At its core, Amazon Aurora Serverless, an extension of the popular PostgreSQL database service, is designed to provide high-performance and availability for large workloads in the cloud. However, when benchmarked against Timescale, a PostgreSQL database optimized for time-series workloads, it fell short in several key areas, including ingest speed, query performance, data size efficiency, compute costs, and storage costs. The results show that Timescale outperforms Aurora in all dimensions, making it a more efficient and cost-effective choice for large-scale PostgreSQL deployments, particularly those focused on time-series workloads. Despite its high-performance capabilities, Aurora's closed-source technology and lack of transparency around its internal workings, such as the ACU billing unit, hindered its ability to match Timescale's performance and efficiency.