Evaluating Performance and User Perceptions of Multi-Cloud Database-as-a-Service Solutions: A Mixed-Methods Study

Abstract

Multi-cloud adoption has accelerated the use of managed Database-as-a-Service (DBaaS) platforms, yet limited research provides provider-agnostic, empirical insight into how performance, scalability, and cost differ across vendors or how these objective outcomes align with practitioner perceptions. This paper presents a mixed-methods evaluation of three major cloud database platforms: Amazon RDS, Azure SQL Database, and Google Cloud SQL. Quantitative benchmarking experiments were conducted using identical workloads on the benchANT framework to measure latency, throughput, and scalability, while a parallel survey collected perception data from academic lecturers, IT employees, and undergraduates. The study's novelty lies in triangulating benchmark results with user perceptions to reveal alignment gaps. Empirical findings show that AWSRDS and Google Cloud SQL deliver strong horizontal scalability, while Azure SQL favors vertical scaling but exhibits higher latency. A moderate correlation (r = 0.62) was found between perceived performance and measured throughput, but perceived reliability showed a weaker relationship to actual latency (r = 0.34), indicating notable perception divergences. Cost analysis further highlights trade-offs involving egress fees, storage tiers, and replica placement. The results provide actionable guidance for workload placement, elasticity planning, and multi-cloud governance, and demonstrate the value of a mixed-methods approach for evaluating cloud database services.