AWS Fargate is a serverless compute engine for containers. It works with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). It lets you run containerized applications without managing the underlying infrastructure.
Fargate eliminates the need to choose server types, decide when to scale them, or optimize cluster packing. With Fargate, there is no need to deal with provisioning, configuration, or scaling of virtual machines to run containers. You simply specify the CPU and memory requirements for your application, define networking and IAM policies, and launch the application.
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AWS Lambda is another serverless compute service that lets you run code without provisioning or managing servers. However, unlike AWS Fargate, Lambda automatically scales your application by running specific code, known as a serverless function, in response to a trigger or event. Your code runs in parallel and processes each event individually, scaling precisely with the size of the workload.
In Amazon Lambda, deploying an application is as simple as writing a code function and uploading it to Lambda. What’s more, you only pay for the compute time you consume. There’s no charge when code isn’t running. You can set up your code to automatically trigger from events created by other AWS services, or call it directly from any web or mobile app.
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In this article
AWS Fargate operates by providing a platform to run containers. You start by defining your application in containers using task definitions in Amazon ECS, or pods in Amazon EKS. This includes the container image to use, the required CPU and memory, the network and data volumes, and the task or service role.
Once your task definition is set up, you can start running tasks, either manually or automatically. Each task or pod runs in its own isolated environment, without sharing resources with other tasks and services.
Fargate automatically allocates and manages all the required compute resources. It also handles the scaling of tasks. When more resources are needed, Fargate can automatically increase the number of tasks running your application. Similarly, when resources are not needed, it can automatically reduce the number of tasks.
Learn more in our detailed guide to Fargate architecture (coming soon)
AWS Lambda operates by running your code in response to specific events or triggers. You start by writing your code, packaging it into a ZIP file, and then uploading it to Lambda. Your code, which is called a Lambda function, can be written in any language that is supported by AWS Lambda.
You then set up your function to be triggered by other AWS services, or by HTTP requests via Amazon API Gateway. For example, you could set up a function to be triggered whenever a new file is uploaded to Amazon S3, or a new record is added to an Amazon DynamoDB table.
Once triggered, Lambda executes your function on the appropriately sized infrastructure. It manages all the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring, and logging.
When an event triggers your function, Lambda launches an instance of the function to process the event. If the function is already running when another event happens, Lambda simply starts a new instance of your function. This allows Lambda to scale automatically with the size of your workload.
In terms of scalability, both Fargate and Lambda offer great options. Fargate provides you with flexible scaling, allowing you to add or remove instances as your application demands change. You don’t need to worry about managing the infrastructure, it’s all taken care of by AWS.
On the other hand, Lambda is designed to scale automatically and quickly. It can swiftly adjust its capacity to maintain consistent performance at the lowest possible cost. With Lambda, you no longer need to worry about capacity planning, ensuring you can focus more on your code and less on the infrastructure.
Performance is another key area where Fargate and Lambda differ. Fargate provides consistent performance, as resources are isolated at a task level. This means that you don’t share CPU, memory, or storage with other tasks, ensuring consistent performance.
Lambda’s performance, on the other hand, is directly tied to the memory size you allocate to your functions. The more memory you allocate to a function, the more CPU power, network bandwidth, and disk I/O it receives. This makes it easy to optimize your Lambda functions for both cost and performance.
In terms of cost, Fargate and Lambda have different pricing models. Fargate charges you for the amount of vCPU and memory resources that your containerized application requests. It’s a simple pay-as-you-go model.
Lambda, on the other hand, charges you for the total number of requests for your functions and the time your code executes, rounded to the nearest millisecond. This means you never pay for idle time and only for the actual compute time your functions consume.
Learn more in our detailed guide to Fargate pricing (coming soon)
When it comes to maintenance and management, Fargate removes the need to interact with the underlying EC2 instances. You don’t need to worry about patching, updating, or even managing the servers. However, Fargate still requires you to package applications as containers.
In Lambda, like Fargate, there are no servers to manage, no runtimes to install, and no ongoing maintenance. Lambda does not use containers, so there is no need to create and maintain container images. You can simply write a code snippet in a language supported by Lambda and deploy it as a serverless function.
Fargate is great for long-running applications, microservices, batch processing, machine learning, and high-performance computing workloads. It works particularly well when you need to quickly scale out applications to meet demand.
Lambda, on the other hand, does not support long-running application components. Lambda functions can run for up to 15 minutes. Therefore, it’s ideal for real-time file processing, data transformation, IoT device data telemetry, and mobile and web backends. It’s best suited to workloads that respond to HTTP requests, process events from AWS services, and work with AWS Step Functions for complex orchestration.
Here are four things that can help you choose which serverless platform is best for your projects:
Fargate and Lambda are suited to different types of applications and environments. Fargate is ideal for applications that require consistent performance and have complex computing needs. It provides all the benefits of containers, including portability, scalability, and isolation, without the need to manage the underlying infrastructure. This makes it perfect for running microservices, batch processing jobs, and machine learning applications.
On the other hand, Lambda excels in running stateless, event-driven applications. It automatically scales your applications in response to the incoming event rate, making it ideal for real-time file processing, data transformation jobs, and backend APIs for web and mobile applications.
Moreover, Fargate and Lambda can be used in different environments. If you are operating in a hybrid cloud or multi-cloud environment, Fargate might be a better choice as it supports both AWS and ECS, making it easier to move your applications between different environments. Conversely, if you are operating solely in the AWS environment, Lambda could be a more suitable choice due to its deep integration with other AWS services.
While serverless technologies like AWS Fargate and AWS Lambda, make it easier to build and scale applications, it’s not a free lunch. Observability challenges await developers of serverless applications, regardless of the compute service you choose.
However, monitoring and troubleshooting AWS Fargate and AWS Lambda deployments can be efficiently accomplished using Lumigo. Lumigo provides a comprehensive observability solution that enables developers to gain deep insights into the performance and behavior of their serverless applications.
With Lumigo’s powerful distributed tracing capabilities, users can track issues across services in serverless environments, proactively identify and resolve issues in real-time, and ensure optimal application performance and reliability. The platform offers detailed metrics, logs, and traces, allowing developers to quickly pinpoint bottlenecks, optimize resource allocation, and enhance overall system efficiency. Lumigo’s intuitive interface and alerting system enable seamless troubleshooting, providing valuable context and facilitating swift root cause analysis.
Whether it’s monitoring function invocations, tracking request flows, or monitoring resource utilization, Lumigo equips users with the necessary tools to effectively monitor and troubleshoot their AWS Fargate and AWS Lambda environments.