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What is Amazon Lambda

What is AWS Lambda? Definition, Features, How It Works, Use Cases, Pricing, and Benefits

AWS Lambda is an event-driven serverless compute service that runs on the AWS cloud. It is a fully managed service that enables developers to focus on writing code for their applications without worrying about server management.

Kevin KiruriAleksander HougenSimona Ivanovski

Written by Kevin Kiruri (Writer)

Reviewed by Aleksander Hougen (Co-Chief Editor)

Facts checked by Simona Ivanovski (Fact-Checker)

Last Updated: 2024-11-19T20:10:58+00:00

All our content is written fully by humans; we do not publish AI writing. Learn more here.

Key Takeaways: What Is Amazon Lambda?
  • Amazon Lambda is a serverless compute service offered by Amazon Web Services. It enables developers to focus on writing code without having to worry about the underlying infrastructure.
  • Lambda is an event-based serverless service that specializes in running functions based on set triggers. It’s perfect for event-based, short-lived applications such as data processing that require high availability and scalability on AWS.
  • The service is fully managed by AWS and utilizes a broad range of programming environments and programming languages. The functions created are independent of each other.
  • The Lambda service is great for saving costs, as users are only billed for the duration the functions run. Running a function on an EC2 would result in charges for as long as the EC2 is running regardless of whether the function is executing.

Facts & Expert Analysis: AWS Lambda Functions

  • Serverless computing: AWS introduced serverless computing in 2014 through Lambda. Since then, cloud companies have continued to develop more serverless services to support the growing adoption of serverless computing.
  • Growing adoption: Serverless adoption for organizations using cloud providers has continued to grow over the years. A research study by Datadog showed that more than 70% of AWS customers use at least one serverless solution, with AWS Lambda being the most popular.1
  • Languages: Node.js and Python are the leading languages used to write Lambda functions. According to the same Datadog study, they contribute to more than 50% of all Lambda invocations. The languages have a large community of developers and robust integration with Lambda, which make them a top pick for new developers.1

Amazon Web Services is a huge ecosystem of hundreds of different services that serve different use cases. AWS Lambda is Amazon’s solution to serverless computing. Developers can use it to deploy code without worrying about infrastructure.

AWS Lambda automatically responds to predefined events by executing a set of code. This makes Lambda well suited to running the backend of web and IoT applications, as well as processing API requests and managing files and streaming data. All the developer has to do is provide the code and define the parameters.

Like all AWS services, Lambda is priced on a pay-as-you-go model. Billing is based on the number of Lambda function invocations, the length of time the Lambda function executes, the memory allocated to the function, the amount of data transferred and the provisioned concurrency. These factors have a price-per-unit allocation to calculate the total cost.

  • 11/19/2024

    Updated article with important key takeaways.

What Is AWS Lambda?

AWS Lambda is a serverless computing service that allows its users to develop code without provisioning or managing servers. In simple terms, AWS Lambda works as a function as a service (FaaS), allowing developers to focus on writing the code they need to run. AWS provisions all the underlying infrastructure required to run the code.

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AWS Lambda Architecture

AWS Lambda implements serverless computing, so developers can run code without provisioning infrastructure. Lambda architecture comprises functions, event sources, execution environments, environment runtimes, scaling, pricing, integration, deployment and more. These aspects work together to create a highly available and efficient serverless environment.

What Are the 3 Components of AWS Lambda?

The three components of AWS Lambda are function code, event sources and execution context.

  1. Function code: This is the custom logic code you write as the Lambda handler function. The code implements the logic and processing you intend to execute in layers. Each layer comprises a .zip file containing all dependencies. You can write Lambda functions using various languages, such as Python, Java, Go, C#, Node.js and more.
  2. Event sources: These are the triggers that invoke your Lambda functions. Event sources can be a variety of actions from other AWS resources, such as Amazon S3, DynamoDB or Amazon API Gateway. The events cause the Lambda function to execute, thus implementing your desired logic and processing.
  3. Execution environment: This is the runtime container where the Lambda function runs. A Lambda function runs in an isolated environment that manages all the resources required for execution, such as memory, compute and runtime. Lambda automatically scales the execution contexts depending on the workload demand to enhance efficiency.

What Are the Features of AWS Lambda?

