AWS vs Azure vs Google Cloud: A Complete Comparison of Cloud Services, Networking, Security and Architecture

Choosing between Amazon Web Services, Microsoft Azure, and Google Cloud is one of the most important infrastructure decisions an organization can make.

All three platforms provide compute, storage, databases, networking, security, monitoring, containers, Kubernetes, serverless computing, artificial intelligence, DevOps tooling, and managed services. However, they are not identical.

The biggest differences often appear in their networking models, identity systems, security tooling, service design, management experience, pricing structure, enterprise integrations, and approach to application architecture.

This guide provides a technical comparison of AWS vs Azure vs Google Cloud, with particular attention to security, cloud networking, architecture, compute, storage, Kubernetes, DevOps, pricing, availability, and practical deployment decisions.

If you are still learning the fundamentals, start with Cloud Computing Explained and IaaS vs PaaS vs SaaS Explained before going deeper into individual cloud providers.

1. AWS vs Azure vs Google Cloud at a Glance

The three providers solve many of the same infrastructure problems, but the service names and implementation details differ.

The service names differ, but the underlying concepts remain familiar: compute needs CPU and memory, networks need addressing and routing, applications need identity controls, and production systems require monitoring, security, backup, and redundancy. The original article already established this service-to-service comparison as its foundation. Pasted markdown

2. AWS vs Azure vs Google Cloud Security Comparison

Security is one of the most important dimensions when comparing cloud providers.

The question should not simply be:

Which provider is the most secure?

AWS, Azure, and Google Cloud all provide extensive security capabilities. The more useful question is:

Which security model, identity architecture, network controls, monitoring system, and operational ecosystem best match the organization?

Core Security Services

The existing article already compared these major security categories and emphasized IAM, encryption, segmentation, logging, MFA, and least privilege. Pasted markdown

AWS documents its security model around shared responsibility: AWS secures the infrastructure of the cloud, while customers remain responsible for many aspects of security in the cloud. AWS Documentation

AWS Security Strengths

AWS provides a large collection of security services covering:

• IAM and access policies
• Encryption
• Key management
• Secrets
• Network filtering
• Threat detection
• Audit logging
• DDoS protection
• Security posture management

A typical AWS security architecture might look like:

Internet
   |
CloudFront / Edge
   |
AWS WAF
   |
Application Load Balancer
   |
Private Application Tier
   |
Private Database

Identity and access should follow least privilege rather than giving broad administrative permissions to users and workloads.

For environments using AWS Shield and AWS WAF, AWS IAM determines who can access and manage those security resources. AWS Documentation

Azure Security Strengths

Azure is especially compelling for organizations that already use:

• Microsoft Entra ID
• Microsoft 365
• Windows Server
• Active Directory environments
• Microsoft Defender products
• Microsoft Sentinel

Identity becomes a major part of the Azure value proposition because cloud resources can be integrated into a wider Microsoft identity and security ecosystem.

The original article already highlighted Azure’s strong Microsoft identity integration and services such as Defender for Cloud, Azure Firewall, DDoS Protection, Sentinel, and Key Vault. Pasted markdown

Google Cloud Security Strengths

Google Cloud uses Cloud IAM for access management and provides services such as:

• Security Command Center
• Cloud KMS
• Secret Manager
• Cloud Armor
• Cloud Audit Logs

Security Command Center uses IAM roles to control access to findings and security assets at organization or project level. Google Cloud Documentation

Which Cloud Is Best for Security?

There is no universal winner.

AWS may be attractive when an organization needs a very broad set of infrastructure and security services.

Azure may have an operational advantage when an enterprise already depends heavily on Microsoft identity, endpoint, productivity, and security technologies.

Google Cloud can be attractive for organizations building cloud-native, data, AI, and globally distributed architectures.

The quality of the final security architecture still depends heavily on configuration.

A poorly configured cloud account can remain insecure regardless of provider.

Common failures include:

• Overly broad IAM permissions
• Publicly exposed storage
• Public databases
• Weak credential management
• Missing MFA
• Excessive firewall access
• Unmonitored administrative actions
• Missing backups
• Disabled logging

For the broader principles behind these controls, see Network Security Basics, Zero Trust Security, and Firewalls Explained.

3. AWS vs Azure vs Google Cloud Network Architecture

Networking is one of the clearest architectural differences between the three providers.

