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Understanding Lambda layers

Let's say we are developing multiple Python functions. Each function uses a number of Python modules that are packaged and deployed with the code each time you create or update a function. A lot of these modules are likely to be common across the Lambda functions, so we are duplicating the amount of storage space that's needed for each function. This has an impact on cost and function initialization performance since each time an execution is scheduled on a new host, it needs to pull the deployment package from S3. It also has an impact on the manageability of module versions if they are spread across multiple locations.

The following diagram shows two Lambda functions, both using the same libraries (A and B), ...