Cloud Fundamentals: Choosing Between AWS, Azure, and Google Cloud

Cloud computing has become one of the foundations of modern software development, data engineering, DevOps, and artificial intelligence.

When organizations build a new application today, they often do not purchase physical servers, install them in a data center, and maintain everything themselves. Instead, they rent computing resources from cloud providers.

Three platforms dominate many enterprise cloud conversations:

• Amazon Web Services (AWS)

• Microsoft Azure

• Google Cloud

All three platforms provide computing power, storage, databases, networking, containers, serverless computing, security, analytics, and increasingly sophisticated AI services.

This creates an obvious question for students and engineers:

Which cloud platform should I learn or use—AWS, Azure, or Google Cloud?

There is no universal winner.

The correct choice depends on the application, existing technology environment, organizational requirements, available skills, geographic needs, security requirements, and cost model.

In this article, we will understand how the three major cloud platforms compare and, more importantly, how engineers should think when choosing between them.

1. What Is a Cloud Provider?

A cloud provider operates large data centers containing servers, storage systems, networking equipment, GPUs, and other infrastructure.

Instead of purchasing this infrastructure, organizations rent resources when they need them.

For example, imagine that a company wants to deploy a web application.

Traditionally, it might need to:

1. Purchase a physical server.

2. Install an operating system.

3. Configure networking.

4. Install the application.

5. Configure storage.

6. Maintain backups.

7. Replace failed hardware.

8. Upgrade infrastructure when traffic increases.

With cloud computing, much of this infrastructure can be provisioned through a web console, command-line interface, API, or Infrastructure as Code.

The architecture may look like this:

Users

↓

Internet

↓

Cloud Load Balancer

↓

Application Servers

↓

Database

↓

Cloud Storage

The cloud provider manages the physical infrastructure while the organization manages the resources and applications it creates on top of that infrastructure.

2. The Three Major Cloud Platforms

The three platforms students will encounter most frequently are AWS, Azure, and Google Cloud.

Amazon Web Services

Amazon Web Services (AWS) was launched by Amazon and has developed an extremely broad portfolio of cloud services.

Some widely used AWS services include:

Amazon EC2 — Virtual machines

Amazon S3 — Object storage

Amazon RDS — Managed relational databases

AWS Lambda — Serverless functions

Amazon ECS / EKS — Container orchestration

Amazon SageMaker AI — Machine learning development

Amazon Bedrock — Generative AI applications and foundation models

AWS describes its current portfolio as more than 240 cloud and AI services and operates infrastructure across dozens of geographic regions. (AWS Support)

Microsoft Azure

Microsoft Azure is Microsoft’s cloud computing platform.

Azure is particularly important in enterprise environments because many organizations already use technologies such as:

Windows Server

Microsoft 365

Microsoft Entra ID

SQL Server

PowerShell

.NET

GitHub

Popular Azure services include:

Azure Virtual Machines — Virtual servers

Azure Blob Storage — Object storage

Azure SQL Database — Managed relational database

Azure Functions — Serverless computing

Azure Kubernetes Service (AKS) — Managed Kubernetes

Azure Machine Learning — Machine learning platform

Microsoft Foundry — Platform for building and managing AI applications and agents

Microsoft currently positions Foundry, formerly Azure AI Studio, as a unified environment for building, deploying, governing, and scaling AI applications and agents. (Microsoft Azure)

Google Cloud

Google Cloud is Google’s cloud computing platform.

Google Cloud has particularly strong visibility in areas such as:

Data analytics

Machine learning

Kubernetes

AI

Big data

Cloud-native applications

Popular services include:

Compute Engine — Virtual machines

Cloud Storage — Object storage

Cloud SQL — Managed relational databases

Cloud Run — Serverless container applications

Google Kubernetes Engine (GKE) — Managed Kubernetes

BigQuery — Serverless data warehouse and analytics platform

Vertex AI — Machine learning and generative AI platform

Google describes Vertex AI as a platform for training predictive and generative AI models at scale, hosting models, and performing inference. (Google Cloud Documentation)

3. The Same Cloud Concepts Have Different Names

One of the biggest sources of confusion for beginners is terminology.

AWS, Azure, and Google Cloud frequently provide similar capabilities but give the services different names.

Understanding the concept is therefore more valuable than memorizing product names.

Google itself maintains a service comparison reference mapping comparable services across AWS, Azure, and Google Cloud, illustrating how closely many fundamental cloud concepts correspond across platforms. (Google Cloud Documentation)

This leads to an important lesson:

Learn cloud concepts first and cloud product names second.

