International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
A REVIEW OF COMPARATIVE STUDY OF SERVERLESS COMPUTING PLATFORMS: AWS LAMBDA vs. GOOGLE CLOUD FUNCTIONS vs. AZURE FUNCTIONS Iqrar Nisar1, Dipti Ranjan Tiwari2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - FaaS (Function-as-a-Service) and serverless
virtualized resources, shifted capital spending to operating expenses, yet required users to oversee and control the new systems. Through PaaS, infrastructure was made even more abstract and developers were provided ready resources and tools for programming, so they had to focus less on OS and middleware management. Now, serverless computing, especially through services called Function as a Service (FaaS), is the newest development on this continuum, making it even easier to break down resource management and task execution into smaller pieces. Serverless automates the management of servers such as provisioning, scaling, patching and planning for capacity which is a big change for creating and deploying cloudnative applications.
computing have made it possible to build cloud applications by managing infrastructure on a needed basis and reacting to events. In this review, we compare the three top serverless platforms—AWS Lambda, Google Cloud Functions (GCF) and Azure Functions—thoroughly. We examine peerreviewed research (2020–2024), company materials and real-world results to assess both technical and cost factors, as well as how mature the technology is. AWS Lambda manages to reach 32,000 requests per minute, offers a good variety of options to trigger functions, but cold starts take the most time. GCF Gen2 gives users the fastest cold starts (one-eighth of a second) along with the flexibility of containers and a 40% discount on processing costs per request for stream data. Azure Functions allows hybrid hosting and the Premium option has the next-highest cold start duration (a little over one second). Durable Functions from Azure allow developers to use stateful workflows. Certain problems remain the same regardless of the platform such as VPC making cold starts slower by at least 1.2 or as many as 4.8 seconds, vendor lock-in reduces the ability to port applications and issues with observability make distributed tracing more challenging. Because of this structure, we discover that Lambda saves money for tasks that happen now and then, GCF is efficient for handling a lot of data and Azure is the right match for programs with recurring changes of state. Based on the review, there are several urgent areas where research is required such as benchmarking, standardizing security and designing serverless systems sustainably.
1.2. Serverless Computing Defined Serverless computing refers mainly to the Function-as-aService (FaaS) model, as seen in this review. Functions are based on Event-Driven Execution which means functions are only executed in response to specific events like an HTTP request; they are managed by the cloud without user involvement, including automatic scaling; charges are only made according to the amount of resources needed for function execution; and this happens quickly, automatically, with zero interference from the user. While the terms Function as a Service (FaaS) and serverless are commonly mixed, this paper is about FaaS rather than all other serverless products.
1.3. Benefits of Serverless
Key Words: Serverless Computing, Function-as-a-Service (FaaS), Comparative Analysis, AWS Lambda, Google Cloud Functions (GCF), Azure Functions, Performance Evaluation.
Choosing a serverless FaaS model provides many benefits that are helping it gain wide interest. Operational overhead should be cut down significantly, since developers escape from managing servers, updating the OS, keeping things running and handling infrastructure issues, giving them more time to build the essential parts of the application. For this reason, delivery of products and updates gets quicker as challenges in infrastructure and deployment are resolved. For apps that often have Sporadic or Bursty Workloads, the Pay-per-Use model saves a lot of money because you only pay for the time your code is active. The platform has Inherent, Granular
1. INTRODUCTION 1.1. Background: Evolution of Cloud Computing The process of providing resources for computing and deploying applications has changed from using (onpremises) servers to using cloud services. This growth included Infrastructure as a Service (IaaS) that offered
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