Containerization and modern cloud infrastructure have made it possible to deploy even the most complex applications without making big initial investments. With most cloud computing models, you pay fixed fees only for the resources you use, and you are all set. Scaling up (and down) becomes easier too since you are not actually investing in the physical hardware supporting the cloud cluster.
While the available services certainly make cloud computing more accessible, they still could cost a lot, especially when the cost of using cloud resources is not managed properly. It is easy to fall into the trap of buying into more resources that are not fully utilized and ending up paying more than you should.
Cost-efficiency is one of the key benefits of deploying a cloud-native app, so it is not surprising to see service providers like Amazon working towards making their services more affordable. There are also tools that can help you optimize your cloud costs effectively. In this article, however, we are going to focus on what you can do to optimize the cost of your AWS EKS clusters.
AWS EKS and EC2
You can run Kubernetes on an EC2 instance and have complete control over how the Kubernetes cluster is set up. This approach offers the most flexibility, but there is some degree of complexity involved in managing your own Kubernetes cluster. The control plane of your cluster is accessible directly, and you can have multiple EC2 instances for different components.
AWS EKS offers a managed Kubernetes environment. This reduces the complexity mentioned earlier; it certainly makes deploying Kubernetes clusters easier. There is one significant impact this has on your cloud cost, and that is the lower overhead cost of running Kubernetes on AWS EKS. You don’t have to maintain a large team of DevOps specialists to keep your clusters running.
The only extra charge you have to pay when using AWS EKS is the cost of maintaining the control plane. Everything else is charged on a pay-as-you-go basis, allowing you to fully take advantage of EKS to run an efficient environment. Worker nodes can even use different instance types for maximum cost efficiency.
Good Design Principles
A consumption model and a way to manage your cloud expenses are key to optimizing the cost of using AWS EKS. For starters, there needs to be a way to monitor resource usage. Instances that are not fully utilized need to be merged or removed completely. The standard utilization threshold is 75%, but you can make adjustments based on your budget.
At the same time, your cloud expenses need to be monitored closely. Month-to-month expenses are great metrics to keep track if you want to compare the scale of your cloud operations with the cost of keeping them operational. When you see a jump in monthly cloud expenses, you know you have some digging to do.
These two steps are in line with the AWS Well-Architected Framework. Cost is a big part of that framework indeed. That actually brings us to the next step, which is….
Cost-Aware Processes
The next thing to do is establishing a series of cloud environment management processes that always take costs into account. You can start by making sure that the financial impact of changes—changes such as the deployment of a new component or scaling up the entire cloud environment—are always considered.
Everyone involved in managing and utilizing the cloud environment must also be aware of cloud expense management. They need to monitor usage proactively, disable resources that are not fully utilized, and be active parts of the management process. Fortunately, AWS has tools like Cloud Cost Management to help your team.
Right Sizing
We’ve touched on the importance of allocating the right resources for the right operations. Right sizing is a step that must never be neglected. Before deploying new worker nodes or pods, you have to assess the CPU and memory requirements of each node, and then allocate resources accordingly. This is a process that’s best done early.
For an existing system, perform a thorough analysis. Using AWS Consolidated Billing, for instance, you can keep track of all cloud expenses and pinpoint which nodes use which resource in great detail. AWS Cost Explorer offers more extensive tools that also allow you to estimate resource utilization based on past operations.
Auto-Scaling
Scaling needs to be fully automated in order to maximize cost-efficiency. In fact, automation in other parts of cloud management is a great way to cut costs and remain efficient. There is no need to manually scale the whole cloud environment up when there is a spike, and there is no need to keep allocating resources when the traffic is down.
Auto-scaling relies on performance parameters to work properly. After all, you want the cloud environment to maintain a certain degree of application performance, don’t you? With EKS being entirely elastic, you can schedule worker nodes and provisioning of resources based on specific usage patterns and parameters.
Auto-scaling even supports grouping. A simple parameter, --balance-similar-node-groups=true, lets you scale groups of worker nodes together.
Expand Your Options
That brings us to the available resources. While AWS EKS is already very affordable, you don’t have to settle for on-demand resources if the goal is maximizing cost efficiency. You can consider moving some of the more permanent worker nodes to spot instances. You can also use spot instances on a larger scale.
Tools like Cost Explorer make monitoring your usage patterns easy, and the insights you get from those tools will allow you to make better, more accurate forecasts of your cloud usage. Using the forecast, you can buy into spot instances and get the resources you need at a lower price. Just make sure you monitor your resource usage to avoid unnecessary costs.
The true key to optimizing the cost of AWS EKS, however, is down-scaling. A lot of development teams and organizations don’t really scale down their cloud environment even when they can. Don’t make the same mistake. If there are cloud resources you don’t use—and will not use in the near future—it is based to destroy them and save.
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Juan Ignacio Giro
