2025 • Beginner Friendly

Top Cloud Platforms Compared: AWS vs Azure vs Google Cloud (2025)

By ITTechGuide

Comprehensive comparison and practical guidance to choose your cloud path, build real portfolio projects, optimize costs, and prepare for certifications in 2025.

Contents

• Quick recommendation
• Market snapshot & trends (2024–2025)
• Deep technical comparison: compute, storage, networking, ML & analytics
• Pricing best practices & cost optimization
• Certifications, hiring signals, and what employers actually look for
• 4 step-by-step portfolio projects (full code snippets & CI/CD)
• 60-day learning plan, decision checklist, and interview prep tips

Quick recommendation — pick a starting cloud

If you want jobs fast

Start with AWS. Largest market share, broadest services, and the biggest ecosystem for third-party tools and community examples.

If you target enterprise customers

Start with Azure for hybrid solutions and Microsoft-centric environments (Active Directory, Azure AD, Microsoft 365 integrations).

If your focus is analytics or ML

Start with GCP. BigQuery's serverless analytics and Vertex AI's integrated tooling make prototyping and analytics faster in many workflows.

Note: Employers value demonstrable projects and problem-solving ability more than a long list of certificates. Build 2–3 real projects and document the architecture, costs, and CI/CD process.

Market snapshot & trends (2024–2025)

Estimated 2025 market share (region & methodology dependent): AWS ~30–33%, Azure ~24–27%, GCP ~10–12%. These percentages are approximate — the important takeaway is relative reach and enterprise penetration.

Key trends shaping hiring and product direction:

• AI + ML integration: Cloud vendors embed model hosting, data pipelines, and MLOps primitives into their platforms. Expect more managed model services and feature stores.
• Serverless + containers hybrid: Serverless functions remain dominant for event-driven tasks, while serverless containers (Cloud Run, Fargate) and Kubernetes handle steady workloads.
• Infrastructure as Code (IaC): Terraform, Pulumi, and native IaC offerings are standard in production deployments — knowing at least one matter to employers.
• Cost observability: Cloud cost management tools are getting richer; engineers who can estimate and optimize cost win interviews.

Pro tip: focus on cloud fundamentals first (IAM, VPC, DNS, storage tiers, CI/CD). Those transfer across clouds and reduce onboarding time when switching vendors.

Deep comparison — compute, storage, networking, ML & analytics

Compute

AWS: EC2 provides the widest instance catalog. Lambda is mature for serverless functions. ECS/EKS for containers. AWS Graviton (ARM) instances give cost/perf advantages for certain workloads.

Azure: Virtual Machines + Azure App Service for PaaS; Functions for serverless; AKS for Kubernetes. Tight integration with Windows Server and Active Directory is a strength for enterprise workloads.

GCP: Compute Engine (VMs), App Engine for PaaS, Cloud Functions for event-driven workloads, and GKE for Kubernetes — GKE is often praised for developer ergonomics and managed upgrades.

Storage & databases

Each cloud has similar building blocks but different managed services and operational trade-offs:

• AWS: S3 for object storage (very mature), EBS for block volumes, RDS/Aurora for relational, DynamoDB for fast NoSQL, Redshift for warehousing.
• Azure: Blob storage, Managed Disks, Azure SQL Database, Cosmos DB for multi-model NoSQL (globally distributed), Synapse for analytics.
• GCP: Cloud Storage, Persistent Disk, Cloud SQL / Spanner for horizontally scalable relational workloads, Firestore for serverless NoSQL, BigQuery for serverless analytics.

Networking & global footprint

AWS typically leads in raw region/availability zone count, Azure has deep enterprise regional coverage, and GCP boasts a high-performance private backbone (useful for data-heavy workloads). For production systems, design for multi-AZ resilience and prefer regions closest to your users.

AI / Analytics / ML

GCP: BigQuery is a fast, serverless analytics engine that reduces ops overhead. Vertex AI streamlines training, deployment, and MLOps. AWS: SageMaker plus a broad ecosystem for data prep, feature stores, and inference. Azure: Azure Machine Learning integrates well with enterprise data and M365 data sources.

Example decision: choose GCP for heavy analytics-first projects; choose AWS for general-purpose enterprise apps; choose Azure if you're operating in Microsoft-dominant organizations.

