How Effective is AWS Lambda Auto Scaling in Reducing Costs under Variable Workload

Serverless computing has transformed cloud-based application deployment by offering on-demand scalability with minimal infrastructure overhead. AWS Lambda, one of the leading serverless platforms, promises automatic scaling and pay-as-you-go pricing, making it attractive for applications with variable workloads. This research investigates the effectiveness of AWS Lambda’s auto-scaling mechanism under different workload patterns — specifically, a step-up workload and an oscillating variable load. Using Locust for load generation and AWS CloudWatch for monitoring, key metrics such as response time, cold start behavior, concurrent executions, and cost were analyzed. Findings reveal that Lambda efficiently handles gradual traffic increases without request failures and maintains stable response times after initial cold starts. However, rapidly fluctuating loads introduced noticeable latency spikes due to cold starts, although no failures occurred. The pay-per-invocation pricing model ensured cost efficiency in all cases, with no idle costs incurred. Overall, the study demonstrates that AWS Lambda is well-suited for applications with unpredictable traffic, though further tuning may be required for latency-sensitive scenarios.