Three starting points for a training decision
These are ways to choose, not claims that a workload can only run on one cloud.
AWS
Start here when your team runs workloads on AWS, or a project you have confirmed must be delivered on AWS.
Take away: Practice that matches the accounts and services your team will run.
Discuss an AWS-aligned learning scopeAzure
Start here when your company’s approved design and day-to-day running are built around Azure.
Take away: Labs that follow your identity, networking and deployment conventions.
Discuss an Azure-aligned learning scopeGoogle Cloud
Start here when a confirmed project uses Google Cloud and the managed services it has chosen.
Take away: A learning path tied to a real delivery milestone.
Discuss a Google Cloud learning scope
Start with the workload and what the team must run
List what the team must deliver: an API, a data pipeline, an analytics setup or an AI app. Write down the identity, network, storage, recovery and monitoring needs before you compare product names.
A team running systems that already exist needs deeper troubleshooting practice on that cloud. A new project may justify a lab that compares clouds before you pick one.
Compare like-for-like designs, not service names
Map compute, storage, identity, networking and managed data services to the same needs. Products with similar names do not always behave the same way, or have the same limits and running effort.
Use each provider’s Well-Architected guidance to review security, reliability, performance, cost and day-to-day running. Learn the general design ideas first, then how each provider does it.
Check the services and locations the project needs
For a team in India, check that each service you need is available in your region, and check speed, support and any rules your company has on where data is kept. A provider having a region in a country does not mean every service or feature is offered there.
Choosing a region does not settle compliance on its own. Send your company’s requirements to the people responsible for security and legal review.
Estimate the full cost of the workload and the labs
Include compute, storage, requests, data transfer, logs, backups and supporting services. Write down your assumptions, such as traffic and how long data is kept, so a second design can be compared fairly.
Free-tier rules and limits change. Use your account’s current terms and set up spending alerts. Deleting a virtual machine can still leave disks, IP addresses or other resources that cost money.
Worked example: compare a small document service
Design a made-up API that accepts a document, stores it and returns a result. Every design must include sign-in, logs and a way to recover.
Compare the setup effort, how clear the permissions are, how easy failures are to find and the estimated cost, using the same workload assumptions. A cheaper estimate does not win if the team cannot run the design reliably.
Go deep first, then learn to move across clouds
Take one small app through its full life on one cloud: set it up, deploy it, monitor it, recover it and remove it. Then map the same jobs to another provider.
Training on all three clouds at once can thin out hands-on time. Use multi-cloud training when the team really works across clouds, not because three logos look more complete.
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 your situation
| Situation | Approach | What to check |
|---|---|---|
| Systems already running in production | Train on that cloud first | Gaps in running it and changes coming up |
| A new project with no cloud chosen yet | Compare clouds on the same workload | Fit, services in your region and total cost |
| A team that really works across clouds | Teach shared ideas plus labs on each cloud | Enough depth to run each one |
Practice brief · Fictional data
Write a cloud training brief
Use a made-up document-service project and list the skills needed to deliver and run it.
What to produce
- A list of the workload and the team’s skills.
- Like-for-like designs and cost assumptions.
- A lab completion and clean-up checklist.
Acceptance checks
- The chosen services meet the needs.
- Permissions and recovery are shown working.
- Cost assumptions are written down.
- Every lab resource can be found 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 is cheapest for every workload. Compare like-for-like designs, usage, support and pricing terms for your actual project.
Should beginners learn all three clouds?
Usually not. Start with the one that matters to you and take an app through its full life. Add another cloud when it serves a real learning or work goal.
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
Tell us the cloud, where your team is starting from and what you need to deliver, and we will suggest a learning scope.
Explore cloud training →Sources and further reading
- AWS — Well-Architected Framework
- Microsoft — Azure Well-Architected Framework
- Google Cloud — Well-Architected Framework
The examples and checklists here are our own teaching material. Check product-specific steps against the version you use.