Cloud Cost Optimization Plan Builder

Document cloud cost findings and a reduction action plan with savings targets

Build a cloud cost optimization plan: log your current spend by category, list waste findings (idle resources, oversized instances, unattached storage), assign recommended actions, and the tool totals projected monthly and annual savings.

What is cloud cost optimization?

Cloud cost optimization, part of FinOps, is the practice of reducing cloud spend without harming performance by eliminating waste and matching resources to actual demand. Common levers include shutting down idle resources, rightsizing oversized instances, and buying commitments for steady workloads.

Turn a cloud bill into an action plan with numbers

Cloud bills grow quietly: an idle instance here, an oversized database there, a forgotten storage volume. The fix is not a spreadsheet of complaints but a prioritized plan with a number next to every action. This builder lets you log each waste finding with its recommended fix and expected saving, then totals the impact so you can show leadership exactly how much you will recover.

How it works

You record your current monthly spend by category as a baseline, then list findings. Each finding has a current monthly cost, a recommended action, and an estimated monthly saving. The tool computes:

  • Total projected monthly saving — the sum of per-finding savings.
  • Projected annual saving — monthly saving multiplied by 12.
  • Percent of spend recovered — total saving divided by your total current spend.

The classic finding categories are idle resources (turn off), oversized instances (rightsize to a smaller type), unattached storage and old snapshots (delete), and steady workloads (cover with a Savings Plan or Reserved Instance). All math runs in your browser; nothing is uploaded.

Tips and example

Start with the quick wins. Shutting down a development environment overnight and on weekends can cut its cost by roughly two thirds at near-zero risk. Deleting unattached volumes and stale snapshots is pure savings with no performance impact.

For rightsizing, base the new size on real usage, not a guess. An instance averaging 10% CPU over a month can typically drop one or two sizes; halving the instance size usually halves its hourly cost. Capture the before and after size in the action so the saving is auditable.

Reserve commitments for the steady baseline only. A workload that runs 24/7 every month is a good candidate for a Savings Plan at a 30 to 50 percent discount; a spiky or experimental workload is not, because you would pay for the commitment whether or not it runs.