Amazon Lambda features include fault tolerance, bring your own code, serverless applications, automatic scaling and pay-per-use pricing, among others. AWS Lambda runs code in response to triggered events and automatically manages the underlying infrastructure required to run the code.

Fault Tolerance

AWS Lambda manages compute resources deployed in multiple data centers across multiple availability zones in each AWS region. This protects the code against failure caused by individual machine failure. AWS Lambda provides high availability for both the service and running functions, with zero maintenance windows or scheduled downtime.

Bring Your Own Code

You do not need to learn any new languages to work with AWS Lambda. AWS Lambda supports multiple languages, such as Python, Node.js, Java and Go, among others. It provides the runtime API required to run and execute the code based on the language you choose. You can have multiple functions written in different languages within a serverless web application.

Serverless Applications

Lambda applications are composed of functions and event triggers to run the functions. They create event-driven environments. A function can also operate on its own — you can trigger it manually or leverage other AWS resources such as Amazon S3 or DynamoDB to act as the trigger.

Automatic Scaling

AWS Lambda runs in fully managed infrastructure and scales your application by running multiple instances of the same function in parallel depending on the application. You don’t have to worry about infrastructure provisioning or whether your application has the right resources. Lambda also deploys resources across different availability zones to ensure high availability.

Pay-Per-Use Pricing

With AWS Lambda, you only pay for the resources you use when running your code and the number of requests your functions receive. You do not incur any charges when the functions are idle and not running any code. This helps you save money since the functions are not deployed on servers that run 24/7.

How Does AWS Lambda Work?

AWS Lambda uses function code, event-driven execution, support for multiple languages, pay-per-use pricing and integration with other AWS services to deliver serverless computing.

AWS Lambda lets developers run code without the complexity of provisioning Amazon web servers. This enables developers to focus more on writing and improving their code when building their web applications. 

What Is Serverless Computing?

Serverless computing is a cloud computing concept that entails the cloud provider provisioning and managing the underlying infrastructure. The developer is only responsible for developing the logic and processes that run on the infrastructure.

What Is an AWS Lambda Execution Role?

An AWS Lambda execution role is an AWS IAM (identity and access management) security role that determines a Lambda function’s permissions to interact with other AWS resources. You create an AWS Lambda execution role when creating a function. The function assumes the role each time it runs. 

The best practice is to use the principle of least privilege when assigning policies to the execution role. This means that the Lambda function will only have access to the intended resources and only perform the required actions.

What Triggers an AWS Lambda Function?

Triggers are composed of various events or services that cause Lambda functions to execute. Services such as Amazon S3, Amazon DynamoDB, Amazon Kinesis, Amazon API Gateway and Amazon SQS can trigger AWS Lambda functions. 

The services trigger the Lambda functions through a change in state. You can also trigger a Lambda function using the AWS Lambda API, which can allow your application to trigger a Lambda function on demand. 

What Programming Languages Does AWS Lambda Support?

AWS Lambda supports various languages to write serverless functions and can run on any operating system. Lambda supports Java, Python, Node.js, .NET, Go and Ruby in its managed runtimes. 

In the event that you want to use a language that is not supported in the managed runtimes, such as Rust, Perl or PHP, Lambda allows you to create custom runtimes through the Lambda Runtime API. The custom runtimes will then allow you to develop functions in your preferred language.

What Runtime Environments Does AWS Lambda Support?

AWS Lambda supports both managed and custom runtimes. AWS fully manages managed runtimes, while the user manages custom runtimes. Managed runtime environments include Java, Python, Node.js, .NET, Go and Ruby. 

If a user wants to use a language that is not among the managed runtimes, they can install custom runtimes such as PHP or Perl to run their functions.

How Much Memory Does AWS Lambda Have?

You can configure your Lambda function memory allocation, ranging from 128 MB to 10,240 MB (10 GB), in increments of 1 MB. The amount of allocated memory determines the amount of CPU power, which also impacts the overall cost of running the function.

What Is the Timeout Limit for AWS Lambda?

A function’s timeout is the amount of time the runtime environment is active for the function to execute. When the timeout limit is reached, the function execution aborts. The default runtime limit for AWS Lambda is three seconds. The timeout limit can be configured in increments of one second up to a maximum of 15 minutes (900 seconds).

What Is the Payload Limit for AWS Lambda?