If you understand virtual machines, containers, object storage, load balancing, networking, IAM, databases, serverless computing, and Kubernetes, moving between cloud providers becomes considerably easier.

4. Comparing Compute Services

Compute is one of the most fundamental cloud services.

Suppose we have a Python API.

We need a server where that API can run.

All three providers allow us to create virtual machines.

AWS

Amazon EC2

Azure

Azure Virtual Machines

Google Cloud

Compute Engine

Conceptually:

Application

↓

Operating System

↓

Virtual Machine

↓

Cloud Infrastructure

The cloud provider manages the physical hardware.

You manage the virtual machine and the software installed on it.

For many workloads, however, engineers no longer need to manage complete virtual machines.

They may instead use containers or serverless services.

5. Comparing Storage

Cloud applications frequently need to store files such as:

• Images

• Videos

• Documents

• Backups

• Logs

• Machine learning datasets

• Model artifacts

• Static website files

The major object storage services are:

AWS → Amazon S3

Azure → Azure Blob Storage

Google Cloud → Cloud Storage

Conceptually:

Application

↓

Cloud Storage API

↓

Object Storage

Unlike a traditional hard drive, applications normally interact with object storage through APIs.

This makes cloud storage highly useful for scalable applications and AI workloads.

For example, an ML pipeline might look like:

Training Dataset

↓

Cloud Object Storage

↓

GPU Training Infrastructure

↓

Trained Model

↓

Model Storage

↓

Inference API

6. Comparing Databases

All three providers offer numerous managed database technologies.

Instead of manually installing and maintaining a database server, organizations can use a managed database service.

For relational databases:

AWS → Amazon RDS

Azure → Azure SQL Database and other managed database services

Google Cloud → Cloud SQL

Managed services can simplify tasks such as:

• Backups

• Patching

• Replication

• Availability

• Monitoring

• Scaling

Cloud providers also offer NoSQL, document, key-value, graph, caching, and globally distributed databases.

The important question is therefore not simply:

Which cloud has databases?

They all do.

The better question is:

Which database technology best fits our application’s data model, scale, latency, availability, and operational requirements?

7. Comparing Serverless Computing

Traditional applications require servers.

Serverless computing changes the model.

Instead of managing servers directly, developers deploy code and allow the cloud platform to manage the underlying infrastructure.

Major serverless function platforms include:

AWS Lambda

Azure Functions

Google Cloud serverless technologies

For example:

User uploads image

↓

Cloud Storage

↓

Serverless Function Triggered

↓

Process Image

↓

Store Result

The developer concentrates primarily on application logic rather than server administration.

Azure, for example, describes Azure Functions as an event-driven serverless computing service designed to run code without requiring developers to manage the underlying server infrastructure directly. (Microsoft Azure)

8. Comparing Containers and Kubernetes

Containers have become extremely important in modern application deployment.

A container packages:

Application + Dependencies + Runtime

A Docker container that runs locally can often be deployed to any major cloud platform.

For Kubernetes, the major managed services are:

AWS → Amazon EKS

Azure → Azure Kubernetes Service (AKS)

Google Cloud → Google Kubernetes Engine (GKE)

Conceptually:

Docker Containers

↓

Kubernetes

↓

Cloud Infrastructure

This demonstrates why learning technologies such as Docker and Kubernetes is useful even when you do not know which cloud provider your future organization will use.

These skills transfer across cloud environments.

9. AWS: Where Does It Stand Out?

AWS has one of the broadest cloud service portfolios.

Its ecosystem covers areas including:

• Compute

• Storage

• Networking

• Databases

• Containers

• Serverless computing

• DevOps

• Machine learning

• Generative AI

• Analytics

• IoT

• Security

• Monitoring

AWS currently advertises more than 240 services and a global infrastructure spanning 39 geographic regions. (AWS Support)

This breadth is one reason AWS is commonly encountered across startups, technology companies, SaaS platforms, and large enterprises.

AWS may be attractive when:

Your organization already operates substantial AWS infrastructure.

You need a very broad selection of cloud services.

Your team already has strong AWS expertise.

You are building cloud-native systems.

You need mature infrastructure tooling.

You want extensive choices for infrastructure architectures.

Potential challenge

The enormous number of AWS services can initially feel overwhelming.

Beginners may encounter many product names before understanding how those products relate to fundamental cloud concepts.

