You've containerized your app with Docker. Now what?
If you're on a PaaS like Railway or Render, you git push and they handle the rest. That simplicity is real but as we covered in The Hidden Costs of Vercel, Railway, and Render, it comes at a 3–5x markup over running the same workload on AWS.
AWS Elastic Container Service (ECS) with Fargate is the alternative. Fargate runs your Docker containers on managed infrastructure, no EC2 instances to patch, no clusters to manage. You define what you want to run, and AWS handles the "how."
The catch? The documentation is scattered across dozens of AWS pages, and most tutorials stop at "Hello World" without covering HTTPS, custom domains, load balancers, or auto-scaling. The things you actually need for a production deployment.
This guide is different. We'll deploy a real web application from zero to production-ready, including every step that other tutorials skip.
By the end of this guide, you'll have:
- ✅ A Docker image stored in Amazon ECR
- ✅ An ECS Fargate service running your container 24/7
- ✅ An Application Load Balancer with HTTPS
- ✅ A custom domain pointed at your service
- ✅ Auto-scaling that handles traffic spikes
- ✅ CloudWatch logging for debugging
- ✅ A CI/CD pipeline for automated deployments
Let's build it.
Prerequisites
Before starting, you'll need:
| Requirement | How to Get It |
|---|---|
| AWS Account | Sign up the free tier includes some ECS resources |
| AWS CLI v2 |
brew install awscli (Mac) or install guide
|
| Docker | Docker Desktop or OrbStack |
| A Dockerized app | If you need one, follow our Docker for Web Developers guide |
| A domain (optional) | For HTTPS setup in Step 7 |
Configure AWS CLI:
aws configure
# AWS Access Key ID: <your-access-key>
# AWS Secret Access Key: <your-secret-key>
# Default region name: us-east-1
# Default output format: json
💡 Tip: For production, use IAM Identity Center (SSO) instead of long-lived access keys. We'll cover this in our IAM Roles Explained post.
How It All Fits Together
Before we start clicking buttons, here's the full architecture we're building:
The flow:
-
User hits your domain (e.g.,
app.yourdomain.com) - Route 53 (DNS) resolves it to your ALB
- Application Load Balancer terminates HTTPS (via ACM certificate) and routes traffic
- ECS Service maintains the desired number of running tasks
- Fargate Tasks pull your Docker image from ECR and run your containers
- CloudWatch Logs captures container stdout/stderr for debugging
- IAM Roles grant your containers permission to access AWS services
Now let's build each piece.
Step 1: Push Your Docker Image to ECR
Amazon Elastic Container Registry (ECR) is AWS's private Docker registry. It's where ECS pulls your images from.
Create an ECR Repository
aws ecr create-repository \
--repository-name my-web-app \
--region us-east-1 \
--image-scanning-configuration scanOnPush=true \
--encryption-configuration encryptionType=AES256
scanOnPush=true enables automatic vulnerability scanning on every push, free and highly recommended.
Build, Tag, and Push
# 1. Get your AWS account ID
AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query "Account" --output text)
# 2. Authenticate Docker with ECR
aws ecr get-login-password --region us-east-1 | \
docker login --username AWS --password-stdin \
${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com
# 3. Build the image (for x86 architecture, what Fargate uses by default)
docker build --platform linux/amd64 -t my-web-app .
# 4. Tag for ECR
docker tag my-web-app:latest \
${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/my-web-app:latest
# 5. Push
docker push \
${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/my-web-app:latest
⚠️ Apple Silicon users (M1/M2/M3): The
--platform linux/amd64flag is critical. Without it, you'll build an ARM image that may crash on x86 Fargate. Alternatively, use--platform linux/arm64and configure your Fargate task for ARM (Graviton) which is 20% cheaper.
Verify the Push
aws ecr describe-images \
--repository-name my-web-app \
--region us-east-1
You should see your image with its digest and size.
Step 2: Create an ECS Cluster
An ECS cluster is a logical grouping of tasks and services. With Fargate, the cluster is essentially a namespace there are no EC2 instances to manage.
aws ecs create-cluster \
--cluster-name my-app-cluster \
--setting name=containerInsights,value=enabled
Container Insights adds CPU, memory and network metrics to CloudWatch invaluable for production debugging.
