The Challenge
An e-commerce platform was spending over $45,000 per month on AWS infrastructure with no clear understanding of what was driving the costs. Their DevOps setup had been built incrementally over three years — every new feature got its own set of resources, nothing was right-sized, and auto-scaling was either misconfigured or missing entirely.
Page load times averaged 4.2 seconds. During sales events, the site slowed to 8+ seconds and occasionally went down entirely. They were spending more on infrastructure than on their entire engineering team.
What We Did
We started with a two-week infrastructure audit. Our DevOps engineer mapped every resource — EC2 instances, RDS databases, ElastiCache clusters, S3 buckets, CloudFront distributions — and tagged them by service, environment, and team. This alone revealed that 23% of their spend was on resources that were either unused or dramatically oversized.
The optimisation work followed three tracks: right-sizing (matching instance types to actual usage), auto-scaling (implementing proper scaling policies based on traffic patterns), and architecture improvements (adding CDN caching, optimising database queries, implementing Redis caching for hot data).
The Approach
We didn’t make all changes at once. Each optimisation was deployed independently, monitored for 48 hours, and validated against both cost and performance metrics before moving to the next one. This methodical approach meant zero surprises and clear attribution of savings.
We also set up cost monitoring dashboards and alerts so the client’s team could track spend in real-time going forward — no more end-of-month surprises.
The Result
Monthly cloud spend dropped from $45,000 to $27,000 — a 40% reduction. Average page load time improved from 4.2 seconds to 1.8 seconds. The site handled their next major sale event without any performance degradation, processing 3x normal traffic without manual intervention. The auto-scaling and monitoring infrastructure we built continues to manage itself.