framework
- Microsoft — Azure Well-Architected Framework
- Google Cloud — Well-Architected Framework
Examples and decision frameworks are original teaching material. Product-specific implementation details should be checked against the version you use.