10. Azure: Where Does It Stand Out?

Azure is particularly attractive to organizations already heavily invested in Microsoft’s technology ecosystem.

Imagine an enterprise using:

Microsoft 365

Windows Server

Microsoft Entra ID

SQL Server

Power BI

.NET

GitHub

Using Azure can allow these technologies to integrate naturally into the organization’s broader infrastructure strategy.

Azure may be attractive when:

Your company is already Microsoft-focused.

You use .NET extensively.

You have enterprise Windows infrastructure.

Identity is heavily integrated with Microsoft Entra ID.

Your organization uses Microsoft security and governance technologies.

You want cloud infrastructure closely integrated with Microsoft’s enterprise ecosystem.

Microsoft also has an increasingly extensive AI ecosystem through Azure Machine Learning and Microsoft Foundry. Foundry integrates models, agents, tools, Azure Functions, Azure App Service, databases, monitoring, identity, security, and governance services. (Microsoft Azure)

Potential challenge

Like AWS, Azure has a very large product portfolio, and naming and service organization can take time for beginners to understand.

11. Google Cloud: Where Does It Stand Out?

Google Cloud has a particularly strong reputation in data, analytics, Kubernetes, and machine learning.

Google created Kubernetes before donating the project to the Cloud Native Computing Foundation, and Google Cloud’s managed Kubernetes offering is called GKE.

Google Cloud also offers technologies such as:

BigQuery

Vertex AI

GKE

Cloud Run

Cloud Storage

Compute Engine

Google Cloud may be attractive when:

You have data-intensive workloads.

You rely heavily on analytics.

You use BigQuery.

You are developing machine learning or AI systems.

Your architecture relies heavily on Kubernetes.

You want highly managed container-based application deployment.

You are building applications around Google’s AI ecosystem.

Google’s official cross-cloud comparison, for example, maps Vertex AI to broadly comparable ML capabilities such as Amazon SageMaker and Azure’s AI/ML platform. (Google Cloud Documentation)

Potential challenge

Organizations that already have substantial AWS or Microsoft infrastructure may find another provider more natural because of their existing technology investments and internal expertise.

12. AWS vs Azure vs Google Cloud for AI

AI has become one of the most important areas of cloud competition.

Modern AI applications may require:

GPUs

↓

Model Training

↓

Model Registry

↓

Model Deployment

↓

Inference API

↓

Monitoring

Each cloud provides technologies covering these layers.

AWS AI Ecosystem

Two important AWS platforms are:

Amazon SageMaker AI

SageMaker is designed around the machine learning lifecycle.

It can support activities such as:

• Data preparation

• Model training

• Experimentation

• Model deployment

• Inference

• ML operations

Amazon Bedrock

Bedrock focuses heavily on building applications using foundation models and generative AI.

A simplified distinction is:

Traditional/custom ML development

→ SageMaker AI

Foundation-model and generative-AI applications

→ Amazon Bedrock

AWS itself provides a decision guide explaining when developers may choose Bedrock versus SageMaker AI.

13. Azure AI Ecosystem

Azure offers several technologies for AI development.

Two particularly important ones are:

Azure Machine Learning

Used for building, training, deploying, and managing machine learning systems.

Microsoft Foundry

Used for developing modern AI applications and AI agents.

A simplified architecture might look like:

Application

↓

Microsoft Foundry

↓

AI Model

↓

Enterprise Data

↓

Azure Infrastructure

Microsoft Foundry currently integrates capabilities including models, agents, tools, Azure Machine Learning, Azure Functions, databases, API management, identity, monitoring, and security services. (Microsoft Azure)

This can be especially attractive for organizations already operating inside the Microsoft ecosystem.

14. Google Cloud AI Ecosystem

Google Cloud’s primary AI development platform is:

Vertex AI

Vertex AI supports both traditional machine learning and generative AI workflows.

A simplified workflow could be:

Data

↓

Cloud Storage / BigQuery

↓

Vertex AI

↓

Training / Model

↓

Deployment

↓

Predictions

Google describes Vertex AI as supporting predictive and generative AI model training, hosting, and inference at scale. (Google Cloud Documentation)

Google Cloud can therefore be particularly compelling when an organization’s data analytics and AI pipelines are closely connected.

15. Generative AI Changes the Comparison

Previously, organizations often compared cloud providers mainly based on:

Compute + Storage + Databases + Networking

Today, another layer has become critical:

Foundation Models

Cloud platforms increasingly provide managed access to foundation models, AI development platforms, vector-search technologies, AI agents, model monitoring, security controls, and AI infrastructure.