That's it. One command. The cluster is ready.
Step 3: Create the Task Execution IAM Role
ECS needs an IAM role to pull images from ECR and send logs to CloudWatch on your behalf.
Create the Trust Policy
cat > ecs-trust-policy.json << 'EOF'
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "ecs-tasks.amazonaws.com"
},
"Action": "sts:AssumeRole"
}
]
}
EOF
Create the Role
# Create the role
aws iam create-role \
--role-name ecsTaskExecutionRole \
--assume-role-policy-document file://ecs-trust-policy.json
# Attach the managed policy
aws iam attach-role-policy \
--role-name ecsTaskExecutionRole \
--policy-arn arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy
This grants ECS permission to:
- Pull images from ECR
- Create and write to CloudWatch log groups
- (Optionally) Read secrets from AWS Secrets Manager or SSM Parameter Store
Step 4: Create the Task Definition
The task definition is the blueprint for your container. It defines what image to run, how much CPU/memory to allocate, which ports to expose, and where to send logs.
Create the Task Definition JSON
cat > task-definition.json << EOF
{
"family": "my-web-app",
"networkMode": "awsvpc",
"requiresCompatibilities": ["FARGATE"],
"cpu": "256",
"memory": "512",
"executionRoleArn": "arn:aws:iam::${AWS_ACCOUNT_ID}:role/ecsTaskExecutionRole",
"containerDefinitions": [
{
"name": "web",
"image": "${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/my-web-app:latest",
"portMappings": [
{
"containerPort": 3000,
"hostPort": 3000,
"protocol": "tcp"
}
],
"essential": true,
"environment": [
{
"name": "NODE_ENV",
"value": "production"
},
{
"name": "PORT",
"value": "3000"
}
],
"logConfiguration": {
"logDriver": "awslogs",
"options": {
"awslogs-group": "/ecs/my-web-app",
"awslogs-region": "us-east-1",
"awslogs-stream-prefix": "ecs",
"awslogs-create-group": "true"
}
},
"healthCheck": {
"command": ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:3000/health || exit 1"],
"interval": 30,
"timeout": 5,
"retries": 3,
"startPeriod": 60
}
}
]
}
EOF
Register It
aws ecs register-task-definition \
--cli-input-json file://task-definition.json
Understanding the Config
| Setting | Value | Why |
|---|---|---|
cpu: "256" |
0.25 vCPU | Smallest Fargate size; fine for most APIs at low traffic |
memory: "512" |
512 MB | Must be compatible with CPU (see valid combos) |
networkMode: "awsvpc" |
Required for Fargate | Each task gets its own ENI (network interface) |
logConfiguration |
CloudWatch Logs | Streams container stdout/stderr to /ecs/my-web-app log group |
healthCheck |
HTTP check on /health
|
ECS replaces unhealthy tasks automatically |
Valid CPU/Memory Combinations
| CPU (vCPU) | Memory Options |
|---|---|
| 256 (.25 vCPU) | 512 MB, 1 GB, 2 GB |
| 512 (.5 vCPU) | 1 GB – 4 GB (in 1 GB increments) |
| 1024 (1 vCPU) | 2 GB – 8 GB (in 1 GB increments) |
| 2048 (2 vCPU) | 4 GB – 16 GB (in 1 GB increments) |
| 4096 (4 vCPU) | 8 GB – 30 GB (in 1 GB increments) |
Start small. You can change CPU/memory at any time by registering a new task definition revision and updating the service. Start with 0.25 vCPU / 512 MB and scale up based on actual usage.
Step 5: Create the Application Load Balancer (ALB)
The ALB sits in front of your ECS service, distributes traffic, terminates HTTPS, and enables health-check-based routing.
