Many choices are presented to information technology (IT) teams when running containerized applications, addressing all levels of technical expertise.
It can be difficult to select one, considering that after making your choice, you’ll probably not migrate to another option anytime soon.
This post contrasts two weighty options: Amazon Elastic Container Service (ECS) and Kubernetes.
Both are capable platforms in the container orchestration and microservices management domains. And just before moving further, a refresher on containers does not harm. Containers have been popularized for easing code development, promotion, and deployment across many environments. They are abstractions at application layers, wrapping code with necessary dependencies, libraries, and environment settings into an executable package.
While the main objective for using containers is to simplify the code deployment process, managing thousands of them gets increasingly challenging. Another mechanism is needed to implement highly reliable deployments, scale applications as per load, swap unhealthy containers with new ones, load balancing, and expose ports.
That’s where container orchestration comes into aid. That aside, there’s a need for the means to run containers and manage their overall infrastructure. Many tools are available to solve this problem, but let’s narrow the focus to a few.
This piece compares ECS and Kubernetes, highlighting the benefits of each, and concludes with a direction on choosing the right one based on your project.
What is Amazon ECS?
Amazon ECS is a container orchestration service that streamlines deploying, managing, and scaling containerized applications. Basically, you define your application and its required resources. Then, Amazon ECS launches, monitors, and scales your app across compute options while allowing the integration of other AWS services needed. For instance, you can check the status and modify your clusters programmatically.
ECS allows you to deploy your apps through a group of servers, called clusters, using task definitions and application program interfaces (API) calls.
Traditional ECS – This version was launched in 2015 and is powered by Amazon EC2 to run Dockers containers easily in the cloud. Traditional ECS gives you underlying control over EC2 options allowing for flexibility. It means you choose the types of instances you’d like to run on your container. The model further hooks you with other AWS services that you can use to monitor and log activity on the EC2 instances.
Fargate ECS – Released in 2017 to run containers without the need for managing underlying EC2 compute options. Fargate takes a different approach by calculating the required CPU and memory. If you’d like to get workloads up and running quickly, this could be your best option, as you’ll not have to worry about the underlying computing options.
Simplified application architectures – ECS is a good option for applications with few microservices (those with few external dependencies or have a few moving parts) working independently.
Easy monitoring and logging – You can easily integrate ECS with AWS logging and monitoring tools like CloudWatch. You do not have to configure visibility into container workloads, saving you some time.
Easy learning curve – ECS is easy to learn. Hosted Kubernetes is gaining more popularity than traditional models like KOPS flavors and Kubeadm.
Serverless infrastructure – ECS allows you to run containers without the need for managing virtual machines; deploys containers while free from human intervention.
In-built security – By default, Amazon ECS is secured and cascades the security measures through an isolated Virtual Private Cloud networking mechanism.
Limitations of ECS
Limited storage – External storage is exclusively limited to Amazon, up to Amazon EBS.
Validation limitations – ECS is an Amazon-based product, thus unavailable for public deployments outside Amazon.
Vendor lock-in – ECS is biased; it can only manage its created containers.
ECS code unavailability – Much of the ECS code is not publicly available. Tools like AWS Blox (a framework for building custom schedulers) have a very small section of their code bases open-sourced.
What is Kubernetes?
Kubernetes, commonly called K8s, is an open-source software for automating containerized applications’ deployment, scaling, and administration.
Leveraging 15 years of experience running Google production workloads (combining the best ideas and community practices), K8s groups your application containers into logic units you can easily discover and manage.
Additionally, K8s’ primary features, like load balancing, persistent storage, automated rollbacks for containerized apps, secrets, self-healing for Kubernetes clusters, and configuration management.
Open source (no vendor lock-in) – Whether running on-premise or in the cloud, you can use Kubernetes without re-architecting the orchestration strategy. Unlike traditional software that incurs some license fees, K8s is free and open source. As if that’s not enough, K8s clusters run across public and private clouds providing virtualization resources on both entities.
High-powered flexibility – K8s is a great solution if your applications need high availability while supporting efficiency and scalability. This trait is tactically useful in applications generating high-income revenues. Simply put, it’s granular control over your workloads. In cases where you’d like to switch your applications to more powerful platforms, K8s is not limited to vendor lock-ins like ECS.
High Availability – As prementioned above, K8s design is geared to provide the availability of applications and their needed infrastructure making it a necessary feature for containers under production. Under high availability, there are a few techniques:
Health checks & self-healing – Kubernetes safeguards your applications from failures through regular inspections of nodes. If a pod or container is crushed due to an error, K8s automatically avails a replacement.
Load balancing and traffic routing – Regarding traffic routing, K8s will only send requests to the appropriate containers. And with load balancing, K8s distributes loads across pods, balancing your resources for several instances like outages, incidental peak traffic, or batch processing. Again, you can also use external load balancers if you want to.
Workload scalability – While prementioned above, let’s break it down further. K8s uses its resources to provide efficient scaling in the following criteria.
Auto-scaling – This feature allows you to automatically adjust the number of running containers according to CPU utilization and other CPU metrics.
Manual scaling – With the help of this feature, you can scale the count of running containers through the command line or interface.
Replication controller – This tool allows you to determine the number of pods that match your cluster specification; if there are few, it starts new, and if there’re too many, it terminates them.
Designed for deployment – K8s is specially designed to speed up the process of building, testing, and shipping software. Here are some of its offered features:
Automated rollbacks and rollouts – You may want to roll out some new configurations or application updates during development. K8s allows you to enact the process without application downtime. In the case of a failure, K8s robotically roll back to the previous version.
