Google Cloud Functions in Serverless Computing: A Comparative Literature Review and Case Study

Aditya Srikar

Abstract: Serverless computing allows developers to run event-driven code without managing infrastructure and has become a key choice for APIs, data pipelines, and automation. This review evaluates Google Cloud Functions (GCF) in comparison with AWS Lambda and Azure Functions, focusing on architecture, performance, cost, and practical usage. Using a narrative review of peer-reviewed studies, official benchmarks, and vendor documentation from 2016?2025, the study synthesizes evidence most relevant to platform selection. Findings indicate that GCF Gen 2 reduces cold start times relative to Gen 1, supports multiple concurrent requests per instance, and integrates tightly with GCP services like Pub/, Firestore, and Cloud Storage. AWS Lambda offers highly predictable cold start latency when Provisioned Concurrency is used, while Azure Functions provides native stateful workflows through Durable Functions and pre-warmed instances. Cost models are similar across platforms, but GCF?s generous free tier favors sporadic, short-duration workloads. Overall, platform choice should consider latency requirements, workflow statefulness, ecosystem fit, and workload patterns.

Keywords: Google Cloud Functions, serverless computing, Functions-as-a-Service, AWS Lambda, Azure Functions

How to Cite?: Aditya Srikar, "Google Cloud Functions in Serverless Computing: A Comparative Literature Review and Case Study", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 53-56, https://www.ijsr.net/getabstract.php?paperid=SR251026080834, DOI: https://dx.doi.org/10.21275/SR251026080834