AWS Lambda functions are designed to allow developers to write scalable code without worrying about infrastructure details, but this presents a challenge for operators and developers who want to monitor and optimize their functions. Analyzing metrics such as function invocation time and HTTP request timing data can help understand the latencies of different components, including cold start times, which refer to the increased invocation time when a function is not used for a long period. Observing cold start times in real-world scenarios shows that while warming a simple Lambda function may provide some benefits, other considerations such as network latency and TLS negotiation have a more significant impact on overall request completion times. To optimize Lambda functions, developers should ask questions such as whether the function performs CPU-intensive tasks or makes many network requests, how to reduce overall function size, and whether function warming has any meaningful impact compared to an identical cold function in the same region. Ultimately, serverless computing does not mean no servers, but rather a shift in thinking about infrastructure and performance data collection and analysis.