How to reduce the cold start in AWS Lambda function?

What is a Cold Start?

When a Lambda function is invoked for the first time (or after being idle), AWS needs to provision a new execution environment, download the code, start the runtime, and initialize the handler. This delay is called a cold start and can range from a few hundred milliseconds to several seconds(which definitely costs 🤑).

Strategies:

Choose a Faster Runtime
This one is my favourite, to leverage the power of high-performance and memory-efficient languages like Go and Rust makes a big difference, as they are very fast. Python and Node.js are fast and widely used as well, with support for a lot of libraries. Avoid JVM based runtimes unless necessary.

Reduce Memory-CPU Imbalance
Lambda allocates CPU proportionally to memory. A higher memory setting means more CPU and better networking, which speeds up initialization. Many times shifting from 128MB → 512MB can cut cold start time significantly, often at negligible extra cost. Note that more memory doesn't necessarily reduce overall execution time, but it does significantly affect cold start. This helps in most cases.

Use Provisioned Concurrency
This is one of the direct fix, it keeps a set number of Lambda instances pre-warmed and ready, eliminating cold starts entirely for those instances.

Reduce Deployment Package Size
The larger your package, the longer Lambda takes to download and initialize it. Use Lambda Layers to share common dependencies across functions. Avoid bloated SDKs, import only what you need.

Optimize Initialization Code
Keep the handler lean. Lazy-load resources that aren't always needed.