The architecture of a modern AI application might therefore look like:

User

↓

Web Application

↓

API

↓

AI Model / Agent

↓

Enterprise Knowledge

↓

Cloud Database + Object Storage

↓

Monitoring + Security

Choosing a cloud for AI therefore requires evaluating much more than GPU availability.

Teams must consider:

• Available models

• Model quality

• AI platform capabilities

• Data integration

• RAG capabilities

• Agent development

• Governance

• Security

• Inference cost

• Latency

• Regional availability

• Existing enterprise systems

16. Comparing Data and Analytics

Modern AI depends heavily on data.

Each cloud therefore provides analytics platforms.

AWS

Common services include:

Amazon Redshift

AWS Glue

Amazon Athena

Amazon EMR

Azure

Common services include:

Azure Synapse Analytics

Microsoft Fabric

Azure Data Factory

Google Cloud

Common services include:

BigQuery

Dataflow

Dataproc

Google’s official service comparison maps BigQuery broadly against Amazon Redshift and Azure Synapse Analytics. (Google Cloud Documentation)

Organizations already using one provider’s data ecosystem may find it easier to build their AI infrastructure on the same cloud.

17. Pricing: Which Cloud Is Cheapest?

This is one of the most common questions students ask.

Unfortunately, there is no simple answer.

AWS is not always cheaper. Azure is not always cheaper. Google Cloud is not always cheaper.

Cloud pricing depends on many variables.

For example:

Monthly Cost = Compute + Storage + Database + Network + API + AI + Other Services

Even compute cost depends on:

• VM type

• CPU

• Memory

• GPU

• Region

• Operating system

• Runtime duration

• Reserved capacity

• Spot/preemptible resources

• Data transfer

AI introduces additional variables such as:

• Tokens

• Model

• Input size

• Output size

• GPU hours

• Training duration

• Number of inference requests

• Embeddings

• Vector storage

Azure’s own pricing catalog, for example, separates pricing across VMs, storage, databases, serverless functions, AI models, agents, speech, and machine learning services rather than presenting one universal “cloud price.” (Microsoft Azure)

Therefore, never choose a cloud using a statement such as:

“Cloud X is cheaper.”

Instead compare the specific architecture and expected workload.

18. Regions and Availability Zones

Cloud providers operate data centers around the world.

Infrastructure is generally organized around concepts such as:

Region

and

Availability Zone

A region represents a geographic area where cloud infrastructure operates.

Availability zones provide separate infrastructure locations within or associated with a region.

For example:

Region

├── Availability Zone A

├── Availability Zone B

└── Availability Zone C

Why does this matter?

Imagine your application is running entirely from one infrastructure location.

If that location experiences a serious failure, your application could become unavailable.

A resilient architecture may distribute workloads across multiple availability zones.

Load Balancer

↓

AZ-1 → Application Server

AZ-2 → Application Server

AZ-3 → Application Server

This improves fault tolerance.

For global applications, organizations may also deploy infrastructure across multiple regions.

19. Geographic Location Matters

Suppose most of your users are in India.

Hosting your application extremely far from those users may increase network latency.

Instead, organizations generally choose infrastructure closer to users while also considering:

• Service availability

• Regulatory requirements

• Disaster recovery

• Data residency

• Pricing

Therefore, when comparing AWS, Azure, and Google Cloud, always verify whether the services you need are available in the required region.

A service being available somewhere in a cloud provider’s network does not mean it is available in every region.

20. Security and Identity Management

Security is one of the most important aspects of cloud computing.

All three providers offer sophisticated identity and security systems.

The core principle is similar:

A user, application, or service should receive only the permissions it actually requires.

This is known as the principle of least privilege.

Imagine an application only needs permission to read images from storage.

It should receive:

READ permission

not:

READ + WRITE + DELETE + ADMIN

Identity systems therefore control:

Who

can perform:

What Action

on:

Which Resource

under:

Which Conditions

Understanding this concept is more important than memorizing individual IAM product names.

21. Shared Responsibility Model

A common beginner misconception is:

“If my application is in the cloud, the cloud provider handles all security.”

That is incorrect.

Cloud security follows a shared responsibility model.

Very broadly:

Cloud Provider

Responsible for protecting underlying cloud infrastructure.

Customer

Responsible for correctly configuring and securing the resources they use.