Get Your VPC and Subnets
# Get default VPC ID
VPC_ID=$(aws ec2 describe-vpcs \
--filters "Name=isDefault,Values=true" \
--query "Vpcs[0].VpcId" --output text)
# Get at least 2 subnets (for high availability)
SUBNETS=$(aws ec2 describe-subnets \
--filters "Name=vpc-id,Values=${VPC_ID}" \
--query "Subnets[0:2].SubnetId" --output text)
SUBNET_1=$(echo $SUBNETS | awk '{print $1}')
SUBNET_2=$(echo $SUBNETS | awk '{print $2}')
echo "VPC: $VPC_ID"
echo "Subnet 1: $SUBNET_1"
echo "Subnet 2: $SUBNET_2"
Create a Security Group for the ALB
ALB_SG_ID=$(aws ec2 create-security-group \
--group-name my-app-alb-sg \
--description "Allow HTTP/HTTPS to ALB" \
--vpc-id ${VPC_ID} \
--query "GroupId" --output text)
# Allow HTTP (port 80) from anywhere
aws ec2 authorize-security-group-ingress \
--group-id ${ALB_SG_ID} \
--protocol tcp --port 80 --cidr 0.0.0.0/0
# Allow HTTPS (port 443) from anywhere
aws ec2 authorize-security-group-ingress \
--group-id ${ALB_SG_ID} \
--protocol tcp --port 443 --cidr 0.0.0.0/0
Create a Security Group for ECS Tasks
TASK_SG_ID=$(aws ec2 create-security-group \
--group-name my-app-task-sg \
--description "Allow traffic from ALB to ECS tasks" \
--vpc-id ${VPC_ID} \
--query "GroupId" --output text)
# Only allow traffic FROM the ALB (not from the public internet)
aws ec2 authorize-security-group-ingress \
--group-id ${TASK_SG_ID} \
--protocol tcp --port 3000 \
--source-group ${ALB_SG_ID}
🔒 Security note: The task security group only accepts traffic from the ALB, not from the internet directly. This is important as your containers should never be directly exposed.
Create the ALB
ALB_ARN=$(aws elbv2 create-load-balancer \
--name my-app-alb \
--subnets ${SUBNET_1} ${SUBNET_2} \
--security-groups ${ALB_SG_ID} \
--scheme internet-facing \
--type application \
--query "LoadBalancers[0].LoadBalancerArn" --output text)
echo "ALB ARN: $ALB_ARN"
Create a Target Group
The target group tells the ALB where to forward requests (to your ECS tasks).
TG_ARN=$(aws elbv2 create-target-group \
--name my-app-tg \
--protocol HTTP \
--port 3000 \
--vpc-id ${VPC_ID} \
--target-type ip \
--health-check-path /health \
--health-check-interval-seconds 30 \
--health-check-timeout-seconds 5 \
--healthy-threshold-count 2 \
--unhealthy-threshold-count 3 \
--query "TargetGroups[0].TargetGroupArn" --output text)
echo "Target Group ARN: $TG_ARN"
Important:
--target-type ipis required for Fargate (notinstance).
Create an HTTP Listener
aws elbv2 create-listener \
--load-balancer-arn ${ALB_ARN} \
--protocol HTTP \
--port 80 \
--default-actions Type=forward,TargetGroupArn=${TG_ARN}
We'll add HTTPS in Step 7. For now, HTTP lets you verify everything works.
Step 6: Create the ECS Service
The service maintains your desired number of running tasks. If a task crashes, ECS automatically replaces it. If a task fails health checks, ECS drains and replaces it.
aws ecs create-service \
--cluster my-app-cluster \
--service-name my-web-app-service \
--task-definition my-web-app \
--desired-count 2 \
--launch-type FARGATE \
--network-configuration "awsvpcConfiguration={
subnets=[${SUBNET_1},${SUBNET_2}],
securityGroups=[${TASK_SG_ID}],
assignPublicIp=ENABLED
}" \
--load-balancers "targetGroupArn=${TG_ARN},containerName=web,containerPort=3000" \
--deployment-configuration "maximumPercent=200,minimumHealthyPercent=100" \
--enable-execute-command
Understanding the Flags
| Flag | Value | Why |
|---|---|---|
--desired-count 2 |
2 tasks | High availability across 2 AZs |
--launch-type FARGATE |
Serverless compute | No EC2 instances to manage |
assignPublicIp=ENABLED |
Public IP for tasks | Needed to pull images from ECR (unless using VPC endpoints) |
maximumPercent=200 |
During deploys | ECS spins up new tasks before killing old ones (zero-downtime) |
minimumHealthyPercent=100 |
During deploys | All existing tasks stay running until new ones are healthy |
--enable-execute-command |
ECS Exec | Lets you SSH-like into running containers for debugging |
Verify the Deployment
# Watch the service stabilize
aws ecs describe-services \
--cluster my-app-cluster \
--services my-web-app-service \
--query "services[0].{status:status,running:runningCount,desired:desiredCount,deployments:deployments[0].rolloutState}"
Wait 2–3 minutes until runningCount matches desiredCount and rolloutState is COMPLETED.
