I want to provide you with a comprehensive guide to AWS Fargate. By the end, you‘ll understand the key benefits it provides, how it differs from self-managed containers, available components, and recommendations on when to leverage this technology. Sound good? Let‘s get started!
What is AWS Fargate Exactly?
AWS Fargate is a serverless compute engine that allows you to focus on building containerized applications without managing any underlying servers.
With Fargate, you package your app into containers, specify CPU, memory, network configurations and simply launch it on the AWS cloud. Fargate automatically handles all infrastructure provisioning, scaling, security, and availability behind the scenes.
Some key advantages you gain by using Fargate:
- No server management – AWS handles all infrastructure to run containers
- Auto-scaling – Tasks dynamically scale up and down
- Consistent performance – Full allocated CPU/RAM per workload
- Enhanced security – Isolated kernel for each workload
- Flexible pricing – Only pay for resources used by each container
By leveraging Fargate, you are employing a "serverless" architecture. This doesn‘t mean there are no servers present. Rather AWS handles the entire lifecycle of servers for you so you can focus strictly on application code vs infrastructure operations.
How Does AWS Fargate Actually Work?
Behind the scenes, Fargate relies on both Amazon EC2 virtual machine instances as well as advanced AWS Graviton hardware to execute your containers.
This allows your applications to leverage the immense scale, availability, and security of the AWS global infrastructure while only paying for the compute resources consumed.
Fargate integrates natively with Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS) which are popular container orchestration platforms.
Rather than configuring resource intensive cluster management yourself, Fargate enables these orchestrators to automate deployment, scaling, networking, load balancing and health monitoring of your container workloads.
When using Fargate, each container workload instance runs within its own isolated kernel providing efficient compute isolation per task. This enables precise scaling where each workload can scale up and down independently.
In addition, Fargate instance configurations are based on vCPU and memory allocations. This allows right-sizing containers without worrying about complicated instance family types to pick from.
Finally, the beauty of Fargate shows in the pay-as-you-go pricing model. You only pay for the exact vCPU and memory resources consumed by your containers down to the second.
I‘ll expand more on the pricing comparison later on…
But first, when might Fargate be the right solution for your container workloads vs self-managed Kubernetes or ECS alternatives?
When Should You Leverage AWS Fargate?
Fargate shines best for containerized applications where you simply want to focus engineering efforts on application code rather than infrastructure operations.
Some examples of excellent use cases for Fargate:
- Microservices – Independently auto-scale each service
- Data & Batch Processing – Efficiently process huge volumes of data
- Web Applications – Deploy apps without capacity planning
- Core Business Systems – Alleviate Ops teams from infrastructure burden
Based on Fargate product reviews, users highlight simplicity, flexibility, scalability as major benefits over provisioning own infrastructure.
So when might you still want to manage your own Kubernetes or ECS clusters? Here are a few scenarios where self-managed resources may be preferable:
- Need for custom operating systems or kernels
- Specialized instance types required (eg GPUs)
- Using EC2 spot instances to reduce costs
- Advanced container networking capabilities
Managing your own infrastructure enables fuller customization capabilities but at the cost of additional DevOps overhead.
Let‘s explore the key components of Fargate next…
Fargate Building Blocks
When running containers on AWS Fargate, there are a few key components you should understand:
Clusters
A logical grouping of Fargate tasks and services within a particular AWS region. You can create multiple Fargate clusters to isolate development, test and production workloads for example.
Task Definitions
JSON file that describes one or more container images to deploy. Specifies parameters like CPU, memory allocations, ports, environment variables etc per container.
Tasks
An instantiation of a task definition running as a container on your Fargate cluster.
Services
Defines how many copies of a task should run and how they should be maintained. Handles scaling, fault tolerance and availability.
In addition to these application architecture components, you also choose appropriate vCPU and memory configurations for each Fargate task:
| vCPU options | 0.25, 0.5, 1, 2, 4 |
| Memory options | 0.5GB, 1GB, 2GB, 3GB, 4GB, 5GB, 6GB, 7GB, 8GB … all the way up to 30GB |
You can run both Linux and Windows based containers on Fargate across ON DEMAND as well as SPOT hardware. For Windows containers specifically, Fargate currently only supports the X86 processor architecture however.