For example, the provider may protect the physical storage infrastructure.

But if a developer accidentally makes sensitive storage publicly accessible, that configuration can still create a security problem.

Cloud security therefore requires knowledge of:

• IAM

• Encryption

• Secrets management

• Network security

• Logging

• Monitoring

• Backup

• Configuration

• Compliance

22. DevOps on AWS, Azure, and Google Cloud

Cloud computing and DevOps are closely connected.

A typical modern deployment might look like:

Developer

↓

Git Repository

↓

CI Pipeline

↓

Automated Tests

↓

Docker Image

↓

Container Registry

↓

Cloud Deployment

↓

Monitoring

All three platforms provide technologies that can participate in this workflow.

But organizations are not required to use every DevOps tool from the same provider.

For example, a company might use:

GitHub

↓

GitHub Actions

↓

Docker

↓

AWS / Azure / Google Cloud

This illustrates another important principle:

Modern cloud architectures often combine cloud services with independent open-source and SaaS technologies.

23. Multi-Cloud Architecture

Organizations do not always choose only one provider.

Some use multiple cloud providers.

This is known as a multi-cloud strategy.

For example:

Application A → AWS

Analytics → Google Cloud

Enterprise Applications → Azure

Why?

Because different teams may have different requirements.

A company might choose one provider for infrastructure, another for specialized analytics, and another because of an enterprise partnership.

However, multi-cloud also creates complexity.

Teams must manage:

• Multiple IAM systems

• Multiple billing systems

• Different networking models

• Different monitoring systems

• Different security configurations

• Different APIs

• Different skill requirements

Therefore:

Multi-cloud should solve a real business or technical problem, not simply be adopted because using several clouds sounds sophisticated.

24. Hybrid Cloud

Another important architecture is hybrid cloud.

Hybrid cloud combines:

On-Premises Infrastructure

Public Cloud

For example:

Company Data Center

↓

Private Network Connection

↓

Azure / AWS / Google Cloud

Large enterprises may retain some workloads on-premises because of:

• Legacy applications

• Regulation

• Security policies

• Hardware requirements

• Data residency

• Migration complexity

while moving other workloads to the cloud.

25. Vendor Lock-In

Cloud providers make application development easier by offering specialized managed services.

But those services can create vendor lock-in.

Imagine an application heavily dependent on many AWS-specific technologies.

Migrating that application to Azure or Google Cloud could require significant redesign.

One way to reduce lock-in is to use portable technologies where appropriate.

Examples include:

Docker

Kubernetes

PostgreSQL

Terraform / OpenTofu

Python

Java

Open-source frameworks

However, avoiding every cloud-specific service is not necessarily a good strategy.

Managed services can provide enormous productivity benefits.

The real question is:

Is the benefit of using this managed service greater than the potential future migration cost?

26. A Practical Decision Framework

Instead of asking:

“Which cloud is best?”

ask a series of more useful questions.

Question 1: What does the organization already use?

If the organization already has extensive AWS infrastructure, AWS may be the natural choice.

If it is deeply invested in Microsoft’s enterprise ecosystem, Azure may integrate naturally.

If the company has significant Google Cloud data infrastructure, Google Cloud may be the logical platform.

Question 2: What kind of workload are we building?

Examples:

Traditional web application

All three are strong choices.

Enterprise Microsoft application

Azure may deserve particular consideration.

Data analytics platform

Google Cloud may deserve particular consideration.

Large cloud-native ecosystem

AWS may deserve particular consideration.

AI application

Compare Bedrock/SageMaker, Microsoft Foundry/Azure ML, and Vertex AI based on the actual model and infrastructure requirements.

Question 3: Where are the users?

Choose infrastructure locations that provide appropriate latency and satisfy data-residency requirements.

Question 4: What expertise does the team already have?

Existing knowledge matters.

A platform may be technically excellent but still be a poor choice if the organization lacks the expertise to operate it safely.

Question 5: What will it cost?

Estimate:

Compute

Storage

Database

Network Transfer

Monitoring

Backup

AI Usage

Support

=

Total Cloud Cost

Do not compare only virtual machine prices.

Question 6: What are the security and compliance requirements?

Consider:

• Identity management

• Encryption

• Audit logging

• Network isolation

• Data residency

• Regulatory compliance

• Governance

Question 7: How portable must the application be?

If portability is important, technologies such as containers, Kubernetes, and portable database technologies may become more valuable.

