Scalable and Cost-Efficient Conversational Surveys through Serverless Design and Implementation

Abstract

The rapid evolution of conversational interfaces has transformed traditional data collection methods into interactive, adaptive, and user-friendly experiences. However, deploying and scaling conversational survey systems using conventional cloud infrastructures can be complex and costly. This research paper explores a serverless computing paradigm for designing, implementing, and scaling conversational surveys efficiently. The study focuses on leveraging cloud-native services such as AWS Lambda, Azure Functions, and Google Cloud Functions to create a scalable, event-driven architecture that minimizes cost and operational overhead. The proposed model allows real-time engagement with participants, dynamic data storage, and elastic scaling based on user demand. Through experimental evaluations involving 1,000 simulated users, the serverless-based conversational survey system demonstrated superior scalability and a 65% reduction in cost compared to traditional VM-based architectures. The results validate the feasibility and performance advantages of serverless design for large-scale conversational applications, offering a sustainable model for research institutions, marketing agencies, and public data collection platforms.