Get the ALB URL
ALB_DNS=$(aws elbv2 describe-load-balancers \
--load-balancer-arns ${ALB_ARN} \
--query "LoadBalancers[0].DNSName" --output text)
echo "Your app is live at: http://${ALB_DNS}"
Open that URL - your Dockerized app is now running on AWS! 🎉
Step 7: Add HTTPS and a Custom Domain
Request an SSL Certificate (ACM)
CERT_ARN=$(aws acm request-certificate \
--domain-name app.yourdomain.com \
--validation-method DNS \
--query "CertificateArn" --output text)
echo "Certificate ARN: $CERT_ARN"
Validate the Certificate
ACM will give you a CNAME record to add to your DNS. You can retrieve it:
aws acm describe-certificate \
--certificate-arn ${CERT_ARN} \
--query "Certificate.DomainValidationOptions[0].ResourceRecord"
Add this CNAME to your DNS provider (Route 53, Cloudflare, etc.). Validation usually takes 5–30 minutes.
Add an HTTPS Listener
Once the certificate is validated:
# Create HTTPS listener
aws elbv2 create-listener \
--load-balancer-arn ${ALB_ARN} \
--protocol HTTPS \
--port 443 \
--ssl-policy ELBSecurityPolicy-TLS13-1-2-2021-06 \
--certificates CertificateArn=${CERT_ARN} \
--default-actions Type=forward,TargetGroupArn=${TG_ARN}
Redirect HTTP → HTTPS
# Get the HTTP listener ARN
HTTP_LISTENER_ARN=$(aws elbv2 describe-listeners \
--load-balancer-arn ${ALB_ARN} \
--query "Listeners[?Port==\`80\`].ListenerArn" --output text)
# Modify it to redirect to HTTPS
aws elbv2 modify-listener \
--listener-arn ${HTTP_LISTENER_ARN} \
--default-actions '[{
"Type": "redirect",
"RedirectConfig": {
"Protocol": "HTTPS",
"Port": "443",
"StatusCode": "HTTP_301"
}
}]'
Point Your Domain to the ALB
If using Route 53:
# Create a hosted zone (skip if you already have one)
aws route53 create-hosted-zone --name yourdomain.com --caller-reference $(date +%s)
# Add an alias record pointing to the ALB
aws route53 change-resource-record-sets \
--hosted-zone-id YOUR_ZONE_ID \
--change-batch '{
"Changes": [{
"Action": "UPSERT",
"ResourceRecordSet": {
"Name": "app.yourdomain.com",
"Type": "A",
"AliasTarget": {
"HostedZoneId": "Z35SXDOTRQ7X7K",
"DNSName": "'${ALB_DNS}'",
"EvaluateTargetHealth": true
}
}
}]
}'
If using Cloudflare or another DNS provider: Create a CNAME record pointing app.yourdomain.com to your ALB DNS name.
Step 8: Enable Auto-Scaling
Auto-scaling adjusts the number of running tasks based on CPU utilization, memory, or request count.
Register the Scaling Target
aws application-autoscaling register-scalable-target \
--service-namespace ecs \
--resource-id service/my-app-cluster/my-web-app-service \
--scalable-dimension ecs:service:DesiredCount \
--min-capacity 2 \
--max-capacity 10
Create a CPU-Based Scaling Policy
aws application-autoscaling put-scaling-policy \
--service-namespace ecs \
--resource-id service/my-app-cluster/my-web-app-service \
--scalable-dimension ecs:service:DesiredCount \
--policy-name cpu-scaling \
--policy-type TargetTrackingScaling \
--target-tracking-scaling-policy-configuration '{
"TargetValue": 70.0,
"PredefinedMetricSpecification": {
"PredefinedMetricType": "ECSServiceAverageCPUUtilization"
},
"ScaleInCooldown": 300,
"ScaleOutCooldown": 60
}'
What This Does
| Metric | Threshold | Action |
|---|---|---|
| Average CPU > 70% | For 60 seconds | Scale out (add tasks) |
| Average CPU < 70% | For 5 minutes | Scale in (remove tasks) |
| Minimum tasks | 2 | Always running (high availability) |
| Maximum tasks | 10 | Cost ceiling |
💡 Pro tip: The asymmetric cooldowns (60s out, 300s in) are intentional. Scale out fast to handle traffic spikes; scale in slowly to avoid flapping.
