AWS Lambda Cold Start Estimator

A cold start occurs when Lambda must initialize a new execution environment for your function — downloading code, starting the runtime, and running initialization code outside the handler. This adds latency ranging from ~100ms for small Node.js functions to 3s+ for large Java/C# functions with heavy dependencies.

Node.js and Python have the fastest cold starts (typically 100-500ms for small packages). Go and Rust are also fast. Java, .NET, and Kotlin have the slowest cold starts (500ms-3s+) due to JVM/CLR startup overhead. Using Lambda SnapStart (Java) or GraalVM native image can dramatically reduce Java cold starts.

Yes — Lambda allocates CPU proportional to memory. More memory means more CPU for initialization, which reduces cold start duration. Going from 128MB to 1024MB can halve cold start time. Beyond 1769MB you get 2 vCPUs and gains diminish. The trade-off is higher cost per invocation.

Larger deployment packages take longer to download and extract during initialization. Packages under 5MB have minimal impact. 50-100MB packages add 200-500ms. Reducing dependencies with tree-shaking or using Lambda Layers (shared cached dependencies) significantly reduces cold start times.

Lambda SnapStart (Java only) takes a snapshot of the initialized execution environment and restores it on cold starts, reducing Java cold starts to under 1 second. Use it for Java functions in production where cold start latency impacts user experience. It requires Java 11+ and Corretto runtime.