Serverless computing offers scalability and cost efficiency by abstracting infrastructure management [1], making it a preferred choice for cloud-based applications. However, it faces challenges such as cold start latency, execution overhead, and performance variability, [2] which impact application responsiveness. This paper presents a comprehensive performance analysis of AWS Lambda, Google Cloud Functions, and Azure Functions, focusing on response time, scalability, and cost efficiency. Using real-world workloads, we evaluate key performance factors, including cold start delays, warm execution efficiency, and handling of request bursts under varying levels of concurrency. Author tried to revealed that AWS Lambda achieved the lowest average cold start latency of 190 ms, approximately 30% faster than Google Cloud Functions (270 ms) and 45% faster than Azure Functions (350 ms). [3] In terms of scalability, AWS Lambda maintained stable throughput up to 1000 concurrent requests, while Google Cloud Functions began throttling beyond 800. Cost analysis showed Google Cloud Functions offered the lowest execution cost at $0.20 per million requests, compared to $0.25 for AWS Lambda and $0.28 for Azure Functions. Authors findings reveal that AWS Lambda consistently offers the best response time and scalability, while Google Cloud Functions provides a balanced trade-off between cost and performance. Azure Functions exhibit higher cold start latency and resource consumption, impacting cost efficiency in certain workloads. These insights help in optimizing serverless deployments for performance-critical applications and guide decision-making when selecting cloud platforms for different workloads.