Although AWS, Azure, and Google Cloud all provide virtual networks, subnets, routing, firewalls, VPNs, DNS, load balancing, and private connectivity, their network resource models are not identical.

Virtual Network Scope

This distinction matters.

AWS VPC designs are built around AWS Regions, and subnets can be distributed across Availability Zones inside a Region. AWS documentation describes VPC network architecture in the context of individual Regions and multi-AZ subnet design. AWS Documentation

Azure Virtual Networks are also region-scoped. Microsoft documents that a VNet belongs to a single Azure region, although VNets in different regions can be connected using global peering and other connectivity services. Microsoft Learn

Google Cloud uses a different model: its VPC networks are global resources, while subnets are regional resources. Google Cloud Documentation

Why This Difference Matters

Imagine an application deployed across several geographic regions.

With AWS, architects commonly create regional VPC architectures and connect them when necessary.

With Azure, organizations may use multiple regional VNets connected through peering, Virtual WAN, VPN, or private connectivity.

With Google Cloud, one global VPC can contain subnets in multiple regions.

None of these approaches is automatically superior.

They simply influence:

• Network design
• IP address planning
• Routing
• Operational complexity
• Multi-region architecture
• Security boundaries

If cloud networking is new to you, read Computer Networking Explained first, followed by Cloud Networking Explained.

4. What Are AWS, Azure, and Google Cloud?

AWS, Azure, and Google Cloud are public cloud platforms that provide infrastructure and managed services over the internet.

Instead of purchasing physical servers, organizations can provision computing resources through software interfaces and APIs.

A traditional infrastructure could look like:

Users
  |
Internet
  |
Firewall
  |
Router
  |
Switch
  |
Physical Servers
  |
Storage

A cloud architecture may look like:

Users
  |
Internet
  |
Cloud Load Balancer
  |
Application Tier
  |
Managed Database
  |
Object Storage

The cloud provider operates the underlying physical infrastructure while the customer consumes virtual machines, managed databases, storage, networking, APIs, and other services.

Understanding Servers and Infrastructure Explained helps connect these cloud services to the physical infrastructure underneath them.

5. Amazon Web Services

AWS is Amazon’s public cloud platform and provides a broad collection of infrastructure and managed services.

Major areas include:

• Compute
• Networking
• Storage
• Databases
• Security
• Containers
• Kubernetes
• Serverless
• AI and machine learning
• Monitoring
• DevOps
• IoT
• Analytics

The official AWS Documentation provides detailed technical references for individual AWS services.

AWS Compute

Amazon EC2 provides virtual-machine compute.

Instances can be selected according to requirements such as:

• CPU
• RAM
• Storage
• Network performance
• Operating system
• Workload type

A common architecture might look like:

Internet
   |
Route 53
   |
CloudFront
   |
Application Load Balancer
   |
EC2 Instances
   |
RDS Database

Other AWS compute technologies include:

• ECS
• EKS
• Lambda
• Fargate
• Auto Scaling
• Elastic Beanstalk

AWS Storage

AWS provides several storage models.

Amazon S3

S3 is object storage commonly used for:

• Static assets
• Backups
• Images
• Video
• Logs
• Archives
• Data lakes

Amazon EBS

EBS provides block storage commonly attached to EC2 instances.

Amazon EFS

EFS provides managed shared file storage.

AWS Networking

The core network service is Amazon VPC.

A simplified design:

VPC
 |
 +-- Public Subnet
 |      |
 |      +-- Load Balancer
 |
 +-- Private Application Subnet
 |      |
 |      +-- EC2 / Containers
 |
 +-- Database Subnet
        |
        +-- Database

Common networking components include:

• VPC
• Subnets
• Route tables
• Internet Gateway
• NAT Gateway
• Security Groups
• Network ACLs
• Transit Gateway
• VPN
• Direct Connect
• Route 53

The original article already treated networking as a major part of AWS architecture. Pasted markdown

6. Microsoft Azure

Microsoft Azure is Microsoft’s public cloud platform.

It is particularly important in organizations already using Microsoft technologies.

Common Azure services include:

• Azure Virtual Machines
• App Service
• Azure Kubernetes Service
• Azure Functions
• Azure Storage
• Azure SQL
• Azure Virtual Network
• Microsoft Entra ID
• Azure Monitor
• Azure Firewall

The official Microsoft Azure documentation provides architecture and product guidance.

Azure Compute

Azure Virtual Machines provide IaaS compute.