The payload limit for AWS Lambda is the maximum amount of data that you can pass to a Lambda function. Synchronous invocations have a 6 MB payload limit, while asynchronous invocations have a 256 KB payload limit.

How to Create an AWS Lambda Function

You can create a Lambda function using the AWS Lambda console, AWS CLI or AWS SDK. We will go through the step-by-step process of creating Lambda functions on the AWS Lambda console below.

  1. Create a Function in the AWS Lambda Console

    From the AWS Lambda console that appears, click on “create function.”

    create function
  2. Create Function Page

    On the “create function” page, select “author from scratch,” give the function a name and select your preferred runtime from the dropdown menu. In the screenshot below, the Lambda function is named “myLambdafunction” and we selected the “Python 3.12” runtime. Once configured, click on “create function” at the bottom.

    func config
  3. Write the Code

    On the Lambda function page, write the Lambda code in the code space provided in the “code” tab.

    Lambda function

How to Deploy an AWS Lambda Function

Once you have written your function code, deploy your Lambda function by clicking on “deploy.”This will save the changes and deploy your function. 

deploy
When the function is deployed, the “deploy” button becomes inactive.

In other cases, a Lambda function can be deployed using a Docker container image using the “container image support for AWS Lambda” feature.

How to Invoke an AWS Lambda Function

You can invoke a Lambda function using the AWS CLI, the AWS SDK or the AWS console. An invocation can be synchronous or asynchronous. In synchronous invocation, you directly invoke the Lambda function to execute it. In asynchronous invocation, you set up event triggers to trigger Lambda functions.

  1. Open the Test Panel

    Go to your Lambda function page and click on “test.”

    invoke
  2. Configure the Test Event

    Enter a name for your test event; then, click on “save” at the bottom of the page to save the configuration.

    test config
  3. Invoke the Lambda Function

    Click on “test” to invoke the Lambda function. The function invokes using your configured test event.

    test invoke
  4. Read the Execution Results

    On invocation, the “execution results” page opens and you can view the function’s logs and output. You can then deduce whether the function executed as expected based on the results. Any errors will be displayed, and you can then modify your Lambda function to correct them.

    execution results

How to Delete an AWS Lambda Function

You delete a Lambda function when you no longer intend to use it in your AWS environment. AWS provides a quick and easy way to delete a function through the AWS console.

  1. View the Functions in Your Account

    Click on “functions” on the left-hand side of the AWS Lambda console.

    view functions
  2. Select the Function to Delete

    Select the function by clicking on its checkbox. Under “actions,” select “delete.”

    delete function
  3. Confirm the Deletion

    In the pop-up window that appears, confirm the deletion by typing “delete”; then, click on “delete.” This action deletes the Lambda function from your account.

    confirm delete

How to Use AWS Documentation for Lambda

AWS provides Lambda documentation, which explains what Lambda is, when and how to use it, and its key features. The left-hand panel links to different guides on how to use AWS Lambda. 

The documentation is well organized. It offers an explanation of implementation and provides example code to enhance your understanding. However, the official documentation does not include videos. Additionally, the examples tend to be basic and do not cover complex Lambda function implementations.

What Are the Main Use Cases of AWS Lambda?

Lambda is mainly used for compute applications that need to rapidly scale when in use and drop demand down to zero when not in use. The main use cases of Amazon Lambda include stream processing, IoT and mobile backends, file processing and web applications.

  • Stream processing: Amazon Lambda is used for processing real-time streaming data from Amazon Kinesis or log filtering. Amazon Lambda can process the incoming data and help make decisions or feed processed data to an application for execution.
  • IoT backends: You can use Amazon Lambda to handle mobile, IoT, web and third-party API requests. Users benefit from having a highly scalable and available backend for their serverless solutions.
  • File processing: AWS customers can upload files to Amazon S3, use AWS Lambda to process data in real time and access stateful data after every upload.
  • Mobile backends: Developers can use AWS Lambda as a backend for mobile applications to handle user authentication with the authorizer, synchronize data, process API requests and carry out other backend tasks.
  • Web applications: AWS Lambda can serve as a backend for web applications, handling HTTP requests from other services such as API Gateway. Developers can combine Lambda with other AWS services to ensure they build applications that automatically scale and are highly available.

