Step-by-Step Guide to Deploying Your Web App to AWS / Azure / GCP

Introduction

Deploying a web app to the cloud can feel overwhelming at first — so many providers, services and configuration knobs. This guide walks you through the essential steps to deploy a modern web application to the three major clouds: AWS, Azure and GCP. You will learn how to choose the right hosting option, prepare your app for production, run CLI commands, set up CI/CD and consider cost, security and scaling. The goal is practical: you should be able to follow the steps and launch a working app that's production-ready.

Why this matters

Cloud platforms power most modern web traffic. Choosing the right deployment pattern affects your app's availability, cost and maintenance burden. This guide condenses cloud knowledge into repeatable steps so you can focus on features instead of firefighting infra issues.

Preparation: common prerequisites

Before diving into provider-specific flows, there are common tasks that make deployments smooth across clouds.

• Containerize when possible — Docker images make deployments predictable.

• Use environment variables for secrets and configuration rather than in-code constants.

• Automate builds with a CI tool (GitHub Actions, GitLab CI, Azure Pipelines, etc.).

• Monitor & logging — plan for logs and metrics from day one.

Standard deployment workflow (step-by-step)

Regardless of provider, most deployments follow the same high-level steps. Treat this as your deployment checklist.

Step 1 — Build and test locally

Ensure the app builds locally and tests pass. If you use containers, create a production-ready Dockerfile and verify the image runs with the production command.

Step 2 — Prepare configuration & secrets

Move any environment-specific values into environment variables or a secrets manager. Avoid checked-in secrets.

Step 3 — Choose a hosting model

Decide between serverless offerings (e.g., AWS Lambda, Azure Functions), platform-as-a-service (Elastic Beanstalk, Azure App Service) or container services (ECS/Fargate, AKS, GKE, Cloud Run). Your app architecture and scaling needs guide this choice.

Step 4 — Deploy and validate

Run the provider's deployment command or push via CI/CD. Validate the app endpoint, test critical flows and check logs.

Step 5 — Configure monitoring & scaling

Attach monitoring (e.g., CloudWatch, Azure Monitor, Cloud Monitoring) and configure autoscaling or schedules to match expected traffic.

Provider-specific quick guides

AWS — Elastic Beanstalk (classic, fast)

Elastic Beanstalk is great for app servers. It handles deployment, health checks and autoscaling with minimal infra code.

• Good for: standard web apps, quick deployments, minimal infra overhead.

• Key steps: install EB CLI, initialize, deploy.

# Install EB CLI (example)
pip install awsebcli

# Initialize (one-time)

eb init -p node.js my-app --region us-east-1

# Create environment and deploy

eb create my-app-env
eb deploy 

AWS — ECS / Fargate or EKS

Use containers with ECS Fargate for managed container hosting or EKS if you need Kubernetes. These give maximum control at cost of complexity.

Azure — App Service & Static Web Apps

Azure App Service is the PaaS equivalent to Elastic Beanstalk. For SPAs and static sites, Azure Static Web Apps integrates easily with GitHub Actions.

# Login to Azure
az login

# Variables (replace with your own values)
RESOURCE_GROUP="myResourceGroup"
LOCATION="eastus"
PLAN_NAME="myAppServicePlan"
APP_NAME="myuniqueappname123"   # must be globally unique
ZIP_FILE="./app.zip"

# Create a resource group
az group create 
  --name $RESOURCE_GROUP 
  --location $LOCATION

# Create an App Service plan
az appservice plan create 
  --name $PLAN_NAME 
  --resource-group $RESOURCE_GROUP 
  --sku B1

# Create a Web App
az webapp create 
  --resource-group $RESOURCE_GROUP 
  --plan $PLAN_NAME 
  --name $APP_NAME

# Deploy the app from a ZIP file
az webapp deployment source config-zip 
  --resource-group $RESOURCE_GROUP 
  --name $APP_NAME 
  --src $ZIP_FILE
 

GCP — App Engine, Cloud Run & GKE

App Engine is serverless PaaS; Cloud Run runs containers serverlessly; GKE is managed Kubernetes. Cloud Run is an excellent balance: simple, scales to zero and supports containers.

