Cost Optimization on Cloud Infrastructure Without Sacrificing Reliability
A client's AWS bill had grown to roughly $22,000/month and their instinct going in was that cost cutting would mean smaller instances and accepting more risk. Almost none of the savings we found actually involved that trade-off. The single biggest line item, at nearly $6,000/month, was over-provisioned RDS instances sized for a traffic spike eighteen months earlier that had never recurred — we right-sized based on 30 days of actual CPU and memory metrics, not the original estimate.
Reserved Instances and Savings Plans accounted for the next largest chunk. The client was running 100% on-demand pricing for a baseline workload that had been stable for over a year — predictable enough to commit to a 1-year Savings Plan for the steady-state load while keeping on-demand and Spot for genuinely variable capacity. That single change saved about $3,200/month with zero architectural changes and zero reliability impact, since it's purely a pricing commitment.
We moved batch and non-time-sensitive workloads — nightly report generation, image reprocessing, log archival — to Spot Instances, which run at 60-70% below on-demand pricing in exchange for possible interruption with two minutes' notice. This is exactly the trade-off Spot is designed for: workloads that can checkpoint and resume are excellent Spot candidates, and none of these had a customer-facing latency requirement that interruption would threaten.
S3 storage class transitions were another easy win: old order-history exports and log files were sitting in S3 Standard indefinitely. We added lifecycle rules moving objects to Infrequent Access after 30 days and Glacier after 180, which cut storage costs on that bucket by more than half with no change to how quickly recent, actively-used data could be accessed.
The one place we explicitly declined to cut costs was the production database's Multi-AZ standby and the load balancer's minimum instance count — both add cost specifically to protect availability, and neither showed up as 'waste' in our audit. Total savings landed at 38% of the original bill, and the client's on-call engineer confirmed nothing about incident response or capacity headroom felt different afterward, which was the actual bar we were optimizing against.