AWS Lambda Pricing Plans

AWS Lambda pricing is based on the number of executions, length of time the function runs, memory allocation, data transfer and provisioned concurrency.

  • Number of executions: AWS Lambda bills for each request made to the Lambda function. However, the first million requests per month are free. After one million requests, you are charged a per-request rate for additional requests.
  • Duration: Lambda charges based on the length of time your function runs, rounded up to the nearest 100ms depending on the amount of memory allocated to the function. The duration is the time from which the function starts running to the time the code terminates or returns.
  • Memory allocation: Lambda allows you to allocate 128 MB to 10 GB of memory to your function. The allocated memory determines the CPU and network performance available for your function as it executes. The larger the memory allocation, the higher the cost, calculated as price per GB-second.
  • Data transfer: Data transfer from Lambda out of one AWS region to another AWS region or to the public internet is charged per GB at the Amazon EC2 data transfer rate. Data transfer within the same AWS region or to other AWS services is free.
  • Provisioned concurrency: You can provision multiple instances of your function to run concurrently to handle a spike in traffic. Higher concurrency levels may increase the number of resources your function consumes, leading to additional costs.

You can find detailed and up-to-date information on the AWS Lambda pricing page.

How to Calculate AWS Lambda Costs

The main cost drivers for AWS Lambda include the number of executions, duration and memory allocation.

Example: Suppose you have a Lambda function for a machine learning application triggered by an HTTP request with the following parameters:

  • Invocation count per month: 1,500,000
  • Average execution duration per month: 2s
  • Memory allocation: 256 MB
  • Pricing: $0.0000166667 for every GB-second, $0.20 per 1 million requests

Solution:

  • Invocation cost = 500,000 invocations * $0.2 per invocation = $100.00 (first million invocations are free)
  • Execution duration = average duration * invocations = 2s * 1,500,000 = 3,000,000s
  • Charged GB-s = 0.256 GB * 3,000,000 – 400,000 = 368,000GB-s ( first 400,000GB-s are free)
  • Execution cost = 368,000GB-s * 0.0000166667 per GB-s = $6.13
  • Total cost = invocation cost + execution cost = $100 + $6.13 = $106.13
How to Monitor AWS Lambda Costs

You can monitor AWS Lambda costs using AWS CloudWatch or AWS Cost Explorer. AWS CloudWatch provides detailed monitoring and logging for your Lambda functions, with metrics like invocation count, duration and error count. You can set up CloudWatch alarms to alert you when the costs go above a certain threshold.

AWS Cost Explorer gives you an overview of the costs the resources in your account incur. You can also use Cost Explorer to view the costs that your specific Lambda functions incur over a period of time. You can use a combination of Cost Explorer and CloudWatch to identify and monitor your Lambda costs. 

AWS Lambda Provisioned Concurrency Pricing

Provisioned concurrency is a Lambda feature that enables you to pre-initialize a specified number of Lambda execution environments. When you enable provisioned concurrency, your function is actively ready to respond to incoming requests at any given time, reducing the latency associated with cold starts.

Provisioned concurrency is charged based on the provisioned concurrency units regardless of whether the function is invoked or the region where your function is deployed, in addition to invocation and duration costs. Provisioned concurrency is mostly applicable in latency-sensitive web and mobile applications.

AWS Lambda Free Tier

The free tier of AWS Lambda is available to all AWS customers. The free tier provides one million free requests to a Lambda function per month and up to 400,000 GB-seconds of compute time per month. Using AWS Lambda within these limits will incur zero charges.

What Is the AWS Lambda Limit Per Account?

AWS has default limits that apply to each AWS account to ensure fair usage for all customers. The table below shows the most common AWS Lambda limits per account.

Resource:Default Quota
Storage for upload functions*75 GB
Concurrent executions1,000
Elastic network interfaces per virtual private cloud (VPC)250
Function memory allocation128 MB – 10240 MB in 1 MB increments
Function timeout900 seconds (15 minutes)
Function layers5 layers
GetFunction API requests100 requests per second
GetPolicy API requests15 requests per second
Invocation requests per function version or alias10X the allocated provisioned concurrency
*.zip file archives and layers

For more information on Lambda limits, including an updated list of all Lambda limits, check out the AWS Lambda quotas page.

What Are the Benefits of AWS Lambda?

Some of the key benefits of AWS Lambda include the pay-per-use pricing, automatic scaling, serverless computing and reduced operational overhead.

