AWS Lambda: The Most Comprehensive Guide for 2024


Launched on November 13, 2014 — AWS Lambda has proved itself an integral tool in the cloud-based services realm. Serverless, or without a dedicated server, implies that AWS Lambda lets you run your applications without worrying about provisioning or managing servers.

It works on a pay-per-execution model. This contrasts with traditional hosting methods, where you’d have to pay for servers irrespective of their usage.

This event-driven and auto-scalable platform can natively run your code in response to changes in data or system state.

For instance,

  • File uploaded in an Amazon S3 bucket or a record getting updated in the DynamoDB can trigger your AWS Lambda function accordingly.
  • Or, an API call via AWS API Gateway initiates the function to achieve the desired action.

In this article, we will delve deeper into how AWS Lambda works, its benefits, use cases, best practices, limitations/disadvantages, and lastly, pricing.

How Does AWS Lambda Work?

The short answer is via running its own container.

Whenever a particular event is triggered, AWS Lambda launches a container to execute the function.

One key aspect is that the container remains available to handle subsequent invocations, thus making the process more efficient by re-utilizing the existing containers and providing “warm starts.”

However, if a request is not triggered for a while, AWS takes down the container, resulting in “cold starts“.

Lambda leverages a VPC for secure system execution, and IAM roles add an extra layer of security to protect cloud operations. Users can customize the VPC for additional customization.

Moreover, AWS Lambda also automatically manages the infrastructure supporting your systems, scaling up or down the concurrency in response to incoming request traffic.

This eliminates the need for manual intervention when spikes in demand occur. The scalability is seamless and immediate, bringing in an enhanced user experience.

The Benefits of AWS Lambda

There are a host of benefits associated with implementing AWS Lambda. Let’s examine some of the key ones below:

Pay for What You Use

The pay-per-execution billing model put AWS Lambda in the spotlight.

The charges are levied based on the actual amount of compute time used by your functions, not on pre-allocated capacity.

AWS’s billing policy stands out amongst other cloud providers; instead of rounding server time up to the nearest hour, they round up to the nearest 100 milliseconds.

Scalability on Demand

Imagine a sudden surge in user traffic hitting your website; AWS Lambda will decisively adjust and scale your services to maintain optimal performance without needing to configure anything. The system dynamically fluctuates and can manage incoming requests, whether one or a million. This agility ensures minimal response time, achieving a high level of customer satisfaction.

Serves a Variety of Use Cases

From daily tasks, notifications, and processing S3 objects to chatbots — AWS Lambda can handle it all. This technology also supports real-time data processing, making streaming services such as Amazon Kinesis or AWS Glue viable. Due to its adaptability, AWS Lambda is not restricted to any background or industry. It offers solutions relevant to IoT, gaming, fintech and more.

Ship Faster With Automatic Management

Code can be shipped directly from the vendor console or Amazon API Gateway. This is all handled automatically without worrying about the underlying operating system, patches, or language runtimes. Quality assurance, security, and deployment processes are managed digitally to speed up the distribution, minimizing human error factors and increasing the productivity of your DevOps team.

Bid Goodbye to Operational Maintenance

Automatic scalability saves time and resources.

Tightening security protocols can eliminate the need for an administrator and result in cost savings, and technical staff can focus on product innovation and development.

Seamless Integration

Mass-mailing services, payment processors, or CRM tools, AWS Lambda’s compatibility with other AWS services enables the smooth integration and communication between all vital infrastructures within an ecosystem.

Even pairing combinations like Lambda & API gateway or Lambda & DynamoDB are simple to deploy and maintain.

From development to monitoring activities — everything can be organized using a single AWS control center.

Customer Experience Is at the Forefront

UX is held up to a high standard with AWS Lambda.

Swift and seamless operations allow developers to prioritize UX design—meaning faster loading times, reduced latency, and generally more responsive interfaces.

With its easy integration, AWS Lambda enables versatile application features. This leads to improved customer experience, audience retention, and engagement.

AWS Lambda Use Cases

There are nine distinctive use cases for AWS Lambda. Below is a brief summary of a few top ones.

  • Operating serverless websites: Whether you’re hosting static websites or single-page web applications, Lambda facilitates this without an actual server. What’s more – you can outsource various active functions like user authentication or form submissions. It proffers high availability, lacing prompt response with cost-effectiveness.
  • Swift document conversion: Users don’t always want documents (e.g., specifications, manuals, or transaction records) in one format. HTML works for some — while others need PDFs or more specialized formats. Storing static copies of these documents takes up space and isn’t practical if the content often changes. Lambda can quickly and easily generate them on the fly – retrieving, formatting, and converting content for display on a webpage or download.
  • Predictive page rendering: AWS Lambda can be used for more than just data cleanup. It can be used for predictive page rendering, which prepares webpages for display based on the likelihood that users will select them. Lambda-based applications can retrieve needed documents and multimedia, render them for display, and check for their availability from external sources (e.g., YouTube). If unavailable, alternate sources can be attempted.

