AWS Fargate vs. Lambda

1. AWS Fargate vs. Lambda

2. AWS Fargate: An overview AWS Lambda: An overview Fargate vs. Lambda: Development Fargate vs. Lambda: Performance Fargate vs. Lambda: Pricing Fargate vs. Lambda: Operational compatibility Fargate vs. Lambda: Scalability and maintenance Fargate vs. Lambda: Security Key characteristics and use cases of AWS Fargate Key characteristics and use cases of AWS Lambda Agenda

3. AWS Fargate: An overview Fargate is a serverless compute engine that allows you to run containers without managing the infrastructure. It complements ECS/EKS and makes launching container-based applications much more effortless. Therefore, you can focus on building your applications while AWS does the heavy lifting of provisioning, configuring, and scaling servers or clusters. All you need to do is define the infrastructure parameters, and Fargate will launch the containers for you.

4. AWS Lambda: An overview Lambda, an event-driven compute engine, is another serverless technology from AWS. It reduces the operational overhead of managing the infrastructure with a pay-per-use model and no upfront cost. In addition, it enables you to run code in response to events and automatically provisions for and manages the compute resources required.

5. AWS Fargate vs. Lambda: Development Fargate: Lambda: Developers can build locally using Docker and run the container images directly in the cloud with Fargate. Recently, AWS and its community have developed numerous tools to develop, create, and manage Lambda functions seamlessly. It takes care of everything in-between. Earlier, developing with Lambda was a complex process as it lacked a straightforward procedure to run Lambda functions in the cloud, along with runtime restrictions. Developing with Docker is quite popular as its features can be used for easily managing multiple containers, network configurations, resource requirements, etc., and it has larger community support.

6. AWS Fargate vs. Lambda: Performance Fargate: Lambda: Typically, the startup time for Fargate containers is 60-90 seconds which is more than Lambda. The initial Lambda startup takes 5 seconds, following which the same functions have a negligible startup time. However, Fargate has dedicated resources and no runtime limitations, so the environment remains in a warm state. But a Lambda function stops running after 15 minutes until triggered by an event, causing cold starts. However, there are multiple ways and techniques to reduce and prevent Lambda cold starts that you can read in our blog post on how to avoid Lambda cold starts. But it may take more startup time if there is a sudden spike of requests, and it takes longer to scale up.

7. AWS Fargate vs. Lambda: Pricing Fargate: Lambda: AWS Fargate pricing is calculated per second based on the allocated memory, vCPUs, OSs, CPU infrastructure, and storage resources used. AWS Lambda pricing is calculated per millisecond based on the number of requests, the memory and runtime used by functions, and the network traffic.

8. AWS Fargate vs. Lambda: Operational compatibility Fargate: Lambda: It comes with additional complexity as you still have to register your containers with ECS. It entirely abstracts the whole infrastructure layer, allowing you to focus on your critical application functionalities. But it offers significantly more flexibility in that you have full access to the configuration of each container. But its operational flexibility is restricted as it limits temporary disk space to 512MB and includes a 50MB deployment package.

9. AWS Fargate vs. Lambda: Scalability and maintenance Fargate: Lambda: In the case of Fargate, you need to set up autoscaling. Lambda functions are scalable by design, so they automatically launch instances to meet the increases in demand. Also, you need to shut down Fargate tasks on a manual or scheduled basis as it cannot scale to 0, adding to maintenance. Apart from rapid auto-scaling, it can also scale to 0. So, you don’t have to pay for idle applications, which is useful for low traffic workloads. Moreover, in Fargate, the developers must keep the base container images updated. This ability of Lambda to scale from 0-1000 rapidly is essential for spiky and unpredictable traffic. So it again adds to the maintenance but allows for more control.

10. AWS Fargate vs. Lambda: Security Fargate: Lambda: With Fargate, permissions are associated with containers or pods the tasks are run in. With Lambda, you can set up IAM roles that each function or service will assume. All Fargate tasks run in their individual, isolated compute environments without sharing any underlying resources (kernel, CPU/memory, Elastic Network Interface) with other tasks. AWS Identity and Access Management (IAM) modules ensure that only the right users can access the function or the application. Moreover, functions have dedicated execution environments that are never shared with other functions.

11. Deploys and scales applications easily, from single-use utility applications to entirely containerized microservices architectures. Eliminates the operational overhead of choosing server types, patching, sizing, cluster scheduling, optimizing cluster packing, and more. Allows you to pay only for what you use as Fargate’s fully managed container environment automatically allocates the required compute power-on-demand. Integrates with a range of sibling AWS services for networking, CI/CD, security, monitoring, etc. Allows developers to have workload isolation. Improves security with isolated compute environments. Key characteristics and use cases of AWS Fargate

12. Long-running compute jobs that are longer than 15 minutes Applications that require more compute than Lambda offers, i.e., more than 3GB of memory Applications with predictable scaling and where longer start times are acceptable Latency-sensitive and storage-intensive applications Applications that need inter-container communication Containerized machine learning applications When to use AWS Fargate

13. Reduces costs because you only pay for the resources you use. Scales automatically to handle a few requests per day or even thousands of requests per second. Reduces operational overhead such as administration, maintenance, security patching, resizing, and adding servers for any type of application or backend services. Allows developers to spend more time on innovation with quicker iterations. Supports multiple programming languages. Allows packaging and deploying of functions as container images, expanding its use cases. Easily integrates with other innovative AWS services. Key characteristics and use cases of AWS Lambda

14. Web applications and websites that require dynamic scaling to handle excessive traffic loads at peak hours and save money when there is no traffic. For applications that can be easily expressed as single functions with predictable usage of resources on each invocation. Event-driven workloads and apps. Custom mobile and IoT backends. Asynchronous, small jobs to be managed in tandem. File processing and automated file synchronization. Real-time log analysis and data processing. IT automation. When to use AWS Lambda

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