D2C eCommerce Platform
A fast-growing fashion and lifestyle D2C brand was losing sales and customer trust every time they ran a major sale event. Their fixed VM infrastructure couldn't handle traffic spikes, deployments were manual and risky, and every peak event was a potential crisis. With a high-stakes annual sale 10 weeks away, we rebuilt their platform to handle 10× normal load — and it delivered flawlessly.
Client name and identifying details withheld at their request. References available during consultation.
!The Challenge
This D2C brand had built a loyal following through strong product and marketing — but their technology was not keeping pace. Their platform ran on a fixed set of virtual machines on AWS, provisioned nearly two years earlier with no thought for scalability. During normal traffic, performance was acceptable. During sale events, it was a disaster.
Their biggest annual sale the previous year resulted in the site being completely inaccessible for nearly 90 minutes at peak — right when tens of thousands of customers were trying to purchase. The team estimated they lost several hundred thousand rupees in direct revenue, plus an untold amount in brand trust and customer churn. They had attempted to "scale up" by manually upgrading to larger EC2 instances before the event, but there was no automation, no auto-scaling, and no way to respond to unexpected traffic surges in real time.
Deployments were equally fragile. There was no staging environment — all code changes were pushed directly to production, often late at night to avoid peak hours. This meant that new features were frequently released with bugs that impacted customers before engineers could respond. The team had no confidence in their release process and had started avoiding deployments in the weeks before sale events entirely — which meant features and fixes were bottlenecked for weeks at a time.
With their annual sale 10 weeks away and growing pressure from investors to demonstrate platform reliability, they came to us with a clear mandate: make the platform scale, or the next sale event will be the last one on their current infrastructure.
⇄Before vs After
⚙Tech Stack
→What We Did
Auto-Scaling Infrastructure Migration
We migrated the application from fixed EC2 instances to AWS ECS Fargate with Application Auto Scaling policies. The platform now monitors CPU and request-count metrics and adds new container instances within 60 seconds when traffic spikes. During the sale event, the platform automatically scaled from its baseline of 4 tasks to 28 tasks at peak — transparently, without any human intervention.
CDN & Caching Layer
We implemented Amazon CloudFront with a carefully designed caching strategy. Product listing pages, images, and static assets are cached at the edge with TTLs tuned to product catalogue update frequency. Dynamic personalisation and cart operations bypass the cache appropriately. We also introduced Redis via ElastiCache for session management and frequently-accessed product data, reducing database load by over 60% during the peak event.
Database Scaling for Peak Traffic
The existing single RDS instance was a bottleneck. We upgraded to a Multi-AZ RDS deployment for failover resilience and added a read replica specifically for product catalogue queries — the heaviest read workload during sale events. Connection pooling was implemented at the application layer to prevent connection exhaustion under high concurrency.
CI/CD & Staging Environment
We built a complete GitHub Actions pipeline with Docker image builds, ECR pushes, and blue/green deployments to ECS. A fully isolated staging environment was created that mirrors production infrastructure. All changes must pass automated tests and be deployed to staging before production. The team went from deploying once a week (nervously) to deploying multiple times a day with confidence.
Load Testing & Game-Day Simulation
Three weeks before the sale, we ran structured load tests using k6, simulating 10× normal traffic in a staging environment identical to production. This revealed two bottlenecks — a slow product search query and a session management race condition — which we fixed before the actual event. On sale day, the team watched dashboards calmly instead of firefighting.
✦Key Engineering Decisions
Decision: ECS over Kubernetes for speed of delivery
With 10 weeks to the sale event, we couldn't spend 4 weeks setting up and learning EKS. ECS Fargate delivered the auto-scaling capability they needed in a fraction of the time, with significantly less operational complexity for a team their size.
Decision: Blue/green deployments over rolling updates
For an eCommerce platform, zero-downtime deployments are non-negotiable. Blue/green gives instant rollback capability — if a deployment has issues, traffic switches back to the previous version in seconds rather than waiting for a rolling update to complete.
Decision: Load testing before go-live, not after
Most teams find their scaling issues on the day of the event. We invested two weeks in structured load testing in a production-identical staging environment. The two issues we found and fixed would almost certainly have caused partial outages during the actual sale.
⏱Engagement Timeline
✓Results Delivered
"Our biggest sale of the year went off without a single incident. For the first time ever, we were watching dashboards and celebrating with the team instead of firefighting server issues. The load tests ESSEMVEE ran found two bugs that would have taken us down. Worth every rupee."
Head of Technology
D2C eCommerce Platform · South Asia · Name withheld on request
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