You’re assigned a task to scale an application effortlessly that runs without a hitch and never overloads. It sounded like a dream, but Amazon Elastic Container Service, or Amazon ECS, brings this to life.
Amazon ECS provides an environment for the seamless deployment, scaling, and management of containerized applications. Amazon ECS handles the heavy lifting of deploying and managing Docker containers, making it easier to keep applications as light, fast, and portable as we need them. Amazon ECS effectively removes the headaches often associated with container management, leaving us free to focus on building and innovating rather than wrestling with infrastructure.
In this blog, we’ll discuss Amazon ECS’s features and benefits. We’ll briefly introduce containers and explain how ECS is different from them. We’ll also discuss ECS’s launch types, use cases, and comparison with Amazon EKS.
Amazon ECS is, at its core, a simple, scalable, and reliable way to run containers without managing container orchestration infrastructure. If you’re running microservices architectures, batch jobs, or any containerized application, ECS handles the container deployment, networking, scaling, and health monitoring. ECS is a scalable environment through which you can run containers on a cluster of EC2 instances (learn how to set up Amazon EC2) or AWS Fargate, a serverless compute engine to run containers without worrying about the underlying infrastructure.
AWS provides Amazon Elastic Block Store (EBS), a service for storing persistent volumes of 1 GB or larger on Amazon Virtual Machine (EC2) instances and for creating persistent volumes on Amazon Elastic Container Service (ECS) clusters. With these integrations, ECS becomes a full stack solution rather than a standalone component, sitting at the core of your AWS cloud architecture.
ECS is preferred over container orchestration platforms like Kubernetes because it is easy to use and integrates with other AWS services.
Amazon ECS is a cost-effective, scalable, and flexible service that can be easily integrated and used with other AWS services. Let’s examine some points to help us understand why we should use a container service like Amazon ECS.
Amazon ECS is perfectly scalable and allows you to automatically start up, stop, and launch multiple containers across your clusters. The most interesting part for me with AWS Fargate is that you can scale even easier than with EC2, the scaling of resources that are managed according to the application’s needs. This gives you full control over the services and tasks to scale horizontally (adding more containers to meet increased demand), making the solution highly flexible for small or large-volume workloads.
One of the most significant benefits of using Amazon ECS is its native integration with various AWS services. For instance, ECS plays nicely with Amazon Elastic Load Balancer (ELB) for routing container traffic. It also supports AWS CloudWatch for real-time monitoring of your container workloads, IAM for secure role-based access control, and Amazon Route 53 for DNS management. These integrations make ECS a natural choice for those already using AWS services, ensuring smooth, efficient operations.
Amazon ECS is highly cost-effective because you only pay for the resources you use. In the case of AWS Fargate, you don’t have to manage the underlying EC2 instance servers, which reduces the server overhead, and you can scale resources dynamically based on the workload. It makes the ECS ideal for organizations seeking to optimize operational costs.
From a modern software development perspective, containers have been a game-changing technology that has completely transformed how applications are built, deployed, and maintained. Containers are lightweight, portable, and efficient ways to wrap software to ensure consistency across different environments.
Containers are a form of operating system virtualization that allows you to run applications and their dependencies in isolated environments. Unlike traditional virtualization, containers run a single OS independently across different containers. They are more lightweight, deploy quicker, and easier to manage.
But at its core, the container is simply a block of the application’s code, dependencies, libraries, and configurations. Once built, this block can be run on any system that supports containerization, such as a developer’s desktop, on-premise machines, or in the cloud. Docker, the most popular containerization platform, has been instrumental in popularizing containers by simplifying their creation and deployment.
Portability: Containers hold all dependencies so that an application runs uniformly across different environments, eliminating the common it works on my machine problem.
Isolation: With containers, different applications can run on the same host without conflicts.
Efficiency: Containers are lighter than VMs and use fewer system resources, so they start faster and more efficiently.
Consistency: It is very efficient because the runtime environment is bundled with the application. Thus, a single container can build, test, produce, and ensure a consistent software development life cycle.
For many years, virtual machines (VMs) have been the de facto answer for running multiple applications on one physical server, but containers have clear advantages over this.
Virtual machines: The problem with each VM is that it runs a full operating system and has a hypervisor, each adding significant overhead. In this case, VMs are heavy to use in terms of resources.
Containers: Containers share the host system’s kernel, which reduces their footprint. They require fewer resources (memory, CPU) because they don’t need a full OS per instance. This means you can run more containers than VMs on the same hardware.
Virtual machines: Spinning up a new VM takes several minutes because it has to boot up an entire OS.
Containers: Containers can be started in seconds as they only need to initialize the application layer, not a complete OS. Developers can deploy applications quickly and save time by rapid deployment.
Virtual machines: VMs achieve strong isolation in that each runs a separate OS environment, but this comes at the cost of higher overhead and less flexible usage.
Containers: Although containers don’t provide the same isolation level as VMs, the tradeoff between portability and isolation is good. A container can easily be moved from environment to environment (local development to cloud, from one cloud provider to another), which makes it perfect for microservices architectures and modern cloud-native applications.
