AWS vs GCP vs Azure: How to Choose Your Cloud Provider

Prologue: Aren't All Clouds the Same?

When I started my first project, choosing a cloud provider seemed trivial. EC2, Compute Engine, Virtual Machine—they're all just servers, right? AWS is the biggest, so I'll go with AWS.

But comparing the characteristics of each cloud, they're completely different. Just like "cars" can be sports cars, SUVs, or sedans, each cloud is built with an entirely different philosophy.

AWS billing is complex given its breadth of services, GCP is innovative but has a history of deprecating services, and Azure's documentation is enterprise-oriented in ways that feel unfamiliar to solo developers or small teams — these are common experiences you hear about.

I came to understand: choosing a cloud isn't about "which is better?" but "which fits our situation?"

---

AWS: The Department Store

AWS feels like a department store. They have everything. Over 200 services, thousands of new features added every year.

The Comfort of the Ecosystem

My first impression of AWS was "oh, I can Google everything." Every error has a Stack Overflow answer, every use case already has a blog post.

// AWS SDK literally has clients for every service
import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3";
import { DynamoDBClient, PutItemCommand } from "@aws-sdk/client-dynamodb";
import { SQSClient, SendMessageCommand } from "@aws-sdk/client-sqs";
import { LambdaClient, InvokeCommand } from "@aws-sdk/client-lambda";

// You can solve almost everything within AWS
const s3 = new S3Client({ region: "ap-northeast-2" });
await s3.send(new PutObjectCommand({
  Bucket: "my-bucket",
  Key: "uploads/image.jpg",
  Body: fileBuffer
}));

This gave me tremendous confidence. Whatever feature I needed, I knew "AWS probably has it."

But the Bill is a Maze

The problem was pricing. AWS bills are like mazes. EC2 charges, EBS charges, data transfer charges, NAT Gateway charges... dozens of line items.

# Example AWS bill (small service, could easily reach $300-400/mo)
# EC2 instances: ~$52
# EBS volumes: ~$18
# Data transfer (outbound): ~$127  ← This is the killer
# NAT Gateway: ~$45
# RDS: ~$63
# S3: ~$12
# CloudWatch Logs: ~$8
# Other: ~$22

# Key lesson: Watch out for data transfer costs

I learned: AWS is "easy to enter, hard to optimize."

When to Choose AWS

• When you have many enterprise customers requiring VPC Peering or PrivateLink
• When integrating with legacy systems (almost every service supports AWS integration)
• When your team already has AWS experience
• When stability is paramount (services rarely get deprecated)

---

GCP: The Developer's Playground

GCP felt completely different. Like Google saying "here, use the tools we use internally."

BigQuery's Magic

The main reason I chose GCP was BigQuery. Data analysis became incredibly easy.

-- Analyze billions of log entries in seconds
SELECT
  user_id,
  COUNT(*) as events,
  APPROX_QUANTILES(session_duration, 100)[OFFSET(50)] as median_duration
FROM `project.dataset.events`
WHERE DATE(timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY user_id
HAVING events > 10
ORDER BY events DESC
LIMIT 1000;

-- This runs in 3 seconds. Even on terabytes of data.

AWS has Athena and Redshift, but BigQuery's speed and convenience were in a different league. The pricing model was simple: pay only for the data you scan.

Perfect Firebase Integration

If you're using Firebase, GCP is the natural choice.

// Firebase + GCP Cloud Functions combo
import * as functions from "firebase-functions";
import { BigQuery } from "@google-cloud/bigquery";

export const onUserSignup = functions.auth.user().onCreate(async (user) => {
  // Save Firebase Auth events directly to BigQuery
  const bigquery = new BigQuery();
  await bigquery.dataset("analytics").table("user_signups").insert([{
    user_id: user.uid,
    email: user.email,
    created_at: new Date().toISOString()
  }]);
});

// Firebase, Cloud Functions, BigQuery work as one ecosystem

But There's Instability

The problem was Google's "sunset" culture. Google Cloud IoT Core suddenly announced shutdown, and we had to migrate urgently.

The lesson: GCP is great for teams wanting cutting-edge tech and innovation, but less reassuring if you need long-term stability.

When to Choose GCP

• When data analysis is a core feature (BigQuery is a genuine game-changer)
• When using Kubernetes (GKE is the best implementation)
• When already using Firebase
• When developer experience matters (most intuitive console UI)

---

Azure: The Cloud That Speaks Enterprise

Azure was confusing at first. Coming from AWS or GCP, Azure's terminology and structure felt foreign.

