Mitigating the Cost Amplification of Platform Defaults in Lean Cloud Data Deployments Through Configuration Hygiene - Manuel O. Diaz (Transactions on Informatics and Data Science, 2025)
Cloud data platforms offer flexibility, but their default configurations are implicitly optimized for enterprise-scale environments, leading to hidden, disproportionate costs for small-scale deployments. This paper introduces the concept of a silent tax, where platform defaults, such as minimum billing durations and over-provisioned column types, disproportionately inflate costs for small business organisations with modest compute budgets, exemplified by a 10 IPU per month scenario. These defaults act as regressive cost drivers, penalizing users unable to amortize inefficiencies. We employ an analytical cost-modelling and Monte Carlo simulation approach, using Snowflake and Informatica Intelligent Data Management Cloud (IDMC) as reference platforms, and validate it through a paired, parametric Monte Carlo experiment using truncated normal and lognormal distributions. Our findings indicate that fixed-cost components embedded in these defaults can consume approximately 65% of a minimal budget. Key mechanisms include 60-second warehouse billing minimums, schema-driven memory over-allocation, and CI/CD pipeline reconfiguration costs. The study demonstrates that cost waste in minimal deployments is predominantly structural. An optimization pathway focusing on configuration hygiene—including explicit data types, aggressive warehouse tuning, and schema-as-code—can reclaim over 80% of this overhead, aligning with FinOps guidance for small estates. This research addresses a gap in cloud cost governance, empowering small teams to leverage cost intelligence for sustainability, and suggests transparent disclosure of default cost impacts by providers.