In short, containers are a leaner, faster, and more flexible alternative to VMs, making them ideal for DevOps workflows and cloud environments. Because they provide portability, consistency, and scalability, they have become a key enabler of modern software development.
The illustration below shows the ECS cluster with its components.
Amazon Elastic Container Service (ECS) offers two primary launch types for running containers: Fargate and EC2. Both options provide different levels of control, flexibility, and cost-efficiency, making them suited for various workloads. Understanding their differences helps you choose the launch type for your containerized applications.
Fargate is AWS’s serverless compute engine for containers. It abstracts the underlying infrastructure, allowing developers to focus solely on building and deploying their applications without worrying about the EC2 instances, scaling, or management.
Serverless container management: With Fargate, you don’t need to provision, configure, or manage EC2 instances. AWS handles the underlying servers, patching, securing, and scaling them as required.
Auto-scaling: Fargate automatically scales your containers up or down based on the resource requirements of your application, ensuring that you’re only paying for what you use.
Simplified workflow: Developers can define the resources they need for each container, such as CPU, memory, and storage, while Fargate handles everything else.
No infrastructure management: Fargate’s serverless nature eliminates the need to manage infrastructure, making it perfect for teams that want simplicity.
Cost efficiency for small or bursty workloads: Fargate is cost-effective when dealing with unpredictable workloads where resources must scale dynamically.
Fast deployment: Fargate’s simplified approach reduces the time spent managing infrastructure, allowing for faster deployments and iterations.
Ideal for developers and teams who prefer not to manage infrastructure.
Best suited for applications with unpredictable or fluctuating workloads.
For projects where simplicity and speed of deployment are priorities over granular infrastructure control.
The EC2 launch type provides a more hands-on approach. You manage the EC2 instances on which your containers run. This offers greater control over the infrastructure, allowing you to customize your instance types, optimize performance, and manage scaling policies.
Full control over infrastructure: With EC2, you have complete control over the virtual machines running your containers. You can choose specific instance types, configure networking, and manage security groups.
Custom resource allocation: EC2 allows for precise CPU, memory, and storage configuration, making it ideal for workloads that need custom tuning.
Auto-scaling: You can define rules for EC2 instances to handle traffic spikes, giving you more control over the scaling process than Fargate.
Customization: EC2 allows you to tailor the infrastructure to your specific needs, fine-tuning and optimizing it.
Cost efficiency for large or steady workloads: EC2 is more cost-efficient than Fargate for large-scale, long-running applications where resource utilization is consistent and predictable.
Supports reserved and spot instances: With EC2, you can take advantage of reserved or spot instances, providing significant cost savings for predictable workloads or those tolerant of interruptions.
Ideal for teams that require fine-grained control over the underlying infrastructure.
It is best suited for large, steady workloads where long-term cost savings from reserved instances or spot instances can be realized.
Useful for applications that need to leverage custom configurations or optimizations.
Feature | Fargate | EC2 |
Infrastructure Management | Serverless, managed by AWS | Fully managed by the user |
Scaling | Auto-scales seamlessly | User-defined scaling policies |
Control | Less control over underlying instances | Full control over EC2 instances |
Cost Efficiency | Better for small, bursty workloads | Better for large, steady workloads |
Use Cases | Microservices, unpredictable traffic | Large-scale apps, steady workloads |
Fargate: Choose Fargate for workloads where simplicity and speed are essential. It’s great for startups, microservices architectures, and applications with varying traffic levels.
EC2: Choose EC2 when you need more control over your instances, especially for large, steady workloads or specialized applications that benefit from custom instance types and advanced scaling strategies.
By understanding the differences between Fargate and EC2, you can better select the right launch type for your Amazon ECS workloads, optimizing for cost, scalability, and performance.
The following use cases demonstrate Amazon ECS’s flexibility and power in different scenarios, making it an essential service for containerized workloads across industries. Whether your application requires microservices, batch processing, machine learning, or hybrid cloud deployments, ECS provides scalable and secure solutions that integrate seamlessly with the broader AWS ecosystem.
Amazon ECS is perfect for deploying microservices, a modern software design pattern where applications are split into smaller, independently deployable services. With ECS, you can run multiple containers representing distinct microservices, allowing independent scaling, updates, and deployment for each service. Fargate, as a serverless option, simplifies infrastructure management, letting developers focus entirely on their microservices architecture without worrying about provisioning or scaling the underlying infrastructure.
Key benefits:
Each microservice can have its scaling, update cycle, and isolated failure domain.
ECS enables easy integration with service discovery, allowing microservices to communicate with each other without hard-coding specific IP addresses.
Fargate enables containerized microservices to scale on demand without manual infrastructure management, improving development speed and agility.
Example: Consider a modern e-commerce platform with individual services for user authentication, product catalog, and payment processing. ECS enables these services to run independently, ensuring continuous deployment without service disruption.
ECS is highly suitable for batch processing workloads where compute-heavy jobs, such as data analysis, image rendering, video processing, or large-scale data transformations, must be run. Using ECS, you can create scheduled tasks to trigger batch jobs in response to specific events or at regular intervals.
