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Confidential clientDirect-to-consumer e-commerce brand
case study / Confidential client
Re-platforming a D2C storefront off a single server
Moved a growing D2C brand's storefront and order system from one overloaded server to containers on AWS, then right-sized the result so the bill follows real traffic.
- Client
- Direct-to-consumer e-commerce brand
- Industry
- E-commerce / D2C
- Service
- Migration · Cost Optimization
The challenge
The whole business ran on one large EC2 instance: storefront, admin panel, background jobs and the database. Sale days pushed it to its limit, every deployment meant downtime, and the instance was sized for the busiest hour of the year.
What we built
- Containerized the storefront and admin on Amazon ECS Fargate behind an Application Load Balancer, scaling on request count
- Migrated the MySQL database to Amazon Aurora with AWS DMS, keeping the cutover window short
- Moved product images and static assets to Amazon S3 behind CloudFront
- Split background jobs (order sync, emails, invoices) into Amazon SQS workers that scale on their own
- Set up CI/CD from GitHub to Amazon ECR with rolling ECS deployments
- Reviewed usage with Compute Optimizer once traffic settled, then committed to a Savings Plan
Architecture
AWS services used
aws services
| AWS service | Role in the solution |
|---|---|
| Amazon ECS Fargate + ALB | Runs the storefront and admin as containers that scale with traffic. |
| Amazon Aurora (MySQL) | Managed database, migrated from the old server with AWS DMS. |
| Amazon S3 + CloudFront | Serves images and static assets from the edge. |
| Amazon SQS | Queues background jobs so they never compete with shoppers for capacity. |
| Compute Optimizer + Savings Plans | Right-sizing and committed pricing once usage was known. |
Outcomes
- Storefront, background jobs and database scale independently, so a sale-day spike no longer slows order processing
- Deployments without downtime
- Compute billed for real traffic instead of the busiest hour of the year
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