# Initialize gcloud and authenticate
gcloud init

# Variables (replace with your own values)
PROJECT_ID="your-project-id"
REGION="us-central1"
SERVICE_NAME="my-app"
IMAGE="gcr.io/$PROJECT_ID/$SERVICE_NAME"

# Set the active project
gcloud config set project $PROJECT_ID

# === Option 1: Deploy to App Engine ===
gcloud app deploy

# === Option 2: Build container and deploy to Cloud Run ===

# Build and push image to Google Container Registry
gcloud builds submit --tag $IMAGE

# Deploy to Cloud Run (fully managed)
gcloud run deploy $SERVICE_NAME 
  --image $IMAGE 
  --platform managed 
  --region $REGION 
  --allow-unauthenticated
 

Comparison table: AWS vs Azure vs GCP

Analysis: If you want predictable, container-first deployments with minimal ops, Cloud Run and App Engine are compelling. If you need the broadest set of services and global footprint, AWS often wins. Azure shines for Microsoft-centric stacks.

CI/CD: a minimal GitHub Actions pipeline example

Automating deployments ensures repeatable releases. Below is a minimal GitHub Actions workflow to build a Docker image and push to a registry — a pattern you can adapt for each cloud.

name: CI Build and Push

on:
push:
branches:
- main

jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
- name: Build and push
uses: docker/build-push-action@v4
with:
push: true
tags: ghcr.io/github.repository_owner/my-app:latest 

From here you can add steps to deploy the pushed image to AWS (ECS/EB), Azure (Container Registry + Web App) or GCP (Cloud Run).

Configuring secrets & environment variables

Each cloud offers a secrets store (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager). Use these for production secrets and reference them securely in your app configuration or CI workflow.

Common troubleshooting checklist

• Check logs: Cloud logs are often the fastest way to diagnose startup errors.

• Health checks: Ensure your health endpoint returns 200 and matches provider timeout.

• Environment mismatch: Verify NODE_ENV, runtime versions and dependency behavior in production builds.

• Resource limits: Memory limits and cold starts can cause failures — adjust instance sizes if needed.

Security essentials

Follow these minimum security practices:

• Use HTTPS by default and enforce HSTS.

• Run services with least privilege IAM roles.

• Scan container images for vulnerabilities.

• Enable audit logging and alerts for suspicious activity.

Real-world use cases and trends

Several trends shape how teams deploy web apps today:

• Containers everywhere: Organizations prefer container images because they decouple runtime from host OS.

• Serverless for microservices: Functions and serverless containers reduce ops burden for sporadic workloads.

• Policy-as-code: Teams adopt guardrails (e.g., OPA, IAM policies) to maintain compliance at scale.

Future-proofing your deployments

To minimize future rework, consider:

• IaC first: Use Terraform, CloudFormation or Pulumi to describe infra as code.

• Modular architecture: Separate frontend, API and background jobs so you can scale independently.

• Observability: Instrument code for traces, metrics and structured logs now — you'll thank yourself later.

Tailored recommendations

If you are starting solo or a small team

Choose a PaaS with minimal setup: AWS Elastic Beanstalk, Azure App Service or GCP App Engine. If you work with containers, go with Cloud Run or equivalent to avoid managing clusters.

If you run data/ML workloads

GCP has strong managed ML and data services. Pick GCP for integrations with BigQuery and Vertex AI; consider GKE for heavy Kubernetes workloads.

If you are enterprise with heavy compliance

AWS or Azure often provide broader compliance certifications and enterprise features. Use IaC and policy enforcement from day one.

Example: Minimal end-to-end deploy to GCP Cloud Run (Docker)

This short snippet shows the typical steps: build, push and deploy. Replace PROJECT_ID and REGION with your values.

# Build and push
gcloud builds submit --tag gcr.io/PROJECT_ID/my-app

# Deploy to Cloud Run

gcloud run deploy my-app 
--image gcr.io/PROJECT_ID/my-app 
--platform managed 
--region REGION 
--allow-unauthenticated 

Cost optimization basics

Cloud costs can surprise you. Start with:

• Right-size instances and containers.

• Use autoscale to match demand.

• Leverage free tiers and sustained-use discounts where applicable.

Final verdict & recommended path

There's no universal winner. For quick web app launches with low infra needs, PaaS products like Elastic Beanstalk, Azure App Service, or App Engine work well. For container-first approaches with flexible scaling, prefer Cloud Run (GCP) or managed containers on AWS/Azure. If your team is already invested in one cloud, use that provider for operational consistency.

Key bullet takeaways

• Prepare: Build, test and containerize locally.

• Automate: Use CI/CD for consistent releases.

• Secure: Use managed secrets and least-privilege IAM.

• Monitor: Add logging and metrics early.

• Choose wisely: Match provider features to your team's needs.

Resources & next steps

Once you complete a manual deploy, automate it with a CI workflow and add monitoring. Consider learning Terraform for multi-cloud reproducible infra.