Other application platforms include:

• App Service
• AKS
• Azure Functions
• Container Apps
• Virtual Machine Scale Sets

A simple application may look like:

Developer
   |
CI/CD
   |
Azure App Service
   |
Azure SQL

Azure Storage

Major storage options include:

• Blob Storage
• Managed Disks
• Azure Files

Blob Storage provides object storage.

Managed disks support virtual machines.

Azure Files provides managed file shares.

Azure Networking

Azure networking is centered around the Azure Virtual Network.

Azure VNet
 |
 +-- Frontend Subnet
 |      |
 |      +-- Application Gateway
 |
 +-- Application Subnet
 |      |
 |      +-- Application Servers
 |
 +-- Database Subnet
        |
        +-- Database

Common networking services include:

• Virtual Network
• Subnets
• Route tables
• Network Security Groups
• Azure Firewall
• Application Gateway
• Load Balancer
• VPN Gateway
• ExpressRoute
• Azure DNS
• Front Door

7. Google Cloud

Google Cloud is Google’s public cloud platform.

Its strengths are commonly associated with areas such as:

• Kubernetes
• Data analytics
• Artificial intelligence
• Machine learning
• Cloud-native workloads
• Global networking

The official Google Cloud documentation provides technical guidance across the platform.

Google Cloud Compute

Compute Engine provides virtual-machine infrastructure.

Other compute technologies include:

• Google Kubernetes Engine
• Cloud Run
• Cloud Functions
• App Engine

A simplified application might look like:

Users
  |
Cloud Load Balancing
  |
Cloud Run / GKE
  |
Cloud SQL
  |
Cloud Storage

Google Cloud Storage

Google Cloud Storage provides object storage for:

• Application assets
• Backup
• Archives
• Images
• Logs
• Data

Google Cloud also provides persistent block storage and managed file-storage options.

Google Cloud Networking

Google Cloud’s core networking platform is VPC.

Services include:

• VPC networks
• Regional subnets
• Cloud Load Balancing
• Cloud NAT
• Cloud VPN
• Cloud Interconnect
• Cloud DNS
• Cloud CDN
• Cloud Armor

Because Google VPC networks are global resources, their design model differs noticeably from AWS VPCs and Azure VNets.

8. AWS vs Azure vs Google Cloud Compute

All three platforms provide virtual-machine services.

At the fundamental level:

Application
    |
Compute
    |
Operating System / Runtime
    |
CPU + Memory
    |
Networking
    |
Storage

The choice should be based on workload behavior rather than service branding alone.

9. AWS vs Azure vs Google Cloud Storage

Cloud storage usually falls into three broad models.

Object Storage

Object storage is useful for:

• Media
• Backups
• Static files
• Logs
• Archives
• Data lakes

Block Storage

Block storage is commonly attached to virtual machines or applications requiring disk-like storage.

File Storage

File services provide shared filesystem-style access.

Architecture matters more than the product name.

For example, a database usually has very different latency and consistency requirements from an archive containing static images.

10. Database Comparison

All three cloud providers offer relational and non-relational managed databases.

AWS

Examples include:

• RDS
• Aurora
• DynamoDB
• Redshift
• ElastiCache

Azure

Examples include:

• Azure SQL
• Azure Database for PostgreSQL
• Cosmos DB
• Managed caching services

Google Cloud

Examples include:

• Cloud SQL
• AlloyDB
• Spanner
• Firestore
• Bigtable
• BigQuery

Relational vs NoSQL

A relational database organizes structured data into tables and relationships.

Customers
Orders
Products
Payments

NoSQL categories may include:

• Document
• Key-value
• Wide-column
• Graph

The correct choice depends on:

• Data model
• Query patterns
• Consistency
• Scalability
• Latency
• Operational requirements

11. Kubernetes: EKS vs AKS vs GKE

Managed Kubernetes services are:

A basic Kubernetes architecture:

Internet
   |
Load Balancer
   |
Kubernetes Cluster
   |
+--------+--------+--------+
| Pod    | Pod    | Pod    |
+--------+--------+--------+
          |
       Database

Google has deep historical experience with container orchestration, while all three providers now offer mature managed Kubernetes services.

For Kubernetes concepts and APIs, refer to the official Kubernetes documentation.

The important decision factors include:

• Cluster management
• Networking
• Identity
• Security
• Scaling
• Monitoring
• Integration with databases and storage
• Existing cloud expertise

12. Serverless Computing Comparison

Serverless platforms allow applications to run without administrators directly managing traditional servers.

