AWS Lambda layers are specifically designed to share common code and dependencies across multiple Lambda functions. A layer is a ZIP archive that contains libraries, a custom runtime, or other function dependencies. Layers are versioned, so the developer can publish a new layer version when libraries change and then update functions to reference the new version. This directly meets the requirements to centralize libraries, update conveniently, and keep libraries versioned—while requiring minimal development work.
Using a single shared layer significantly reduces duplication across function deployment packages. It also simplifies CI/CD because the developer can build and publish the layer independently and reuse it across many functions.
Option A (CodeArtifact) is useful for dependency management, but it still requires each Lambda deployment to package dependencies or download them during build. It’s more moving parts than a layer for this use case.
Option B (container images) can centralize dependencies but typically requires more effort: building, scanning, versioning container images, and updating function image URIs. It’s heavier operationally than layers for “shared custom libraries.”
Option D (EFS) can be mounted by Lambda, but introduces networking considerations (VPC config), EFS lifecycle/permissions, and is unnecessary for simply sharing libraries. Also, cold start and operational overhead can increase.
Therefore, creating a Lambda layer for the shared libraries is the least-effort, most standard approach.
