Case Study 01$12KMonthly savings after remediation. 92%Traffic traced to a single bucket. 50%Faster incident resolution.
Solving a 500% AWS S3 Cost Surge
A mid-sized SaaS company saw AWS S3 costs rise from $3,000 to $15,000. CloudPi helped the team understand the cause of the surge, isolate the main sources of traffic, and reduce avoidable cloud spend.
Background
The company experienced a sudden 500% increase in monthly AWS S3 cost. While the billing data made the spend change visible, it did not clearly explain which bucket, request pattern, or internal workflow had caused the increase. That left the team with a cost problem, but without enough context to respond quickly.
Challenge
The practical challenge was not just cost growth. It was the lack of clear attribution. The team needed to determine whether the increase was caused by internal automation, public access, or workload behavior. Without clearer visibility, investigation remained too broad during an active incident.
Low clarityThe initial cost view did not identify the responsible bucket or workflow.
Slow analysisToo much time was spent narrowing the search space.
High waste riskSpend could continue to rise while teams investigated.
Solution
CloudPi surfaced a 400% increase in GetObject activity and traced 92% of the unexpected traffic to one bucket. The analysis showed that the main causes were an overactive development sync process and unauthorized public-bucket downloads. That gave the team a focused remediation path instead of a broad and time-consuming investigation.
Anomaly detectionThe request spike was surfaced early and clearly.
AttributionTraffic was mapped back to one dominant source.
ActionabilityThe team could fix the real issue instead of guessing.
Results
After remediation, the company reduced avoidable monthly spend by $12,000 and improved incident response speed by 50%. Beyond the savings, the company also gained a more repeatable process for reviewing future cost anomalies and connecting billing spikes to accountable workloads.
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