A serverless architecture might be:

User
 |
API
 |
Function / Managed Runtime
 |
Database

Advantages can include:

• Automatic scaling
• Event-driven execution
• Lower infrastructure-management overhead
• Rapid deployment

Potential challenges include:

• Cold starts
• Distributed debugging
• Observability
• Cost unpredictability
• Provider-specific dependencies

13. Identity and Access Management

Identity is one of the most important layers in cloud security.

AWS

AWS primarily uses IAM to define users, roles, policies, and access to resources.

Azure

Azure commonly combines:

• Microsoft Entra ID
• Azure RBAC
• Managed identities
• Conditional Access

Google Cloud

Google Cloud uses Cloud IAM and a resource hierarchy built around organizations, folders, projects, and resources.

The conceptual flow remains:

Identity
   |
Authentication
   |
Authorization
   |
Resource

Authentication asks:

Who are you?

Authorization asks:

What are you allowed to do?

Organizations should apply least privilege rather than granting broad administrator permissions by default.

14. Shared Responsibility Model

Cloud providers do not take responsibility for every security decision made by a customer.

A simplified model is:

Cloud Provider
-------------------------
Data Centers
Physical Hardware
Core Cloud Infrastructure
Physical Security
-------------------------

Customer
-------------------------
Users and Accounts
Application Security
Data
Configuration
Permissions
Access Policies
-------------------------

The exact boundary changes depending on whether the service is:

• IaaS
• PaaS
• SaaS

With IaaS, customers typically manage more layers.

With highly managed services, the cloud provider manages more of the underlying platform.

That is why IaaS vs PaaS vs SaaS Explained is essential when evaluating cloud security responsibilities.

15. Cloud Networking Services Compared

The terminology varies, but the architectural functions are similar.

The concepts underlying these services—IP addressing, routing, DNS, NAT, firewalls, VPNs, and load balancing—remain important regardless of provider.

For those fundamentals, see TCP/IP Explained, DNS Explained, and Routers, Switches and Network Devices.

16. AWS vs Azure vs Google Cloud for DevOps

All three platforms support modern software-delivery workflows.

A typical process is:

Developer
   |
Git
   |
CI Pipeline
   |
Build
   |
Test
   |
Security Scan
   |
Artifact / Container
   |
Deployment
   |
Cloud Infrastructure

Common technologies include:

• Git
• GitHub
• GitLab
• Jenkins
• Terraform
• Ansible
• Docker
• Kubernetes

The official Docker documentation explains container workflows in detail.

DevOps architecture should focus on repeatability and automation rather than simply choosing a particular vendor’s pipeline tool.

17. Infrastructure as Code

Infrastructure as Code allows infrastructure to be represented in machine-readable configuration.

For example:

resource "aws_instance" "web" {
  ami           = "ami-example"
  instance_type = "t3.micro"
}

The architectural principle is:

Infrastructure Code
       |
Version Control
       |
Review / CI
       |
Provisioning
       |
Cloud Infrastructure
       |
Monitoring

Provider-native approaches include AWS CloudFormation and Azure Bicep, while Terraform is widely used across multiple cloud providers.

The official Terraform documentation provides syntax and provider guidance.

For the broader concept, read Infrastructure as Code Explained.

18. Cloud Monitoring

Production systems require monitoring.

AWS

CloudWatch can provide metrics, logs, alarms, and dashboards.

Azure

Azure Monitor provides monitoring and observability capabilities across infrastructure and applications.

Google Cloud

Cloud Monitoring and Cloud Logging provide metrics, logs, dashboards, and alerts.

A production monitoring strategy should consider:

CPU
Memory
Storage
Network
Latency
Errors
Request Rate
Availability
Security Events

Monitoring a single metric is rarely enough.

A server may have normal CPU usage while users cannot connect because DNS or a firewall rule is broken.

19. Observability

Observability extends beyond basic infrastructure monitoring.

It commonly combines:

• Metrics
• Logs
• Traces

For example:

User Request
     |
Load Balancer
     |
Application
     |
API
     |
Database

Distributed tracing can help determine which component is responsible for high latency.

Observability becomes particularly important in:

• Microservices
• Serverless
• Kubernetes
• Distributed systems

Security logs may also be centralized in SIEM platforms. For the security side of monitoring, see SIEM Explained.

20. AWS vs Azure vs Google Cloud Pricing

There is no universally cheapest cloud provider.