Alright, with the basics covered, let‘s compare running containers directly on provisioned EC2 instances vs leveraging AWS Fargate…
Fargate vs Self-Managed EC2 for Containers
There are definite tradeoffs to consider when deciding between Fargate-managed serverless containers vs deploying docker directly on your own EC2 instances within an ECS cluster.
Fargate Pros
- No server configuration or cluster management required whatsoever
- Consistent vCPU & memory performance isolated per container
- Built-in auto-scaling, platform fault tolerance and availability
EC2 Pros
- Ability to customize OS, kernel, specialized instance types
- Leverage EC2 spot instances for cost savings
- More advanced networking and placement control
With Fargate, you don’t provision instances yourself nor handle cluster optimization and maintenance. This alleviates your admin burden but reduces infrastructure customizations.
You also give up some advanced networking configurations only accessible when running containers directly on EC2 instances.
To quantify the performance and cost differences, let’s analyze some real-world benchmarks…
Fargate vs Self-Managed ECS Performance
CloudSpectator conducted in-depth performance testing comparing 1 vCPU Fargate tasks vs manually configured c4.2xlarge EC2 instances under equivalent load.
The study revealed:
| Operation | Fargate vCPU | c4.2xlarge EC2 |
| Database Read Latency (ms) | 12.11 | 10.68 |
| Database Write Latency (ms) | 9.68 | 16.35 |
| Web Request Latency (ms) | 125 | 107 |
As shown above, the dedicated vCPU on Fargate performed comparably on critical performance metrics despite running on dramatically lower resources than the provisioned EC2 instance.
Now let‘s explore the cost advantages…
Fargate vs Self-Managed ECS Pricing
A detailed Fargate pricing model analysis revealed:
- Running a 256 MB / 0.25 vCPU task on Fargate is 37% cheaper than m5.large EC2 instance
- Bumps up to 47% savings for 1 GB / 0.5vCPU Fargate task vs m5.large
- And over 60% cheaper than EC2 when using Fargate Spot capacity
Clearly substantial cost reductions can be achieved due to the granular pay-per-second pricing model and eliminating unused EC2 capacity.
Now let’s explore a similar comparison of self-managed Kubernetes with Amazon EKS vs leveraging Fargate…
Fargate for EKS Use Cases
As with Amazon ECS clusters, leveraging Fargate for Kubernetes workloads can greatly alleviate cluster management vs self-standing up AWS EC2 nodes.
Some benefits specific to pairing Fargate with Amazon EKS include:
Fargate Pros
- No node provisioning, scaling or patching
- Pod isolation security via dedicated kernels
- Auto-healing and auto-placement
- Cost savings due to per-second billing
Self-Managed Node Pros
- Custom machine types or specialized hardware
- Advanced networking configurations
- Windows server node support
Do note that Fargate for EKS currently only supports the awsvpc network mode limiting options in that area. Also, privileged pods are not supported which blocks some workloads requiring Linux capabilities.
However, these limitations enable simplified management through auto-scaling, security controls, platform stability and availability assurances out of the box.
Alright my friend, we have covered quite a bit of ground! Let‘s wrap up with some closing thoughts…
Final Takeaways
I hope walking through all aspects of AWS Fargate from benefits, inner workings, building blocks, and comparisons has provided full transparency into this serverless container offering.
To summarize key takeaways:
- Fargate removes all infrastructure management allowing pure focus on application code
- Tradeoffs vs self-managed contain infrastructure are reduced control and customization vs simplicity
- Multiple studies have shown substantial cost savings in addition to agility gains
- For core production workloads, Fargate can alleviate burden without sacrificing performance
- Ensure to evaluate if advanced networking needs make self-managed solutions preferable
Ultimately AWS Fargate delivers immense value from productivity, operational and economic perspectives for organizations leveraging containers…with the right expectations set on customization needs.
Now you should feel equipped to determine if Fargate is a fit for your container workloads or not! Please reach out with any other questions.
