Cost-Aware Logging: Measuring the Financial Impact of Excessive Log Retention in Small-Scale Cloud Deployments

Log data plays a critical role in observability, debugging, and performance monitoring in modern cloud-native systems. In small and early-stage cloud deployments, however, log retention policies are frequently configured far beyond operational requirements, often defaulting to 90 days or more, without explicit consideration of their financial and performance implications. As a result, excessive log retention becomes a hidden and recurring cost. This study examines the financial and operational impact of log retention window selection from a cost-aware perspective. Using synthetic log datasets designed to reflect real-world variability in log volume and access patterns, we evaluate retention windows of 7, 14, 30, and 90 days. The analysis focuses on three metrics: storage cost, operationally useful log ratio, and cost per useful log. Operational usefulness is defined as log data accessed during simulated debugging and incident analysis tasks. The results show that reducing log retention from 90 days to 14 days can lower log storage costs by up to 78 percent while preserving more than 97 percent of operationally useful logs. Longer retention windows provide diminishing operational returns while disproportionately increasing storage cost and query overhead. These findings suggest that modest configuration changes can yield significant cost savings without compromising system reliability. Rather than proposing new logging mechanisms, this work offers a lightweight and accessible framework to help small engineering teams reason about log retention policies through a cost-effectiveness lens. The study aims to encourage more deliberate observability configurations, particularly in resource-constrained cloud environments.