The cloud data management infrastructure is being transformed by serverless databases because of their operational simplicity, usage-based pricing, and elastic scalability. However, their performance in real-world workloads analysis is still unexplored. This paper presents an in-depth analysis of serverless database systems using simulation-based benchmarks evaluating Aurora Serverless and FaunaDB against RDS PostgreSQL. We simulate cold start latencies, dynamic cost settlement, autoscaling behaviors, transaction throughput, and various cost per transaction efficiencies. Our findings reveal up to 45% cost saving in burst-heavy workload scenarios while exposing the latency costs stemming from cold starts and storage rehydration during recovery. Throughput and stream-level metrics are evaluated highlighting IOPS, CPU consumption, query drop rates revealing the critical Elapsed Time benchmarks and operational choke point windows. This work provides direct guidance for system designers and cloud served database users seeking to shift from provisioned static architectures, fueling upcoming research addressing surge anticipation, data processing, and distributed multi-cloud frameworks for real-time replication in data-centered systems.
Jonas E, Schleier-Smith J, Sreekanti V, Tsai C, Khandelwal A, Pu Q, et al. Cloud programming simplified: A berkeley view on serverless computing. 2019;
2.
B A, S H, G S, J.E V, T SS. An Efficient Privacy - Aware Authentication Framework for Mobile Cloud Computing. International Academic Journal of Innovative Research. 2023;10(1):1–7.
3.
Papadopoulos G, Christodoulou M. Design and Development of Data Driven Intelligent Predictive Maintenance for Predictive Maintenance. Association Journal of Technics in Engineering Mechanics. 2024;(2):10–8.
4.
Gupta P. A developer-centric compliance tool for serverless applications [dissertation]. 2024;
5.
Fusaro V, Patil P, Gafni E, Wall D, Tonellato P. Biomedical cloud computing with Amazon Web Services. PLoS Computational Biology. 2011;(8):1002147.
6.
Castillo M, Mansouri A, A. Big Data Integration with Machine Learning Towards Public Health Records and Precision Medicine. Global Journal of Medical Terminology Research and Informatics. 2025;(1):22–9.
7.
Bansal M, Naidu D. Dynamic Simulation of Reactive Separation Processes Using Hybrid Modeling Approaches. Engineering Perspectives in Filtration and Separation. Jun. 2024;8–11.
8.
Kodakandla N. Serverless architectures: A comparative study of performance, scalability, and cost in cloudnative applications. Iconic Research and Engineering Journals. 2021;(2):136–50.
9.
Nwosu P, Adeloye F. Transformation leader strategies for successful digital adaptation. Global Perspectives in Management. 2023;(1):1–6.
10.
Kapoor P, Malhotra R. Zero Trust Architecture for Enhanced Cybersecurity. :56–73.
11.
Palepu S, Chahal D, Ramesh M, Singhal R. Benchmarking the data layer across serverless platforms. 2022;3–7.
12.
McGrath G, Brenner PR. Serverless Computing: Design, Implementation, and Performance. 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE; 2017. p. 405–10.
13.
Barnhart B, Brooker M, Chinenkov D, Hooper T, Im J, Jha PC, et al. Resource Management in Aurora Serverless. Proceedings of the VLDB Endowment. 2024;17(12):4038–50.
14.
Ustiugov D, Petrov P, Kogias M, Bugnion E, Grot B. Benchmarking, analysis, and optimization of serverless function snapshots. Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. ACM; 2021. p. 559–72.
15.
Baldini I, Castro P, Chang K, Cheng P, Fink S, Ishakian V, et al. Serverless Computing: Current Trends and Open Problems. Research Advances in Cloud Computing. Springer Singapore; 2017. p. 1–20.
16.
Gupta P, Moghimi A, Sisodraker D, Shahrad M, Mehta A. Growlithe: A Developer-Centric Compliance Tool for Serverless Applications. 2025 IEEE Symposium on Security and Privacy (SP). IEEE; 2025. p. 3161–79.
17.
Martins H, Araujo F, da Cunha PR. Benchmarking Serverless Computing Platforms. Journal of Grid Computing. 2020;18(4):691–709.
18.
Pavlo A, Angulo G, Arulraj J, Lin H, Lin J, Ma L, et al. Self-Driving Database Management Systems. :1.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