Best Practices for Using AWS Lambda

There are several best practices that developers and companies should follow to get the most out of AWS Lambda:

  1. Ensure your Amazon Lambda functions do not utilize function URLs.
  1. Use the right-sized context when writing Lambda functions to optimize resource usage and management.
  1. Keep Cold-Start in mind. Cold starts happen when there is an initial delay in running a function after it has remained idle. Consider ‘warming up’ your functions by periodically invoking them.
  1. Pay attention to your security – implement suitable IAM roles and credentials, assign least privilege policies, and regularly audit these policies.
  1. Include limits for execution times when configuring your function, preventing cases that could cause prolonged uses of your Lambda functions.
  1. Conduct regular checks in the AWS Lambda management console to identify debugging and bottleneck issues.
  1. Always use the most recent runtime in AWS Lambda to ensure the latest patches and optimal performance.
  1. Isolate Lambda functions to not share resources by using sandbox functionality. This can result in better security and performance. Monitor your applications and Lambda functions using AWS CloudTrail.
  1. Optimize short-lived and long-lived Functions differently; understand that not every scenario requires keeping Lambda execution environments alive.
  1. Ensure that your Lambda functions are stateless to maintain scalability and reduce heavy execution burden.
  2. Know the AWS Lambda limits, whether that means the size of the deployment package or the maximum execution timeout. This can mitigate avoidable runtime errors.

Limitations and Potential Disadvantages of AWS Lambda

Sure, AWS Lambda comes with a pack of benefits, but like all technologies, it has some potential limitations and downsides to consider before integration.

Cold Starts

The issue of cold starts affects all serverless computing platforms, not just AWS Lambda. If a function isn’t invoked for some time, it goes into a state of ‘hibernation’ called a ‘cold start.’ This can delay the execution time of your Lambda function, where speed matters.

Local Testing is Complex

While not impossible, local testing of AWS Lambda functions is arduous and complex compared with traditional applications. More meticulous planning, tools, and time must be devoted to ensure the effective application of tests.

Limited Language Support

Be aware that AWS Lambda doesn’t support all programming languages. AWS Lambda provides native support for Java, Go, PowerShell, Node.js, C#, Python, and Ruby. However, if you use languages like R, Swift, PHP, or others, an AWS custom runtime may be required, complicating your setup process.

Maximum Execution Timeout

AWS Lambda functions must conclude within 15-minute windows; anything exceeding the limit causes an issue, particularly for long-batch processes. Concurrent AWS SQS service, for instance, would be more appropriate for such needs.

Complexity Around Handedness and Distribution

Configuring and managing environments for developers can be time-consuming and complicated as it implicates both national regions and multiple accounts.

Finite Lambda Resource

The resources granted to each execution context of an AWS Lambda function are finite. The memory to execution time, networking bandwidth, and storage varies from function to function and can end up hitting limitations if not carefully managed. 

AWS Lambda Pricing

AWS Lambda provides an indefinite free tier consisting of 1 million requests and 400,000 GB-seconds of compute time per month. If you surpass these limits, you will have to pay for any excess computing time and requests.

With the Lambda Free Tier, you get 400,000 GB seconds of compute time and 1 million free requests per month. That works out to 110 hours of compute time with 1 GB of function memory and 11 hours with 10 GB.

Once you have used up your free resources, you will need to pay for any further requests.

AWS Lambda charges users based on the number of requests for their functions and the duration required for it to execute.

A request is counted when code begins to run in response to an event. The total number of requests for all functions is charged.

Duration is calculated as the time elapsed between when the code begins executing and when it returns or is terminated, rounded up to the nearest millisecond.

The pricing for AWS Lambda depends on the memory allocated to the function.

For example

If you have allocated 512 MB of memory to your function, which runs for 200 milliseconds to serve one request, then you’re charged for 200 ms of execution time.

This applies regardless of whether the function used all the allocated memory.

That means if your Lambda function only used 200 MB, you’re still charged the rate of 512 MB.

Harnessing the Power and Potential of AWS Lambda

No matter how complex your use case is, AWS Lambda is an efficient, practical, and cost-effective choice. Strengthen its potential by integrating it with cutting-edge security solutions like Perimeter81 to provide an extra layer of security. Step into the future with Lambda’s simplicity, flexibility, and power – learn more here!


What is the difference between Lambda and EC2?
While EC2 runs a full copy of a virtual operating system, Lambda automatically runs your code without requiring you to provision or manage servers.
What are the 3 components of AWS Lambda?
Function, configuration, and event source are the three main components of AWS Lambda. The event source triggers a Lambda function and passes in event data. Configuration consists of settings like function name, description, entry point, and resource requirements.
What are the advantages of Lambda over EC2?
Lambda doesn’t call for security patching, OS maintenance, or capacity provisioning like EC2. It offers auto-scaling, no requisition of additional capacity, and you only pay for the computation time you consume.
Why use S3 with Lambda?
S3 with Lambda ensures that you don’t need to alter the application code. Like clockwork, each time you add, update, or delete an S3 object, S3 triggers a Lambda function. This makes asynchronous processing possible whenever specific file changes are made.
What is an example of AWS Lambda?
One practical AWS Lambda example is a file processing system where Lambda is used to resize, watermark, and process image files stored in an Amazon S3 bucket whenever a new image is uploaded.
What is AWS Lambda also known as?
AWS Lambda is also known as a serverless compute service because it offers event-triggered computing which automates the running of your applications without requiring server management.