Interview › Resume & Behavioral
At VGS you reduced AWS spend by 25% - what specifically did you change and how did you avoid hurting reliability?
Resume & Behavioral · Basic level
Answer
I approach cost optimization as reliability-aware engineering, not blind cutting. I first build visibility by service, account, tag, environment, and usage pattern, then identify over-provisioned compute, idle resources, storage growth, data transfer, NAT costs, and commitment opportunities. Any change must preserve SLOs and headroom, so I validate with utilization data, load testing, canaries, and post-change monitoring. Cost savings are valuable only if they do not create fragility.
Technical explanation
Cost and reliability must be evaluated together: a cheaper system that misses SLOs is not a win.
Common levers include rightsizing, autoscaling, non-production schedules, storage lifecycle, data-transfer reduction, and Savings Plans/Reserved Instances for stable usage.
Measure before/after with spend, utilization, latency, saturation, error rate, incident count, and rollback readiness.
Hands-on example
1. Rank top spend drivers by service/team/environment and validate tags.
2. For a high-cost service, review 30-90 days of CPU, memory, network, p95/p99 latency, request volume, and scaling events.
3. Test rightsizing or autoscaling in staging/canary, then roll out gradually with dashboards and rollback.
4. Report monthly savings alongside reliability metrics so leadership sees both value and safety.
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