What This Actually Costs
Let's calculate the real cost of running this setup 24/7 in us-east-1 with 2 tasks at 0.25 vCPU / 512 MB:
Fargate Compute (Linux/x86)
Per task:
vCPU: 0.25 × $0.000011244/sec × 86,400 sec/day × 30 days = $7.29/mo
Memory: 0.5 GB × $0.000001235/sec × 86,400 sec/day × 30 days = $1.60/mo
Per task total: $8.89/mo
2 tasks: $17.78/mo
ARM/Graviton (20% Cheaper)
Per task:
vCPU: 0.25 × $0.0000089944/sec × 86,400 sec/day × 30 days = $5.83/mo
Memory: 0.5 GB × $0.0000009889/sec × 86,400 sec/day × 30 days = $1.28/mo
Per task total: $7.11/mo
2 tasks: $14.22/mo
Full Monthly Cost Breakdown
| Component | x86 Cost | Graviton Cost |
|---|---|---|
| Fargate compute (2 tasks) | $17.78 | $14.22 |
| Application Load Balancer | ~$22.00 | ~$22.00 |
| ECR storage (~500 MB) | ~$0.05 | ~$0.05 |
| CloudWatch Logs (5 GB) | ~$2.50 | ~$2.50 |
| Data transfer (10 GB) | ~$0.90 | ~$0.90 |
| Monthly total | ~$43 | ~$40 |
Compare this to PaaS pricing: Railway ~$100/mo, Render ~$85/mo for equivalent resources. That's a 50–60% saving by running on Fargate directly. And if you use Compute Savings Plans (1-year commit), Fargate compute drops by another 50%.
Deploying Updates (Zero Downtime)
When you push a new version, ECS performs a rolling deployment:
# 1. Build and push the new image
docker build --platform linux/amd64 -t my-web-app .
docker tag my-web-app:latest \
${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/my-web-app:latest
docker push \
${AWS_ACCOUNT_ID}.dkr.ecr.us-east-1.amazonaws.com/my-web-app:latest
# 2. Force a new deployment (pulls the latest image)
aws ecs update-service \
--cluster my-app-cluster \
--service my-web-app-service \
--force-new-deployment
What Happens During a Deploy
- ECS launches new tasks with the updated image (alongside old tasks)
- The ALB starts health-checking the new tasks
- Once healthy, the ALB shifts traffic to new tasks
- Old tasks are drained (existing connections finish) and stopped
- Zero downtime users never see an error
Debugging with ECS Exec
ECS Exec lets you shell into a running container like docker exec but for production:
aws ecs execute-command \
--cluster my-app-cluster \
--task <task-id> \
--container web \
--interactive \
--command "/bin/sh"
Get the task ID:
aws ecs list-tasks \
--cluster my-app-cluster \
--service-name my-web-app-service \
--query "taskArns[0]" --output text
View Logs
# Recent logs
aws logs tail /ecs/my-web-app --follow
# Search logs for errors
aws logs filter-log-events \
--log-group-name /ecs/my-web-app \
--filter-pattern "ERROR" \
--start-time $(date -d '1 hour ago' +%s000)
Bonus: CI/CD with GitHub Actions
Automate the entire build → push → deploy pipeline:
# .github/workflows/deploy.yml
name: Deploy to ECS
on:
push:
branches: [main]
permissions:
id-token: write
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials (OIDC)
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::${{ secrets.AWS_ACCOUNT_ID }}:role/GitHubActionsECSRole
aws-region: us-east-1
- name: Login to ECR
id: ecr-login
uses: aws-actions/amazon-ecr-login@v2
- name: Build, tag, and push image
env:
ECR_REGISTRY: ${{ steps.ecr-login.outputs.registry }}
IMAGE_TAG: ${{ github.sha }}
run: |
docker build -t $ECR_REGISTRY/my-web-app:$IMAGE_TAG .