Total cloud cost can include:

Compute
+
Storage
+
Databases
+
Networking
+
Data Transfer
+
Managed Services
+
Monitoring
+
Backup
+
Support

A VM price alone does not describe the total cost of an architecture.

Organizations should also evaluate:

• Regional pricing
• Storage classes
• Backup
• Data transfer
• Cross-zone traffic
• Cross-region traffic
• Logging
• Managed databases
• Load balancing
• Discounts and commitments

The original article correctly emphasized that asking which platform is always cheapest is usually misleading because cost depends on architecture and workload. Pasted markdown

21. Pay-As-You-Go and Cost Control

Cloud resources can often be consumed on demand.

This provides flexibility but can also create uncontrolled spending.

Example:

Development VM
      |
Forgotten Resource
      |
Runs Continuously
      |
Unexpected Cost

Cost-management practices include:

• Budgets
• Alerts
• Tags
• Resource ownership
• Automated shutdown
• Rightsizing
• Capacity planning

Cloud cost optimization should therefore be part of architecture, not an activity performed only after bills become too high.

22. Regions and Availability Zones

Cloud infrastructure is geographically distributed.

A simplified model:

Region
 |
 +-- Availability Zone A
 |
 +-- Availability Zone B
 |
 +-- Availability Zone C

Using multiple failure domains can improve resilience.

Example:

                 Load Balancer
                 /           \
                /             \
             Zone A         Zone B
                |             |
           App Server    App Server
                \             /
                 \           /
                  Database

Regional architecture influences:

• Latency
• Availability
• Disaster recovery
• Data residency
• Compliance
• Cost

23. High Availability in the Cloud

A production architecture should avoid unnecessary single points of failure.

Avoid:

Load Balancer
     |
Single Server
     |
Single Database

Prefer redundancy where business requirements justify it:

            Load Balancer
            /           \
           /             \
       Server A        Server B
           \             /
            \           /
             Database

More advanced architectures may use:

                 Global DNS
                    |
             Global Traffic Layer
               /              \
              /                \
          Region A          Region B
          /     \            /     \
        AZ-A   AZ-B        AZ-A   AZ-B

High availability is not simply a cloud feature.

Applications, databases, networking, DNS, storage, and operational processes must all be considered.

24. Cloud Auto Scaling

Cloud platforms can automatically add or remove compute capacity according to workload conditions.

Examples include:

• AWS Auto Scaling
• Azure Virtual Machine Scale Sets and related scaling features
• Google Cloud Managed Instance Groups

Conceptually:

Low Traffic
   |
2 Instances

Traffic Increases
   |
Scaling Policy
   |
5 Instances

Scaling does not automatically solve inefficient application design.

Administrators still need to understand:

• Capacity
• Metrics
• Scaling thresholds
• Database bottlenecks
• Network limits
• Cost

For more detail, see Cloud Auto Scaling Explained.

25. Multi-Cloud Architecture

Multi-cloud means using services from more than one cloud provider.

For example:

                Users
                  |
              Global DNS
              /        \
             /          \
           AWS          Azure
            |             |
        Application    Application
             \           /
              \         /
               Data Layer

Potential advantages include:

• Workload specialization
• Regulatory flexibility
• Reduced dependence on one provider
• Business-continuity options

Potential disadvantages include:

• Greater complexity
• More skills required
• More security policies
• More monitoring systems
• More network complexity
• Higher operational overhead

Multi-cloud should therefore be an architectural decision, not a goal by itself.

26. Hybrid Cloud

Hybrid cloud combines public cloud resources with private or on-premises systems.

Public Cloud
     |
VPN / Dedicated Connectivity
     |
On-Premises Infrastructure
     |
Internal Systems

Hybrid environments remain important for organizations that have:

• Existing data centers
• Legacy applications
• Regulatory requirements
• Large Microsoft environments
• Data residency constraints
• Systems that cannot immediately move to public cloud

Dedicated connectivity services such as AWS Direct Connect, Azure ExpressRoute, and Google Cloud Interconnect can form part of these architectures.

27. AWS vs Azure vs Google Cloud: Which Is Best?

There is no universal winner.

Choose AWS When

AWS may be attractive when:

• You need a broad cloud service ecosystem.
• You require extensive infrastructure flexibility.
• AWS expertise already exists in the organization.
• Applications rely heavily on AWS-native services.
• Your architecture requires many infrastructure options.

