Cutting Cloud Spend Without Cutting Reliability
Cloud bills rarely grow because of one bad decision. They grow because every individual decision was defensible at the time, and nobody has revisited any of them since. The instance sized for a launch spike three years ago is still running. The staging environment nobody uses still bills at production rates.
Start by measuring what you actually use
Before changing anything, pull two weeks of utilisation data for every resource. In most audits we run, average CPU across the fleet sits under twenty percent. That is not a sign of good headroom; it is a sign that nobody has looked.
Where the money usually is
- Oversized instances sized for a peak that either never came or is now handled by autoscaling.
- Non-production environments running around the clock when they are used eight hours a day, five days a week.
- Storage that was never lifecycled — old snapshots, orphaned volumes, logs retained forever by default.
- Data transfer between availability zones that a small architecture change would remove entirely.
- Managed services chosen for convenience early on, still running at a tier the workload outgrew in the other direction.
Do not trade reliability for the invoice
The failure mode here is cutting until something breaks, then over-provisioning again in a panic. Set your reliability targets first — what uptime you actually need, what recovery time you can live with — and treat those as the floor. Every reduction gets tested against a realistic load profile before it reaches production.
Done in that order, a thirty percent reduction is a routine outcome rather than a risky one. We have yet to run an audit that did not find at least a fifth of the bill sitting in resources nobody would defend if you asked them directly.
