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.

翻译：日志数据在现代云原生系统的可观测性、调试和性能监控中扮演着关键角色。然而，在小型及早期云部署中，日志留存策略的配置时长常远超实际运维需求，通常默认设置为90天或更长，而未明确考量其经济成本与性能影响。因此，过度日志留存成为一项隐蔽且持续发生的成本。本研究从成本感知视角出发，系统考察日志留存窗口选择的经济与运维影响。通过使用模拟真实场景中日志量与访问模式变化的合成日志数据集，我们评估了7天、14天、30天和90天四种留存窗口。分析聚焦于三项指标：存储成本、运维有效日志比率及单位有效日志成本。运维有效性定义为在模拟调试与事件分析任务期间被实际访问的日志数据。结果表明：将日志留存期从90天缩短至14天，可降低日志存储成本最高达78%，同时保留超过97%的运维有效日志。更长的留存窗口虽能带来逐渐递减的运维收益，却会不成比例地增加存储成本与查询开销。这些发现表明，适度的配置调整可在不影响系统可靠性的前提下实现显著的成本节约。本研究并未提出新的日志记录机制，而是提供了一个轻量级、易实施的框架，帮助小型工程团队通过成本效益视角理性制定日志留存策略。该研究旨在推动更具审慎性的可观测性配置实践，特别是在资源受限的云环境中。