Choose Azure When

Azure may be attractive when:

• Microsoft Entra ID is central to your identity strategy.
• Microsoft 365 is widely deployed.
• Windows Server remains important.
• Hybrid Microsoft infrastructure is required.
• Microsoft security and enterprise tools are already deeply integrated.

Choose Google Cloud When

Google Cloud may be attractive when:

• Kubernetes is strategically important.
• Data analytics is central to the business.
• AI and machine learning are major workloads.
• Global network architecture is important.
• Cloud-native application design is a priority.

The correct provider is the one that best matches the workload and organization, not necessarily the one with the largest service catalog.

28. AWS vs Azure vs Google Cloud Decision Matrix

This table represents general architectural tendencies rather than absolute rules.

Skills, existing contracts, application dependencies, regulations, and operational maturity can be more important than theoretical feature differences.

29. Cloud Provider Lock-In

Vendor lock-in occurs when an application becomes highly dependent on technologies that are difficult to replace.

For example:

Application
   |
Provider-Specific Database
   |
Provider-Specific Messaging
   |
Provider-Specific Functions

Migration may become complicated.

Portable technologies can sometimes reduce dependency:

• Linux
• Docker
• Kubernetes
• PostgreSQL
• Terraform
• Standard networking protocols

However, avoiding every provider-specific managed service can also increase operational complexity.

The goal should be intentional dependency, not zero dependency.

30. Cloud-Native vs Cloud-Hosted

These concepts are often confused.

Cloud-Hosted

A traditional application runs on cloud infrastructure.

Traditional Application
        |
      Cloud VM

Cloud-Native

An application is designed to use cloud capabilities.

Containers
    |
Kubernetes / Managed Runtime
    |
Managed Database
    |
Object Storage
    |
Load Balancing
    |
Observability

Cloud-native architectures can provide scalability and automation benefits, but they can also introduce more distributed-system complexity.

31. Virtualization and Cloud Computing

Cloud computing and virtualization are related but not identical.

Virtualization allows multiple virtual systems to share underlying hardware.

Cloud computing adds additional layers such as:

• APIs
• Automation
• Identity
• Managed databases
• Elasticity
• Software-defined networking
• Metered consumption
• Self-service provisioning

For the underlying infrastructure model, see Virtualization Explained.

32. Cloud Architecture Example

Consider a typical production web application:

                     INTERNET
                        |
                     DNS / CDN
                        |
                    Load Balancer
                        |
              +---------+---------+
              |                   |
         App Server A        App Server B
              |                   |
              +---------+---------+
                        |
                 Private Network
                        |
                 Database Cluster
                        |
                  Object Storage

A security layer may add:

Internet
   |
WAF
   |
Load Balancer
   |
Application
   |
Private Subnet
   |
Database

Monitoring adds another layer:

Applications
     |
Metrics + Logs + Traces
     |
Monitoring Platform
     |
Alerts
     |
Operations Team

A production architecture therefore cannot be evaluated by compute performance alone.

33. Common Cloud Architecture Mistakes

Exposing Databases Directly to the Internet

Avoid unnecessary exposure:

Internet
   |
Database

Prefer architectures where databases are reachable only from systems that actually require access:

Internet
   |
Application
   |
Private Database

Excessive Permissions

Avoid giving users and services full administrative access by default.

Use:

• Least privilege
• Role-based access
• MFA
• Temporary credentials where appropriate
• Service identities

Assuming Cloud Means Backup

Cloud infrastructure does not automatically eliminate backup requirements.

You still need:

• Backup policies
• Retention
• Recovery procedures
• Restore tests

Ignoring Network Costs

Network architecture can affect cost.

Consider:

• Internet egress
• Cross-region traffic
• Cross-zone traffic
• CDN design
• Database traffic

No Governance

Large environments require:

• Naming standards
• Tags
• Accounts / subscriptions / projects
• Security policies
• Budgets
• Logging
• Ownership

These mistakes were already identified as important operational risks in the original article. Pasted markdown

34. Cloud Security Checklist

Before deploying production infrastructure, verify the following.

Identity

• MFA is configured where appropriate.
• Administrative access is protected.
• Least privilege is implemented.
• Service identities are controlled.
• Unused credentials are removed.

Networking

• Private resources are not unnecessarily public.
• Firewall policies have been reviewed.
• Unnecessary ports are closed.
• Segmentation is appropriate.
• Routing is documented.