Perfect Fit with Microsoft Ecosystem

But working with B2B customers taught me Azure's value. Especially Active Directory integration was essential in enterprise environments.

// SSO through Azure AD
import { DefaultAzureCredential } from "@azure/identity";
import { SecretClient } from "@azure/keyvault-secrets";

// Automatic authentication with company AD account
const credential = new DefaultAzureCredential();
const client = new SecretClient(
  "https://my-vault.vault.azure.net",
  credential
);

// Enterprise customers love this
// Access cloud resources with existing AD accounts

When a large enterprise customer asked "We only use Azure, can you deploy on Azure?", supporting Azure became a contract requirement.

Paradise for .NET Developers

If you develop in .NET, Azure is the obvious choice.

// Azure Functions with C#
[FunctionName("ProcessOrder")]
public static async Task Run(
    [QueueTrigger("orders")] Order order,
    [Table("Orders")] IAsyncCollector<OrderEntity> orderTable,
    [SendGrid] IAsyncCollector<SendGridMessage> messages,
    ILogger log)
{
    // Queue, Table Storage, SendGrid all connected via bindings
    await orderTable.AddAsync(new OrderEntity(order));
    await messages.AddAsync(CreateConfirmationEmail(order));
}

Press F5 in Visual Studio for local debugging, one-click deployment.

When to Choose Azure

• When targeting B2B/enterprise customers
• When using .NET stack
• When integration with Microsoft 365, Teams is needed
• When your company is already in Microsoft ecosystem

---

Real Comparison: Category Winners

Compute

Service	AWS	GCP	Azure
VM	EC2	Compute Engine	Virtual Machines
Serverless	Lambda	Cloud Functions	Functions
Container	ECS/EKS	GKE	AKS

Winner: GCP (Kubernetes), AWS (versatility)

Database

Type	AWS	GCP	Azure
Relational	RDS	Cloud SQL	Database for MySQL/PostgreSQL
NoSQL	DynamoDB	Firestore	Cosmos DB
Analytics	Redshift	BigQuery	Synapse

Winner: GCP (BigQuery dominates)

Serverless

// All three are similar but developer experience differs

// AWS Lambda - most mature
export const handler = async (event: APIGatewayEvent) => {
  return { statusCode: 200, body: JSON.stringify({ ok: true }) };
};

// GCP Cloud Functions - most concise
export const hello = (req: Request, res: Response) => {
  res.json({ ok: true });
};

// Azure Functions - best for .NET developers
[FunctionName("Hello")]
public static IActionResult Run([HttpTrigger] HttpRequest req) {
    return new OkObjectResult(new { ok = true });
}

Winner: AWS (ecosystem), GCP (simplicity), Azure (.NET environment)

AI/ML

• AWS: SageMaker - most features, but complex
• GCP: Vertex AI - direct access to Google's AI tech, powerful AutoML
• Azure: Azure ML - familiar to .NET developers, OpenAI partnership

Winner: depends on use case (generally GCP)

---

Pricing: The Devil is in the Details

Free Tier Comparison

AWS Free Tier: 12-month limited + always free

• EC2: t2.micro 750 hours/month (12 months)
• S3: 5GB (12 months)
• Lambda: 1M requests/month (always free)

GCP Free Tier: Always Free + $300 credit (90 days)

• Compute Engine: e2-micro (always free in specific regions)
• Cloud Functions: 2M requests/month (always free)
• BigQuery: 1TB queries/month (always free)

Azure Free Tier: $200 credit (30 days) + 12-month free + always free

• Virtual Machine: B1S 750 hours/month (12 months)
• Functions: 1M requests/month (always free)

Startup Credits

This was crucial. If you're an early-stage startup, definitely leverage credit programs.

• AWS Activate: up to $100,000 (specific accelerators like YC)
• GCP for Startups: up to $100,000
• Azure for Startups: up to $150,000

We survived a year on $5,000 GCP credits.

---

Vendor Lock-in: Can You Escape?

The scariest thing was vendor lock-in. Once you choose, it's hard to switch.

Lock-in Risk (highest to lowest)

1. Azure Cosmos DB - proprietary API, nearly impossible to migrate
2. AWS DynamoDB - unique NoSQL structure
3. GCP BigQuery - SQL is standard but performance optimization is GCP-specific
4. AWS Lambda - code is portable but IAM, EventBridge integration is the problem
5. GCP Cloud Functions - relatively closer to standards

Cloud-Neutral Strategy

The key was creating an abstraction layer.

