CloudPi turns rightsizing into a controlled operating motion. Detect oversized resources, route each action through the right execution model, and prove the savings after the change lands.
Teams already know there is waste. The hard part is deciding what is safe, who should approve it, and how to act without creating incidents.
A useful rightsizing workflow combines utilization context, role-based control, and measurable outcomes. Otherwise every recommendation becomes another manual review queue.
Finance wants savings. FinOps exports a list. Engineering asks whether the resource is actually safe to change. CloudOps worries about incidents. Weeks later, only a few actions move.
CloudPi gives each rightsizing opportunity the level of speed and control it deserves. Low-risk actions can move automatically. Production changes can require approval. Complex workloads can become enriched tickets for engineering review.
CloudPi supports three rightsizing modes so teams can move quickly in low-risk environments and stay cautious where reliability matters.
Ideal for predictable lower-risk environments where the utilization pattern and policy guardrails already make the action safe enough to automate.
CloudPi presents the resource, recommended target size, utilization evidence, and savings estimate so teams can review before any change is made.
Instead of emailing recommendations, CloudPi routes the action into the tools teams already use with resource details, projected savings, and execution context attached.
Not every oversized resource should be treated the same way. CloudPi helps teams choose the right action path based on environment, risk, and ownership.
| Scenario | Without CloudPi | With CloudPi |
|---|---|---|
| Oversized dev instances Low-risk, repeatable, policy-safe workloads. |
Teams identify them manually, then wait for someone to make the change when time allows. | Autonomous policy executes the rightsize action automatically and records the outcome. |
| Production app servers Utilization is low, but reliability matters. |
Recommendation sits in review because nobody wants to approve it without enough context. | Approval-gated workflow shows evidence, projected savings, and target size before execution. |
| Shared platform services Multiple teams depend on the same workload. |
FinOps sends a recommendation and waits for engineering to investigate manually. | Enriched ticket routes the opportunity with technical and financial context already attached. |
| Board reporting Leadership wants the savings number. |
Teams report theoretical opportunity totals that may never become real savings. | TRUE Savings reports what was actually executed and what changed on the bill. |
Rightsizing has more credibility when the organization can see how much moved, what remained blocked, and what actually changed on the bill after execution.
Track oversized resources by account, environment, service, or team — with 30-day utilization evidence already attached to each opportunity.
Know which recommendations were accepted, who approved them, and when — with a complete audit trail attached to every decision.
Separate actions that actually ran from those still pending — execution state is always visible, timestamped, and traceable back to the approver.
TRUE Savings compares actual billing data before and after execution — so every reported saving is tied to a real invoice change, not a projected estimate.
Explore how teams across industries use CloudPi to move from opportunity discovery to verified savings.
Dev and QA environments were running on over-provisioned instances for months. CloudPi automated the fix overnight.
Read case study →A structured approval workflow gave CloudOps the control they needed to move fast on production recommendations.
Read case study →Jira tickets enriched with utilization data and savings estimates cut the average resolution time from 3 weeks to 2 days.
Read case study →CloudPi gives your team the detection, workflow, and proof layer needed to move faster without risking reliability. See it working on your own cloud environment.
CloudPi keeps each team in the loop without forcing everyone into the same workflow or level of responsibility.
Set rules that define what can execute automatically and what needs a human decision. Track every recommendation through its full lifecycle — from detection to billing confirmation.
Finance gets a single view of how much was identified, how much was approved, how much executed, and how much changed on the bill — with no gap between the claimed and the real number.
Every action that hits the approval queue includes the resource, recommended target, 30-day utilization trend, projected savings, and estimated risk — so decisions are fast and informed.
Instead of chasing context across consoles and email threads, engineers receive tickets pre-loaded with the current size, recommended target, CPU/memory trend, and estimated savings.