Allocating the most competent crowdworkers to each upcoming task is a fundamental challenge in crowdsourcing. The mechanism becomes complicated when the arriving tasks require a high level of expertise within a constrained budget. The validation of skill matching between tasks and crowdworkers adds a new dimension to the traditional problem of task allocation. In addition, in real-world scenarios, the influx of both tasks and workers is dynamic, making it nearly impossible to predict the precise amount of computational resources required for the crowdsourcing platform to operate efficiently. Serverless computing is a new pay-per-use, auto-scalable, Function-as-a-Service based model, that ensures parallel execution of lightweight event-driven functions. The developer with serverless can solely concentrate on writing application logic with zero effort on resource provision, server management, environmental configuration, and availability. Today, collaboration has become the new competition. In light of these considerations, we propose a novel framework to facilitate task allocation strategies for crowdsourcing applications, deployed within a serverless platform in order to improve performance. The results obtained are compared to the baseline, Online-Greedy, and simulations are run in both serverless and local environments.