Serverless REST API Using AWS, Azure, and GCP

Introduction

In modern cloud computing, serverless architectures have revolutionized how applications are developed and deployed. Serverless REST APIs eliminate the need for infrastructure management, enabling developers to focus purely on business logic. AWS, Azure, and GCP offer robust serverless services like AWS Lambda, Azure Functions, and Google Cloud Functions to handle API requests dynamically.

In this blog, we'll explore how to build a Serverless REST API using each cloud provider and deploy it efficiently.

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Why Choose Serverless for REST APIs?

• No Server Management: Fully managed execution environments.

• Scalability: Automatically scales based on traffic.

• Cost-Efficiency: Pay only for execution time, not idle servers.

• High Availability: Built-in redundancy across cloud zones.

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Architecture Overview

A typical Serverless REST API consists of:

• API Gateway: Routes HTTP requests.

• Function as a Service (FaaS): Processes requests.

• Database (Optional): Stores and retrieves data.

• Authentication: Secures API access.

Cloud-Specific Services

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Implementation: Serverless REST API

1. AWS: Lambda + API Gateway

Step 1: Create a Lambda Function

import json
def lambda_handler(event, context):
    return {
        'statusCode': 200,
        'body': json.dumps({'message': 'Hello from AWS Lambda'})
    }

Step 2: Deploy via Terraform

resource "aws_lambda_function" "api_lambda" {
  function_name = "serverless_api"
  runtime       = "python3.8"
  handler       = "lambda_function.lambda_handler"
  role          = aws_iam_role.lambda_exec.arn
  filename      = "lambda.zip"
}

Step 3: Create an API Gateway

resource "aws_apigatewayv2_api" "http_api" {
  name          = "serverless-api"
  protocol_type = "HTTP"
}

2. Azure: Azure Functions + API Management

Step 1: Create an Azure Function

import azure.functions as func
import json

def main(req: func.HttpRequest) -> func.HttpResponse:
    return func.HttpResponse(json.dumps({'message': 'Hello from Azure Functions'}), mimetype="application/json")

Step 2: Deploy with Azure CLI

az functionapp create --name myServerlessAPI --resource-group myResourceGroup --consumption-plan-location eastus --runtime python --os-type Linux

Step 3: Configure API Management

az apim api create --name serverless-api --resource-group myResourceGroup --service-name myAPIMService --path /api

3. GCP: Cloud Functions + API Gateway

Step 1: Create a Cloud Function

def hello_world(request):
    return {'message': 'Hello from Google Cloud Functions'}

Step 2: Deploy Using gcloud CLI

gcloud functions deploy hello_world --runtime python39 --trigger-http --allow-unauthenticated

Step 3: Set Up API Gateway

swagger: "2.0"
info:
  title: Serverless API
  version: 1.0.0
paths:
  /hello:
    get:
      x-google-backend:
        address: https://REGION-PROJECT_ID.cloudfunctions.net/hello_world

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Testing the API Endpoints

After deployment, test API endpoints using cURL or Postman:

curl https://<API_GATEWAY_URL>/hello

Expected Response:

{"message": "Hello from Serverless API"}

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Best Practices for Serverless APIs

1. Optimize Cold Starts

   • Use Provisioned Concurrency (AWS) or Premium Plan (Azure).

2. Implement API Security

   • Use OAuth, JWT, or API Keys.

3. Enable Logging & Monitoring

   • AWS CloudWatch, Azure Monitor, GCP Logging.

4. Use Infrastructure as Code (IaC)

   • Manage deployments via Terraform or CloudFormation.

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Conclusion

We explored how to build a Serverless REST API using AWS, Azure, and GCP. Serverless architectures provide cost savings, scalability, and simplified deployment. By leveraging API Gateway + Functions, you can build a high-performance API with minimal operational overhead.

Next Steps:

✅ Extend the API with a database (DynamoDB, CosmosDB, Firestore).
✅ Secure endpoints with authentication (OAuth, JWT).
✅ Automate deployments using CI/CD pipelines.

🚀 Got questions? Drop a comment below!