AWS Lambda Layers Made Simple: The Complete Beginner’s Guide (2025)

AWS Lambda is one of the most widely used serverless computing services, which enables developers and companies to run code without provisioning or managing servers. However, as the project grows, Lambda functions often end up with duplicate dependencies, bulk packages, and repetitive code. This is where Lambda Layers come in.

In this article, we will delve into Lambda Layers and explore when to use them and when not to.

What are Lambda Layers?

A Lambda Layer is essentially another Node.js project that contains libraries, dependencies, or code blocks that we reuse frequently. This allows us to separate dependencies from the business logic.

Instead of bundling the same libraries like axios, uuid, or logging utilities in every function, we can package them into a layer and reuse them across multiple Lambda functions.

Each Lambda function can be attached to up to 5 layers, which will be merged with the function during runtime.

Why use Lambda Layers?

Reduced Deployment Size

Without layers, every Lambda function will have the commonly used packages. For example, if we have 10 lambda functions that use the package axios, each deployment package will include axios. This leads to comparatively bigger package sizes.

With Lambda Layers, we can upload the package axios into a layer and make all functions simply reference it. This reduces package duplication and keeps the function lightweight.

Code Reusability

Layers make it easy to share common code logic across multiple functions. For example, we can place a logger util in layers and share it across functions to use it.

Faster Deployments & Easy Maintainance

Since layers separate dependencies from the function, we only publish layers when the layer code or package changes. This avoids unnecessary redeployment of bulky packages when you have just changed a simple code logic or a minor package upgrade.

Versioning

Each time we publish an updated layer, AWS automatically assigns a new version number to it. Lambda functions can point to a particular version of a layer. This helps when some of the function depends on an older version of a particular package.

Example:

Layer V1 has axios@2.xxx.xx

Layer V2 has axios@3.xxx.xx

Let’s say we have a function A depending on axios@2, and we have to migrate all the other functions to axios@3. We can just point the function A to Layer V1, and the rest to Layer V2.

How Lambda Layers Work

At runtime, Lambda extracts the contents of each layer and mounts them into specific directories:

/opt/nodejs/node_modules → for Node.js dependencies

/opt/bin → for binaries

/opt/lib → for libraries

The lambda functions can just require these modules directly.

Example: logger

Let’s create a custom logger and share it across functions.

Lambda dashboard

In your Lambda dashboard, go to Layers and create a Layer named logger_layer, and let’s upload a zip that contains the logger code.

Layer code

export function getLogger() {
  return {
    info: (msg) => console.log(`[INFO] ${msg}`),
    error: (msg) => console.error(`[ERROR] ${msg}`)
  };
}

Create Layer

Optionally, we can specify compatible architectures, runtimes, etc…

Layer information

Now, let’s attach this Layer to a Lambda function

Function code

import { getLogger } from '/opt/logger.js';
const logger = getLogger();

export const handler = async (event) => {
  logger.info("Lambda started");
  return { status: "ok" };
};

Adding a lambda layer

We can attach the Layer by providing the Layer’s ARN and verifying it.

Let’s test it with a sample event and see if the log has been made

Hurray! We can see the log. In a similar way, we can separate commonly used code out of functions into a layer, reducing code duplication.

Limitations of Lambda Layers

• Size Limit: 50 MB compressed, 250 MB uncompressed
• Max Layers: 5 per function
• Cold start Overhead: Layers can slightly increase cold start time
• Versioning: If we update a layer, we have to update every function to the new layer version manually

Best Practices

• Group dependencies logically (logging utils, AWS libraries,…)
• Avoid putting large libraries in layers
• Clean up layer versions occasionally

Conclusion

AWS Lambda Layers are a powerful feature for code reuse, dependency management, and cleaner deployments in serverless applications. By separating business logic from libraries, we reduce duplication, accelerate deployments, and simplify updates.

However, Layers should be used with size limits, versioning, and cold start implications in mind.

About the Author

Renjith R T, a full-stack developer with nearly 3 years of hands-on experience. I’ve contributed to various projects, honing my skills in application development. My expertise spans a range of technologies, including Flutter, Angular, ASP.NET, and Node.js. I’m passionate about creating seamless user experiences and eager to continue pushing boundaries in the tech industry.

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