Pricing & cost optimization (practical)

Cloud pricing structures are intentionally complex. Instead of memorizing prices, learn patterns and controls that let you keep costs predictable:

Concept	AWS	Azure	GCP
Free tier / credits	12 months + always-free services	Free credits for new accounts + always-free	$300 new-user credit + always-free
Discounts	Reserved Instances, Savings Plans	Reserved VM Instances, Azure Hybrid Benefit	Committed Use, Sustained Use discounts
Spot/Preemptible	Spot Instances (interruptible)	Spot VMs	Preemptible VMs (very low cost)
Cost tools	AWS Cost Explorer, Budgets	Azure Cost Management	GCP Pricing Calculator, Billing alerts

Actionable cost rules

1. Always set budgets/alerts immediately (don't rely on memory).
2. Use tagging to attribute costs to projects and avoid orphaned resources.
3. Prefer serverless or preemptible instances for experiments to reduce spend.
4. Automate teardown for ephemeral environments (CI test clusters, labs).
5. Measure — don’t guess. Use cost analysis tools and track changes per commit or deployment.

Practical tip: For learning and portfolio work, use free tiers and turn off resources when not actively testing. Use cloud lab platforms (Qwiklabs, Cloud Skills Boost) to practice without high costs.

Certifications, hiring signals & career advice

Certifications help automated resume filters and can demonstrate baseline knowledge — but real projects and good documentation are what get you interviews.

AWS

• AWS Certified Cloud Practitioner → Solutions Architect (Associate) → Specialty certs (Security, Data, ML).
• Show practical work: EC2, S3, Lambda, IAM, and Terraform deployments.

Azure

• AZ-900 (Fundamentals) → Administrator / Solutions Architect tracks.
• Focus on hybrid scenarios: Azure AD, ExpressRoute, and Windows Server migrations.

GCP

• Cloud Digital Leader → Associate Cloud Engineer → Professional Data Engineer.
• Show BigQuery projects and Vertex AI prototypes for data roles.

What hiring teams look for

• Clear, working projects with README, architecture diagrams, and cost notes.
• Understanding of IAM, networking fundamentals, and secure defaults.
• Automation: Terraform, CI/CD for repeatable deployments.
• Observability basics: structured logging, metrics, and alerting.

Portfolio projects — detailed, step-by-step (4 projects)

Below are four projects designed to show a breadth of cloud skills. For each, include a GitHub repo, README with architecture diagram, deployment steps, cost summary, and teardown instructions.

Project 1 — Static Portfolio Website (S3 / Blob / Cloud Storage)

Goal: Host a static portfolio site on object storage with CDN and HTTPS. Deliverable: live site (custom domain), README, and CI-based deploy.

Why build this?

It demonstrates DNS, TLS, object storage, CDN, and automation — all are essential entry-level cloud skills. Low risk and low cost.

Prerequisites

• Cloud account (AWS/Azure/GCP) and CLI configured
• Domain name with DNS control
• Static site files (index.html, CSS, assets)

Step-by-step (AWS example)

# 1. create an S3 bucket (replace BUCKET & REGION)
aws s3api create-bucket --bucket your-portfolio-bucket-12345 --region us-east-1 --create-bucket-configuration LocationConstraint=us-east-1

# 2. upload site
aws s3 sync ./site s3://your-portfolio-bucket-12345 --acl public-read

# 3. request ACM certificate (for CloudFront)
aws acm request-certificate --domain-name example.com --validation-method DNS --region us-east-1

# 4. create CloudFront distribution to serve bucket with HTTPS (use OAI/origin access identity)
# use console or IaC (CloudFormation/Terraform) for production deployments

CI (GitHub Action snippet)

name: Deploy Static Site
on:
  push:
    branches: [ main ]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: jakejarvis/s3-sync-action@master
        with:
          args: --acl public-read --delete
        env:
          AWS_S3_BUCKET: ${{ secrets.S3_BUCKET }}
          AWS_REGION: 'us-east-1'
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

Teardown

# remove site content
aws s3 rm s3://your-portfolio-bucket-12345 --recursive
# delete bucket
aws s3api delete-bucket --bucket your-portfolio-bucket-12345 --region us-east-1

Include a README explaining cost (approx. $0–$3/month for low traffic), DNS changes, and how to rotate certificates.