Canary deployments – You can take advantage of this feature by testing new deployments in production parallel to the previous version; K8s allows you to scale down the last version app while simultaneously scaling up the latest version.
Diverse support for programming languages and frameworks – Whether you come from the Go, Java, or .Net programming language background, Kubernetes supports many development languages and frameworks. If an app can run on a container, it runs on K8s.
Service discovery – Every developer desires that all provided services have a way of communicating with each other. However, the K8s operation model involves creating and destroying containers continuously, making some services non-existent in particular locations. In traditional development, a service register would be adapted to track the locations of these services. K8s solves this problem through a native service concept to group pods and discover services seamlessly. So, K8s provides IP addresses for all pods, allocates DNS names for every pod set, and then balances the load traffic on every pod set. This architecture generates an environment where service discovery is abstracted from every container.
Vibrant Community – K8s is backed by a vibrant community with thousands of developers leveraging its services. At the time of writing, over 100 million developers use K8s to discover, contributing 330 million projects. The community shows no signs of slowing down and encourages collaboration among developers.
Limitations of Kubernetes
Steep learning curve – To get started with Kubernetes, you’ll need to understand its landscape. Besides, delivering an end-to-end solution requires the inclusion of a variety of technologies and services. And with the supplementary technologies varying significantly (at times, some solutions date back to the UNIX dominated while others are new technologies with a low adoption), figuring out which ones to include can be hectic. You’d also need to figure out how all components fit together to provide a more comprehensive solution for particular problems. The documentation is available, but you’ll need to understand how to deliver and manage these services.
Differentiating features and projects – Understanding the differences between projects and features can be challenging. While you can easily acquire advice on managing projects, you may not get a clear distinction between features and community projects.
Knowledge beyond Kubernetes – Kubernetes is a sophisticated platform. With all this complexity in delivering solutions, you’ll likely encounter some confusion, especially if you’re new to it. Yet, organizations still want to provide solutions (like data stores-as-a-service), amplifying the learning curve. If you use such services on your product, you must broaden your knowledge beyond Kubernetes.
Managing Kubernetes is difficult – Getting to production with K8s is one thing. You’ll need to supply all the required resources for your applications to manage it. You’ll also need to handle all the security and integrate it with your infrastructure. Also, you’ll need some high-level expertise to process and operate its tools effectively. You’ll need to get profound knowledge to manage Kubernetes clusters, monitor and troubleshoot clusters, and support them at scale.
Comparing ECS and Kubernetes
Here’s a side-by-side comparison showing the differences:
Point of difference
Applications are deployed by combining pods, nodes, and services.
Application deployment takes the form of tasks. The tasks are container instances – for example, Docker containers running on ECS instances.
Complex as you have to deploy and configure clusters manually.
Easy deployment via the AWS console.
Node support (number of machines)
5000 Nodes per cluster.
1000 Nodes per cluster.
Up to 300,000 containers per cluster.
Limited by utilized infrastructure capacity.
Pods are exposed through services used as load balancers behind ingress controllers.
Two load balancers available; ELB-Application or Network.
ECS is free, but you have to pay for EC2 resources.
Well optimized for a single large cluster.
Preconfigured with requirements and container requirements.
You define autoscaling parameters when building deployments.
You use monitoring services like CloudWatch to auto-scale based on CPU, memory, and custom parameters.
Two health checks are available: readiness and liveness.
Achieved through monitoring services like CloudWatch.
Enacted through environment variables or DNS.
Attained through monitoring services – CloudWatch.
Use Cases of ECS and Kubernetes
Here’s how ECS and Kubernetes containerization technology is revolutionizing industries:
ECS INC International highlights numerous use cases where the ECS technology has been implemented. In modern medical devices, you’ll find revolutionized methods to treat patients and drug delivery techniques. Many tools exist, like electronic inhalers, medical auto-injectors, and infusion pumps.
In the IoT domain, we have smart home devices. If you shift attention to the automotive industry, we have smart electric cars with enhanced driving experience and improved safety measures like assisted braking systems.
So far, that’s the tip of the iceberg; you can check out more applications of ECS that are not limited to wireless tech, wearable devices, and industrial use cases.
On the other end, Kubernetes has its share of practical applications. First, the IBM cloud offers private, public, and hybrid functionalities across a wide scope of runtimes.
Spotify, a giant in the music streaming field, leverages Kubernetes technology to facilitate seamless operations, up to 10 million requests per second. While these are real-world use cases, K8s serves more functionalities in microservices architecture, cloud-native network functions, machine learning, and pivoting the software development life cycle.
Having walked through this guide, you have a solid overview of the merits and demerits of opting for either ECS or K8s. The key to picking the right option is based on a few arguments. You’ll have to weigh between cost, service limitations, and talent costs.
If you’d like to use a free service, K8s will be your number one choice. However, you’ll need solid talent or skills to handle the complexity that comes with it. While K8s does not have vendor lock-in limitations, it will require an in-depth understanding of how the platform work. ECS, on the other hand, got fast configurations.
John Walter is an Electrical and Electronics Engineer with deep passion for software development, and blockchain technology. He loves to learn new technologies and educate the online community about them. He is also a classical organist.
Rashmi has over 7 years of expertise in content management, SEO, and data research, making her a highly experienced professional. She has a solid academic background and has done her bachelor’s and master’s degree in computer applications…. read more