  • Pay-per-use pricing: You only pay for the compute time and resources consumed when your code is running.
  • Automatic scaling: Lambda has the ability to automatically scale your application in response to the number of events or requests invoking the code at a particular time.
  • Serverless computing: Lambda allows you to run code without worrying about provisioning or managing the infrastructure it runs on. You can completely focus on developing the strategies you use to write code for maximum efficiency and performance.
  • Reduced operational overhead: Lambda reduces operational overhead, as there is no need to provision, scale and maintain servers.

What Are the Disadvantages of AWS Lambda?

The key disadvantages of using Lambda include vendor lock-in, limits and quotas, the complexity of distributed systems and cold starts.

  • Vendor lock-in: Using AWS Lambda makes you dependent on the AWS ecosystem, and you may find it difficult to migrate to other cloud infrastructure providers in the future.
  • Limits and quotas: AWS Lambda has limits and quotas, such as maximum function duration and function size, which can constrain the workloads you can run or lead to throttling.
  • Complexity of distributed systems: Building complex, distributed applications with multiple Lambda functions can make the overall system difficult to manage and debug.
  • Cold starts: Lambda functions can experience cold starts, especially when they have been idle for a while, which can lead to increased latency.

What Are the Differences Between AWS Lambda and EC2?

AWS Lambda and Amazon EC2 are both efficient compute services for your workloads. The choice of which type of service you use depends on the intended use case.

AWS Lambda uses a serverless compute model, so you can write code specifying the triggers and destination without worrying about the base infrastructure. Amazon EC2 uses a traditional virtual machine service requiring you to provision the infrastructure. Lambda automatically scales up and down. EC2 uses manual scaling, though it can be automated with autoscaling.

AWS is best for event-driven, short-lived workloads that need to automatically scale; when you want to focus on writing code; and for workloads with unpredictable and highly variable usage patterns. Amazon EC2 is best for long-running, stateful applications where you need to have more control over the underlying resources and in workloads with predictable usage patterns.

Is Amazon EC2 Serverless?

Amazon EC2 is not serverless. It uses a traditional virtual machine model, and you can fully provision and manage the underlying infrastructure.

Other AWS Lambda Alternatives

Alternatives to Amazon Lambda include Google Cloud Functions and Microsoft Azure Functions.

Google Cloud Functions

Google Cloud Functions offers serverless, event-driven compute services on the Google Cloud Platform. The similarities between Google Cloud Functions and AWS Lambda include making use of a serverless computing model, event-driven execution, autoscaling and pay-per-use pricing.

The differences between Google Cloud Functions and AWS Lambda include different vendors and cloud platforms, varying execution environments and different integration ecosystems. Each service integrates with its respective cloud provider, thus providing different capabilities.

Microsoft Azure Functions

Microsoft Azure Functions offers serverless, event-driven compute services on the Microsoft Azure platform. The similarities between Microsoft Azure Functions and AWS Lambda include event-driven execution, the serverless computing model, pay-per-use pricing and autoscaling.

The differences between Microsoft Azure Functions and AWS Lambda include the vendors, cloud platforms and execution environments. Azure Functions uses triggers and bindings to interact with external services, while Lambda uses SDKs and HTTP endpoints. Azure Functions runs on the Azure App Service platform, while Lambda runs on AWS architecture.

Final Thoughts

AWS Lambda is a key service for implementing serverless computing. The AWS Lambda resource model provides an environment to easily interact with different AWS services to enhance your serverless applications. Its event-based nature ensures that it runs at the exact time you need, which helps you save on compute service costs.

Would you use AWS Lambda? How would Lambda help you develop your solutions? What else would you like to learn about Lambda? Let us know in the comment section below. Thank you for reading and engaging with our content.

FAQ: AWS Lambda

  • Amazon Lambda is used to run serverless applications so you can focus on writing code and let AWS manage the underlying infrastructure.

  • Lambda provides a serverless, event-driven compute service on AWS.

  • Both AWS Lambda and EC2 are good AWS services. The choice between the two depends on the specific requirements and characteristics of your workload.

  • AWS Lambda is best for event-driven, serverless applications where you want to focus on writing code and have AWS manage your underlying infrastructure.

Sources:

  1. State of Serverless — Data Dog
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