Project 2 — Serverless REST API (Lambda / Functions / Cloud Functions)

Goal: Build a small REST API (Node.js or Python) using serverless functions, API Gateway, and a managed NoSQL DB. Add simple JWT authentication and CI deployment. Deliverable: documented endpoints, tests, and cost notes.

Why build this?

Shows event-driven architecture, stateless functions, serverless DB patterns, and production-enablement via CI and secret management.

Design & architecture

1. API Gateway routes HTTP requests to functions.
2. Functions perform business logic and interact with a managed NoSQL DB (DynamoDB / Firestore / Cosmos).
3. Authentication via a login function issuing JWTs; for production prefer managed identity providers (Cognito, Identity Platform, Azure AD).
4. Integration tests run in CI using emulator or a dedicated test environment with limited resources.

Sample minimal Node.js Lambda

// handler.js
const AWS = require('aws-sdk');
const dynamo = new AWS.DynamoDB.DocumentClient();
exports.handler = async (event) => {
  try {
    if (event.httpMethod === 'POST') {
      const body = JSON.parse(event.body);
      const id = Date.now().toString();
      await dynamo.put({ TableName: 'items', Item: { id, ...body } }).promise();
      return { statusCode: 201, body: JSON.stringify({ id }) };
    }
    if (event.httpMethod === 'GET') {
      const data = await dynamo.scan({ TableName: 'items' }).promise();
      return { statusCode: 200, body: JSON.stringify(data.Items) };
    }
    return { statusCode: 405, body: 'Method not allowed' };
  } catch (err) {
    return { statusCode: 500, body: JSON.stringify({ error: err.message }) };
  }
};

Local testing & emulators

Use local emulators to test without cloud costs: DynamoDB Local, Firebase emulators, or Azure Storage emulator. Use serverless-offline for Lambda development locally.

CI/CD pipeline (GitHub Actions example)

name: Deploy Serverless API
on: push
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup Node
        uses: actions/setup-node@v3
        with:
          node-version: '18'
      - name: Install
        run: npm ci
      - name: Run tests
        run: npm test
      - name: Package & deploy
        run: |
          zip -r function.zip .
          aws lambda update-function-code --function-name my-api-fn --zip-file fileb://function.zip
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

Security & secrets

• Never commit secrets. Use Secret Manager / Key Vault / Parameter Store.
• Set minimal IAM permissions for the function role (least privilege).
• Rate-limit endpoints and enable WAF or API throttling for public endpoints.

Cost considerations

Serverless is cost-effective for spiky or low-volume workloads. Monitor lambda duration & memory settings and use Provisioned Concurrency if latency matters (but weigh cost).

Project 3 — Containerized App + CI/CD (GCR/ECR/ACR + GKE/EKS/AKS)

Goal: Containerize an app, push to a container registry, deploy to managed Kubernetes, add Helm chart or k8s manifests, and implement a CI/CD pipeline. Deliverable: running service, autoscaling, monitoring, and README.

Why build this?

Kubernetes + CI/CD is standard for production engineering roles. This project demonstrates container builds, image registries, manifests, and automated deploy pipelines.

Sample Dockerfile (Node app)

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
ENV PORT=8080
CMD ["node","server.js"]

Build & push (GCP example)

# authenticate
gcloud auth configure-docker
# build & push image
docker build -t gcr.io/YOUR_PROJECT_ID/myapp:v1 .
docker push gcr.io/YOUR_PROJECT_ID/myapp:v1

Deploy to cluster

# assumes kubectl already configured for the cluster
kubectl create deployment myapp --image=gcr.io/YOUR_PROJECT_ID/myapp:v1
kubectl expose deployment myapp --type=LoadBalancer --port=80 --target-port=8080
# add HPA (Horizontal Pod Autoscaler)
kubectl autoscale deployment myapp --cpu-percent=50 --min=1 --max=5

CI/CD (GitHub Actions simplified)

name: Build and Deploy
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Build and push image
        run: |
          docker build -t gcr.io/${{ secrets.GCP_PROJECT }}/myapp:${{ github.sha }} .
          docker push gcr.io/${{ secrets.GCP_PROJECT }}/myapp:${{ github.sha }}
        env:
          GCP_PROJECT: ${{ secrets.GCP_PROJECT }}
          # auth handled via service account key secret or OIDC
  deploy:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to GKE
        run: |
          kubectl set image deployment/myapp myapp=gcr.io/${{ secrets.GCP_PROJECT }}/myapp:${{ github.sha }}

Observability & best practices

• Use liveness/readiness probes to detect unhealthy pods.
• Set resource requests/limits to control bin-packing and avoid noisy neighbors.
• Integrate logging and tracing: CloudWatch/Stackdriver/Application Insights/OpenTelemetry.
• Use Infrastructure as Code (Terraform/Helm) so your cluster is reproducible.