Data

• Encryption requirements are defined.
• Backups are configured.
• Secrets are stored securely.
• Sensitive data is classified.
• Restore procedures are tested.

Monitoring

• Audit logging is enabled.
• Application logs are available.
• Infrastructure metrics are collected.
• Security alerts are configured.
• Important changes are traceable.

Availability

• Failure scenarios are documented.
• Critical services have appropriate redundancy.
• Disaster recovery has been planned.
• Backups have been tested.

35. How to Compare Cloud Providers Correctly

Do not compare cloud providers using only VM price.

A better framework is:

Architecture
+
Security
+
Networking
+
Performance
+
Managed Services
+
Developer Experience
+
Skills
+
Compliance
+
Support
+
Total Cost

One provider might have cheaper compute for a particular workload but higher total costs once networking, managed databases, monitoring, backups, and operational requirements are included.

A provider can also be technically excellent but operationally inappropriate if the organization does not have the required skills.

36. Cloud Networking Skills Remain Essential

Cloud infrastructure is still networked infrastructure.

Cloud engineers should understand:

• IP addressing
• Subnets
• Routing
• DNS
• NAT
• Firewalls
• VPNs
• Load balancing
• TLS
• Segmentation

For example:

Application
    |
Private Subnet
    |
Route Table
    |
NAT / Firewall
    |
Internet

The interface may be software-defined, but the networking principles remain.

This is why Cloud Networking Explained and Network Security Basics are useful companions to this comparison.

37. Troubleshooting Cloud Connectivity

When a cloud application cannot connect to a service, troubleshoot systematically instead of immediately changing firewall rules.

Step 1: Check DNS

Linux examples:

nslookup example.com

or:

dig example.com

Step 2: Test Connectivity

Where appropriate:

ping example.com

Remember that some systems intentionally block ICMP, so ping failure alone does not prove the application is unreachable.

Step 3: Check Routing

On Linux:

ip route

You may also inspect path behavior with tools such as:

traceroute example.com

Step 4: Test the Required Port

Example:

nc -vz example.com 443

Step 5: Check Application Dependencies

Review:

• DNS
• Route tables
• Firewall rules
• Security groups
• Network ACLs
• Application logs
• Database configuration
• TLS
• Identity policies

The Linux Network Tools Guide provides a deeper troubleshooting workflow.

38. Example: Private Cloud Application Failure

Suppose:

Internet
   |
Load Balancer
   |
Application
   |
Database

The application cannot connect to the database.

Check in order:

1. Is the database running?
2. Is DNS resolving correctly?
3. Is routing correct?
4. Is the application using the right address?
5. Is the database listening on the required port?
6. Do firewall policies permit the connection?
7. Is authentication valid?
8. Is TLS configured correctly?
9. Is a security policy blocking access?

Do not immediately assume that the database service itself is broken.

A structured troubleshooting method helps isolate the failed layer.

39. AWS vs Azure vs Google Cloud for Beginners

Beginners should avoid trying to memorize every service.

A stronger learning path is:

Networking
    |
Linux
    |
Virtualization
    |
Cloud Fundamentals
    |
AWS / Azure / Google Cloud
    |
Containers
    |
Kubernetes
    |
Infrastructure as Code
    |
DevOps
    |
Cloud Security

Start with:

1. IP addressing
2. DNS
3. TCP/IP
4. Routing
5. Linux
6. Virtual machines
7. Cloud networking
8. IAM
9. Storage
10. Databases
11. Containers
12. Kubernetes
13. Infrastructure as Code

The Linux Server Administration and DNS Explained guides provide useful technical foundations.

40. Recommended Learning Lab 1: Simple Web Server

Build:

Internet
   |
Cloud VM
   |
Nginx

Learn:

• Linux
• SSH
• IP addressing
• Firewall configuration
• HTTP
• DNS

This lab builds the bridge between basic server administration and cloud infrastructure.

41. Recommended Learning Lab 2: Secure Web Architecture

Build:

Internet
   |
Load Balancer
   |
Private Application
   |
Private Database

Learn:

• Subnets
• Routing
• Security rules
• Load balancing
• Database isolation
• IAM

Then recreate the same architecture on a second cloud provider to understand how similar concepts are implemented differently.

42. Recommended Learning Lab 3: Containerized Application

Build:

Developer
   |
Docker
   |
Container Registry
   |
Kubernetes / Managed Container Platform
   |
Database

Learn:

• Containers
• Image registries
• Kubernetes
• Cloud networking
• CI/CD
• Monitoring

This is particularly useful because cloud-native environments combine application, networking, identity, and infrastructure concepts.

