Score how well a team turns optimisation recommendations into realised savings, and value the backlog left on the table.
Identifying savings is easy and free, so the score weights realisation most heavily and treats an explicit dismissal as a valid outcome — a documented decision not to act is far better than a recommendation quietly aging in a queue. Velocity is scored as a ratio of target to actual days, so beating the target cannot inflate the score beyond 100. Most cloud cost programmes fail in the gap between the recommendation and the change, and putting an annual dollar figure on the untouched backlog is usually what unlocks engineering time. The weights are a default starting point, not an industry standard.
Cloud Optimization Score
score = realisation rate × 35% + action rate × 20% + (target days ÷ actual days) × 20% + architecture coverage × 25%.
score = realisation rate × 35% + action rate × 20% + (target days ÷ actual days) × 20% + architecture coverage × 25%. Identifying savings is easy and free, so the score weights realisation most heavily and treats an explicit dismissal as a valid outcome — a documented decision not to act is far better than a recommendation quietly aging in a queue. Velocity is scored as a ratio of target to actual days, so beating the target cannot inflate the score beyond 100.
Most cloud cost programmes fail in the gap between the recommendation and the change, and putting an annual dollar figure on the untouched backlog is usually what unlocks engineering time. The weights are a default starting point, not an industry standard.
This calculator takes 10 inputs: Recommendations still open, Recommendations actioned, Recommendations dismissed with a reason, Monthly saving identified, Monthly saving actually realised, Mean time to action a recommendation, Target time to action, Workloads right-sized in the last quarter, Workloads behind auto-scaling, Workloads on a current-generation instance family. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.
Many recommendations are wrong for good reasons — the instance is oversized for a known seasonal peak, or a reservation is being held for a migration. Recording that decision closes the loop and stops the same suggestion recurring, which is a genuinely better outcome than leaving it open forever.