Terraform snippet (GKE cluster)

provider "google" {
  project = var.project_id
  region  = var.region
}
resource "google_container_cluster" "primary" {
  name     = "example-cluster"
  location = var.region
  initial_node_count = 1
  node_config {
    machine_type = "e2-medium"
  }
}

Cost & environment

Use small node pools for dev/testing and autoscaling. Use node auto-provisioning and spot instances for cost saving during non-critical workloads.

Project 4 — Data pipeline & analytics (ETL → Warehouse → Dashboard)

Goal: Ingest data into object storage, run serverless ETL, load into a warehouse, and build a dashboard. Deliverable: documented pipeline, queries, and dashboard link.

Suggested stack

• Storage: S3 / Blob / Cloud Storage
• ETL: Glue / Dataflow / Data Factory
• Warehouse: Redshift / Synapse / BigQuery
• Dashboard: QuickSight / Power BI / Data Studio

High-level steps

1. Ingest and stage raw CSV/JSON files in object storage.
2. Run a serverless job to transform & clean data (Dataflow/Glue).
3. Load aggregated tables into the warehouse.
4. Create dashboards and document cost & query performance trade-offs.

Include SQL examples, sample query performance numbers, and a short note about data retention and partitioning strategy.

60-day learning plan — focused & timeboxed

Days 1–10: Foundations

• Spin up a free account; enable billing alerts and set a small budget cap.
• Complete a vendor's fundamentals course (Cloud Practitioner, AZ-900, or Cloud Digital Leader).
• Practice IAM policies, create a VPC/network, and host a simple static site.

Days 11–30: Build two beginner projects

• Project 1: Static portfolio site (host on S3/Cloud Storage + CDN).
• Project 2: Serverless API with a NoSQL backend and CI tests.
• Document both: README, architecture diagram, costs, and teardown steps.

Days 31–60: Intermediate project & certification

• Project 3: Containerized app on Kubernetes + CI/CD and IaC (Terraform).
• Review and take a fundamentals exam and share certificates on LinkedIn/GitHub.
• Start a small data/ML prototype (Project 4) to diversify skills.

Pro tip: Aim to deploy all projects in a way that can be reproduced by anyone from your repo (IaC + clear README). Employers often ask for walk-throughs; practice explaining your architecture in 5–10 minutes.

Readable feature table & decision checklist

Category	AWS	Azure	GCP
Market strength	Largest global footprint & ecosystem	Enterprise + hybrid adoption	Data & ML leadership
Best for	General-purpose enterprise & startups	Microsoft stack & hybrid	Data engineering & ML
Serverless	Lambda (mature)	Functions (integrated)	Cloud Functions / Cloud Run
Kubernetes	EKS	AKS	GKE
Data warehouse	Redshift	Synapse	BigQuery
Free tier	12 months + always-free	Free credits + always-free	$300 new-user credit + always-free

Decision checklist

1. Which projects do you plan to build (web app, serverless API, containerized app, data pipeline)?
2. Which employers or industries are you targeting (enterprise vs startup vs data teams)?
3. Do you need hybrid/on-prem integration?
4. Pick one cloud, finish 3 thorough projects, then add a second cloud for breadth.

FAQ

Do I need to learn all three clouds?

No — mastery of one cloud with practical projects is far more valuable than shallow knowledge of all three. Learn vendor-agnostic skills and then pick one additional vendor later if needed.

What should I show on my resume?

Show 3–4 projects with GitHub links, architecture diagrams, cost notes, and a short video/walkthrough or README that explains decisions and trade-offs.

Final thoughts

All three clouds are excellent. Choose based on the work you want to do, build practical projects, document them well, and focus on transferable skills like IaC, CI/CD, security, and observability.

© 2025 — ITTechGuide

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• Virtualization in IT & Getting Started
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