// Bad: direct dependency on cloud SDK
import { S3Client } from "@aws-sdk/client-s3";

export async function uploadFile(file: Buffer) {
  const s3 = new S3Client({ region: "us-east-1" });
  await s3.send(new PutObjectCommand({ /* ... */ }));
}

// Good: abstraction layer
interface StorageService {
  upload(key: string, data: Buffer): Promise<void>;
  download(key: string): Promise<Buffer>;
}

class S3Storage implements StorageService {
  async upload(key: string, data: Buffer) { /* AWS implementation */ }
  async download(key: string) { /* AWS implementation */ }
}

class GCSStorage implements StorageService {
  async upload(key: string, data: Buffer) { /* GCP implementation */ }
  async download(key: string) { /* GCP implementation */ }
}

// App code depends only on StorageService interface
export function createStorage(): StorageService {
  return process.env.CLOUD === "aws"
    ? new S3Storage()
    : new GCSStorage();
}

This way, you can switch clouds later while keeping core logic intact.

---

What About Vercel/Cloudflare?

Honestly, nowadays the "big three" aren't the only options. Especially for web applications, Vercel or Cloudflare might be better.

Vercel: Optimal for Next.js

// Vercel Edge Functions - runs on edge worldwide
export const config = { runtime: "edge" };

export default async function handler(req: Request) {
  // Near-zero latency - runs on edge closest to user
  return new Response(JSON.stringify({ ok: true }), {
    headers: { "content-type": "application/json" }
  });
}

If using Next.js, Vercel was overwhelmingly convenient. Deployment was just git push, performance optimization was automatic.

Cloudflare: Power of Global Edge

Cloudflare Workers were genuinely fast. Sub-5ms responses from over 300 cities worldwide.

// Cloudflare Workers
export default {
  async fetch(request: Request): Promise<Response> {
    // This responds in under 5ms anywhere in the world
    const url = new URL(request.url);
    return new Response(`Hello from ${request.cf?.colo}`);
  }
}

Pricing was attractive too. Workers were free up to 100k requests/day.

When to Use Vercel/Cloudflare?

• Vercel: Next.js/React web apps, frontend-focused
• Cloudflare: global APIs, CDN + logic, ultra-low latency needed
• Big Three: complex backends, databases, enterprise

We ended up going hybrid: frontend on Vercel, backend API on GCP, data analytics on BigQuery.

---

Decision Framework

Summarizing my experience, here's a framework for choosing:

Step 1: Check Team Capabilities

Does your team already know a cloud well?
└─ YES → Use that. Learning costs are the highest.
└─ NO → Go to Step 2

Step 2: Identify Primary Use Case

Is data analytics core?
└─ YES → GCP (BigQuery)

Using .NET? Or many B2B customers?
└─ YES → Azure

Using Kubernetes?
└─ YES → GCP (GKE)

Is enterprise customer support important?
└─ YES → AWS (largest ecosystem)

Just a Next.js web app?
└─ YES → Vercel

Need global API + ultra-low latency?
└─ YES → Cloudflare Workers

Step 3: Consider Budget

Early startup (<$1k/month)
└─ GCP/Vercel/Cloudflare (generous free tiers)

Mid-size ($1k-10k/month)
└─ GCP or AWS (AWS if you have time to optimize)

Large scale ($10k+/month)
└─ AWS (room for negotiation, Enterprise Support is valuable)

Step 4: Think About the Future

Will this service exist in 5 years?
└─ AWS > Azure > GCP (stability order)

Need global expansion?
└─ GCP/Cloudflare (infrastructure evenly distributed worldwide)

Need multi-cloud strategy?
└─ Terraform + abstraction layer required

---

Summary: How to Choose

After comparing the characteristics of all three clouds, here's how I'd summarize it.

Choose AWS if:

• You have many enterprise customers
• You need maximum service variety
• Stability is top priority
• Your team already has AWS experience

Choose GCP if:

• Data analytics is core
• You're using Kubernetes
• Developer experience matters
• You're using Firebase

Choose Azure if:

• B2B customers use Microsoft ecosystem
• You're on .NET stack
• You need Active Directory integration

Choose Vercel/Cloudflare if:

• Next.js web app is primary
• You need global edge
• You want to avoid complex infrastructure management

The most important lesson: There's no "best cloud." There's only the cloud that fits your team and product.

I initially chose AWS because it was the biggest. But now I use Vercel for frontend, GCP for backend, BigQuery for data, Cloudflare for CDN. Multi-cloud looks complex, but using the best tool for each domain ended up being simpler.

If you're struggling with cloud choice, start small. Build a prototype with free tiers and experience it firsthand. That's worth more than a hundred comparison articles.