27. A Simple Selection Guide

A simplified starting point could look like this:

This table should not be treated as a rigid rule.

It is simply a starting point for investigation.

28. Example: Choosing a Cloud for an AI Application

Imagine that we are building an AI customer-support system.

The architecture requires:

Web Application

↓

Backend API

↓

LLM

↓

RAG System

↓

Vector Search

↓

Company Documents

↓

Database

We need:

• Application hosting

• Storage

• AI models

• Embeddings

• Search

• Database

• Authentication

• Monitoring

• Security

Instead of asking:

Which cloud is best?

we should compare:

AWS option

Application infrastructure

• Amazon Bedrock

• Storage

• Database

• Search/vector technologies

• AWS security and monitoring

Azure option

Azure application infrastructure

• Microsoft Foundry

• Azure data services

• Microsoft identity/security ecosystem

Google Cloud option

Google Cloud infrastructure

• Vertex AI

• Google Cloud data and analytics technologies

The architecture requirements should drive the decision.

29. What Should Students Learn First?

Students often worry:

“Do I need to learn all three clouds?”

No.

That is usually unnecessary at the beginning.

A much better strategy is:

Step 1

Learn cloud fundamentals.

Understand:

• Regions

• Availability zones

• Compute

• Storage

• Databases

• Networking

• IAM

• Load balancing

• Containers

• Serverless

• Monitoring

• Scaling

Step 2

Choose one cloud provider.

Step 3

Build practical projects.

For example:

Project 1

Deploy a simple web application.

Project 2

Upload and retrieve files from cloud storage.

Project 3

Connect an application to a managed database.

Project 4

Deploy a Docker container.

Project 5

Create a serverless function.

Project 6

Deploy an AI API.

Once you understand these concepts on one cloud, learning another becomes considerably easier.

30. A Better Way to Think About Cloud Skills

Do not think:

AWS Engineer

or

Azure Engineer

or

Google Cloud Engineer

Think:

Cloud Engineer

who currently implements cloud concepts using a particular platform.

For example:

If you understand object storage through Amazon S3, learning Azure Blob Storage becomes easier.

If you understand EC2, understanding Compute Engine or Azure Virtual Machines becomes easier.

If you understand EKS, the conceptual transition to AKS or GKE becomes easier.

The products change.

The fundamental architecture principles remain remarkably similar.

31. Cloud Knowledge Is Becoming Important for AI Engineers

AI engineers increasingly need at least basic cloud knowledge.

Why?

Because production AI requires more than a model.

A complete AI system may require:

Model

GPU / Compute

Data

Storage

API

Database

Networking

Security

Monitoring

Deployment

This means AI engineers benefit from understanding:

• Cloud storage

• GPUs

• Containers

• APIs

• Serverless computing

• Kubernetes

• Identity management

• Secrets

• Monitoring

• Cost management

The model is only one component of the complete production system.

AWS, Azure, and Google Cloud all provide mature platforms for building modern applications.

The biggest mistake beginners can make is trying to determine which platform is universally “best.”

There is no universal winner.

Instead, remember these principles:

AWS offers an extremely broad cloud ecosystem and is commonly encountered across many types of cloud-native workloads.

Azure can be particularly attractive for enterprises deeply integrated with Microsoft technologies.

Google Cloud has strong offerings around data, analytics, Kubernetes, machine learning, and AI.

All three provide:

Compute

Storage

Databases

Networking

Containers

Kubernetes

Serverless computing

Security

Monitoring

Analytics

Machine learning

Generative AI

The service names differ, but many fundamental concepts remain the same.

Therefore, the most important skill is not memorizing hundreds of cloud services.

It is understanding cloud architecture.

Once you understand:

Compute + Storage + Networking + Databases + Security + Containers + Scaling + AI

you can transfer that knowledge between AWS, Azure, Google Cloud, and future cloud platforms.

The better question is therefore not:

Which cloud is the best?

It is:

Which cloud is the best fit for this workload, this organization, and these requirements?

That is the mindset of a cloud engineer.

Cloud platforms should be viewed as large toolboxes.

AWS, Azure, and Google Cloud contain many of the same categories of tools, but each toolbox has different strengths, integrations, pricing structures, and specialized services.

A good engineer does not choose a toolbox because it is popular.

A good engineer first understands the problem—and then chooses the tools that solve it effectively.

Learn the concepts first. Learn one cloud deeply. Then transferring your knowledge to another cloud becomes much easier.

Happy Learning!