43. Frequently Asked Questions

Is AWS better than Azure?

Not universally. AWS may suit organizations requiring a broad infrastructure ecosystem, while Azure can be especially attractive to enterprises heavily invested in Microsoft technologies.

Is Azure better than AWS for Microsoft environments?

Azure often provides natural integration with Microsoft Entra ID, Microsoft 365, Windows-based environments, and other Microsoft enterprise technologies.

Is Google Cloud better for Kubernetes?

Google Cloud has strong Kubernetes capabilities through GKE, but AWS EKS and Azure AKS are also mature managed Kubernetes platforms.

Which is more secure: AWS, Azure, or Google Cloud?

All three offer extensive enterprise security capabilities. Real-world security depends heavily on IAM policies, network design, secrets management, logging, application security, patching, and operational processes.

What is the biggest networking difference between AWS, Azure, and Google Cloud?

AWS VPC and Azure VNet architectures are region-oriented, while Google Cloud VPC networks are global resources with regional subnets. AWS Documentation

Is Azure cheaper than AWS?

There is no universal answer. Pricing depends on workload, region, commitments, architecture, storage, networking, managed services, and traffic patterns.

Which cloud should a beginner learn first?

Any of the three can be a good starting point. Choose based on your target jobs, existing environment, training resources, and organizational technology stack.

Should I learn all three cloud providers?

Understanding all three can be valuable, but learning one deeply before attempting deep multi-cloud expertise is usually more practical.

Can Terraform manage AWS, Azure, and Google Cloud?

Yes. Terraform supports infrastructure management across multiple providers.

Is cloud computing the same as virtualization?

No. Virtualization is one technology used to abstract computing resources. Cloud computing adds automation, APIs, managed services, networking, identity, self-service provisioning, elasticity, and usage-based service models.

Which cloud provider is best for data analytics?

Google Cloud has strong data and analytics offerings, but AWS and Azure also provide extensive analytics ecosystems. Workload requirements should determine the final decision.

Which cloud provider has the best global networking?

All three operate large global networks. Google Cloud’s global VPC model is architecturally distinctive, while AWS and Azure use region-scoped network resources connected through their respective global connectivity services.

Can an organization use AWS, Azure, and Google Cloud together?

Yes, but multi-cloud increases operational, security, networking, monitoring, and skills complexity. It should have a clear business or technical justification.

44. Final Comparison

AWS, Azure, and Google Cloud are all capable of supporting anything from a small application to complex enterprise infrastructure.

AWS stands out for the breadth of its service ecosystem and infrastructure options.

Azure is particularly compelling for organizations deeply invested in Microsoft’s identity, productivity, server, and security ecosystems.

Google Cloud is especially attractive for Kubernetes, data, AI, and globally distributed cloud-native architectures.

However, cloud expertise should not be reduced to memorizing provider product names.

A strong cloud engineer understands:

Networking
+
Linux
+
Security
+
Virtualization
+
Storage
+
Databases
+
Containers
+
Automation
+
Observability

Once those fundamentals are understood, AWS, Azure, and Google Cloud become different implementations of many familiar infrastructure concepts.

Conclusion

The comparison of AWS vs Azure vs Google Cloud should not be reduced to a simple question of which provider is the biggest, cheapest, or most popular.

The correct choice depends on:

• Architecture
• Networking
• Security
• Identity
• Performance
• Managed services
• Existing technology investments
• Developer experience
• Operational skills
• Compliance
• Availability
• Total cost of ownership

For organizations prioritizing cloud security, the comparison should focus on IAM design, network isolation, secrets management, threat detection, logging, encryption, and the shared responsibility model.

For organizations prioritizing networking, the architecture of VPCs, VNets, regional subnets, routing, private connectivity, VPNs, DNS, and global traffic becomes particularly important.

The most effective approach is therefore to learn the infrastructure principles first and then map them to each cloud provider.

Continue with Cloud Computing Explained for the broader cloud model, Cloud Networking Explained for VPCs, subnets and routing, and Infrastructure as Code Explained for automated infrastructure deployment.

The core principle is simple:

AWS, Azure, and Google Cloud provide different implementations of the same fundamental infrastructure problems. The best cloud platform is the one whose architecture, security model, networking, services, skills, and cost structure best match the workload you actually need to run.