Key benefits:
ECS integrates well with AWS Batch, which dynamically provisions the optimal quantity and type of compute resources based on the volume and complexity of jobs.
You can easily configure scaling policies to increase or decrease the number of batch jobs based on demand, ensuring cost efficiency.
Example: Companies requiring regular data ingestion and transformation, such as data from IoT devices or financial market feeds, can automatically leverage ECS to schedule and scale batch jobs.
Amazon ECS offers a scalable environment for running machine learning (ML) models in containers. Since ML workloads often require custom environments, packaging them into containers and deploying them with ECS provides flexibility and portability.
Inference and training: You can easily run ML inference jobs by deploying pretrained models inside ECS containers, handling real-time or batch inference. Likewise, ECS can run large-scale distributed training jobs across multiple compute nodes.
Flexibility: ML environments are typically complex. Containers help encapsulate the required libraries and dependencies, ensuring consistency across development and production environments.
Example: A recommendation engine deployed via ECS can be continuously updated with retrained models, ensuring efficient resource usage and seamless transitions without downtime. Also, ECS can handle GPU-based tasks for computationally heavy ML models.
Amazon ECS offers seamless integration for hybrid cloud workloads, allowing enterprises to manage containerized applications both on-premises and in AWS.
Key benefits:
With AWS Outposts, ECS extends to your on-premises data center, providing a consistent AWS experience across both cloud and on-prem.
ECS on Outposts helps in cases where data residency, latency, or regulatory compliance requires running applications on-prem but still benefiting from AWS’s cloud management tools.
Example: A company needing low-latency access to data stored in an on-premises data center can use ECS for hybrid cloud architectures. This allows them to run sensitive workloads locally while maintaining the scalability and flexibility of AWS.
Amazon ECS and Amazon EKS are the primary options when considering container orchestration in AWS. Let’s break down their differences and when to choose each based on application needs.
Amazon ECS is a fully managed container orchestration service that simplifies the deployment, management, and scaling of containerized applications in AWS.
Ease of use: ECS is native to AWS and offers seamless integration with other AWS services such as Amazon RDS, IAM, and CloudWatch. It uses its orchestration engine, so there’s no need to learn external technologies like Kubernetes.
Operational complexity: ECS is easier to set up and manage. It abstracts many operational complexities, making it ideal for developers who want to focus on application development without worrying about container infrastructure.
Cost: ECS can run on AWS Fargate (serverless), which reduces management overhead since there’s no need to manage EC2 instances. This also helps optimize costs as you only pay for the resources consumed by running containers.
Amazon EKS is AWS’s fully managed Kubernetes service, allowing users to run and manage Kubernetes clusters.
Ease of use: Compared to ECS, EKS has a steeper learning curve, as you must be familiar with Kubernetes concepts and operations. Kubernetes is an open-source container orchestration tool, and while it’s more flexible and widely adopted, it requires more setup and management effort.
Operational complexity: With EKS, managing clusters, networking, and upgrades can be more complex than with ECS. However, EKS provides a familiar environment with AWS-specific optimizations for those already using Kubernetes.
Cost: EKS can be more costly in terms of infrastructure and operational management due to the costs of the Kubernetes control plane and the need for specialized resources to manage the Kubernetes infrastructure.
Choose ECS if:
You want a simple, managed solution that tightly integrates with AWS.
Your team is focused on AWS-native development and doesn’t require Kubernetes.
You want to use Fargate for serverless container deployment, minimizing infrastructure management.
Choose EKS if:
You’re already using Kubernetes or plan to adopt it for multi-cloud or hybrid cloud environments.
You need Kubernetes’s flexibility and features, such as custom scheduling, advanced networking, or diverse tool support.
Your team has Kubernetes expertise or needs to integrate with existing Kubernetes clusters.
Understanding the pricing model of Amazon ECS is crucial for optimizing your costs and resource management. Here’s a breakdown of ECS pricing components:
Fargate follows a pay-per-use model, charging based on the vCPU and memory each running task or container uses.
Charges are calculated from when your image is pulled until the ECS tasks are terminated.
The minimum charge of one minute applies.
ECS on EC2 uses traditional EC2 instance pricing, which means you pay based on the type of instance, its capacity, and the time it runs.
Ensure that your ECS tasks are not over-allocated with resources, reducing wasted vCPU and memory.
Use AWS Cost Explorer to monitor ECS resource usage and spot optimization opportunities.
For EC2-based ECS, consider using Auto Scaling and Spot instances to dynamically adjust capacity and minimize costs.
Fargate offers flexibility with a straightforward pay-for-what-you-use model, while EC2-based ECS provides more control but requires thoughtful instance management to optimize costs.
This blog post covered a few important points related to Amazon ECS, such as its robustness and flexibility for running containerized applications at any scale. We discussed its launch types in detail and some use cases. We also discussed Amazon ECS’s integration with other AWS services, scalability, cost-efficiency, and ease of use, which make it suitable for startups and enterprises. We briefly analyzed the pricing for both launch types and some cost optimization tips.
As you dive deeper, consider exploring advanced topics like setting up blue/green deployment, CI/CD pipelines with ECS or leveraging its features for multi-region deployments to unlock even greater potential.
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