docker build -t $ECR_REGISTRY/my-web-app:latest .
docker push $ECR_REGISTRY/my-web-app:$IMAGE_TAG
docker push $ECR_REGISTRY/my-web-app:latest
- name: Update ECS task definition
id: task-def
uses: aws-actions/amazon-ecs-render-task-definition@v1
with:
task-definition: task-definition.json
container-name: web
image: ${{ steps.ecr-login.outputs.registry }}/my-web-app:${{ github.sha }}
- name: Deploy to ECS
uses: aws-actions/amazon-ecs-deploy-task-definition@v2
with:
task-definition: ${{ steps.task-def.outputs.task-definition }}
service: my-web-app-service
cluster: my-app-cluster
wait-for-service-stability: true
Why OIDC, Not Access Keys
This workflow uses OIDC federation instead of storing AWS access keys in GitHub secrets:
- ✅ No long-lived credentials - temporary tokens, auto-rotated
- ✅ No secrets to leak - the identity is tied to the GitHub repo
- ✅ Audit trail - shows up in CloudTrail as
GitHubActionsECSRole
Common Errors and Fixes
| Error | Cause | Fix |
|---|---|---|
CannotPullContainerError |
ECR auth expired or wrong region | Re-run aws ecr get-login-password and check --region
|
ResourceNotFoundException |
Task definition not found | Check aws ecs list-task-definitions
|
service is not ACTIVE |
Service failed to stabilize | Check aws ecs describe-services for events field |
Essential container exited |
App crashed at startup | Check aws logs tail /ecs/my-web-app for stack trace |
HealthCheck failed |
/health endpoint not responding |
Verify endpoint exists and returns 200 within timeout |
port 3000 is not accessible |
Security group misconfigured | Ensure task SG allows ingress from ALB SG on port 3000 |
OutOfMemoryError |
App exceeds memory allocation | Increase memory in task definition |
unable to place a task |
No subnets have available IPs or wrong AZ | Use multiple subnets across 2+ AZs |
Cleanup (If You're Done Testing)
# 1. Delete the service
aws ecs update-service \
--cluster my-app-cluster \
--service my-web-app-service \
--desired-count 0
aws ecs delete-service \
--cluster my-app-cluster \
--service my-web-app-service --force
# 2. Delete the ALB and target group
aws elbv2 delete-load-balancer --load-balancer-arn ${ALB_ARN}
aws elbv2 delete-target-group --target-group-arn ${TG_ARN}
# 3. Delete the cluster
aws ecs delete-cluster --cluster my-app-cluster
# 4. Delete the ECR repository
aws ecr delete-repository --repository-name my-web-app --force
# 5. Clean up security groups
aws ec2 delete-security-group --group-id ${ALB_SG_ID}
aws ec2 delete-security-group --group-id ${TASK_SG_ID}
TL;DR
| Step | What You Did | Time |
|---|---|---|
| 1. ECR | Created a private registry and pushed your Docker image | ~5 min |
| 2. Cluster | Created an ECS cluster (one command) | ~1 min |
| 3. IAM | Created the task execution role | ~2 min |
| 4. Task Definition | Defined your container's blueprint | ~3 min |
| 5. ALB | Created a load balancer with security groups | ~5 min |
| 6. Service | Launched your tasks behind the ALB | ~3 min |
| 7. HTTPS/Domain | Added SSL certificate and custom domain | ~10 min |
| 8. Auto-Scaling | Configured CPU-based scaling (2–10 tasks) | ~2 min |
| Total | Production-ready deployment | ~30 min |
Skip All 8 Steps
If this guide felt like a lot of work for "deploy my app", you're right. This is the exact problem TurboDeploy solves.
With TurboDeploy, you:
- Connect your GitHub repo
- We detect your framework and generate an optimized Dockerfile
- We create ALL of the above - ECR, cluster, task definition, ALB, HTTPS, auto-scaling in your AWS account
- Push to
main, and we deploy automatically
Same infrastructure. Same AWS pricing. Zero configuration.
Join the TurboDeploy waitlist → Ship to production in 5 minutes, not 30.
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