Kubernetes is ruling the container market. According to a CNCF survey, the use of Kubernetes in production in 2020 was 93%, up from 78% in 2019. Moreover, the survey reveals that the use of containers in production in 2020 was 92%. This figure is up 300% from CNCF’s first survey in 2016.
Due to the adoption of Kubernetes by DevOps teams and the open source community’s encouragement, this figure could grow more. And if it stays at present prices, this market share still is a significant portion. This means that even though Kubernetes makes a lot of things easier, challenges will always appear, as the survey confirms. Namely, the problems listed include networking, storage, tracking, surveillance, a lack of preparation, and, of course, cost management.
Running Kubernetes can be very costly, especially if done inefficiently. When businesses first try to incorporate Kubernetes in their organizations, they usually use the same architecture and setup that performed well with initial research experiments. However, this setup is often unoptimized and companies don’t think about expenses right away. This could save a lot of unnecessary costs and encourage the implementation of good habits from the beginning.
Check out our comprehensive guide on creating an AWS cost optimization strategy.
In this article, we’ll go over several methods for controlling and lowering Kubernetes costs. Moreover, as Amazon EKS is the most common container management approach after self-managed Kubernetes, we’ll offer more actionable advice on Kubernetes cost optimization on AWS.
This is the most logical step towards starting to manage your Kubernetes costs more efficiently. Monitoring should show you how you’re spending your money when it comes to Kubernetes. More importantly, you should identify saving opportunities.
Cloud vendors offer billing summary that provides information about what you’re paying for. However, they will usually only include a simple overview that is only slightly useful for multi-tenant Kubernetes clusters. This is inaccessible in private clouds. As a consequence, it’s popular to use external software to monitor Kubernetes consumption. Prometheus, Kubecost, Microtica, and Replex are some useful tools in this field.
Choose the tools you’ll use and how you’re going to monitor your Kubernetes costs. Then, start implementing more concrete actions for Kubernetes cost optimization.
Resource constraints that are effective guarantee that no program or operator of the Kubernetes system uses too much processing power. As a result, they protect you from unwelcome shocks such as unexpected billing changes.
A container can’t use more than the resource limit you set. The kubelet (and container runtime) implement the memory cap if you set it to, for example, 4GB for a particular container. The container’s runtime prohibits it from exceeding the configured resource cap. When a process in the container attempts to use more memory than is permitted, the system kernel aborts the process with an out-of-memory (OOM) error.
Developers can enforce limits in two ways. First, reactively, when the system detects a violation. The second way is by enforcement, which means that the system never allows the container to go over the limit. They can implement the same constraints in various ways for different runtimes.
Limiting resources is crucial, especially if many of your developers have direct access to Kubernetes. They ensure that available resources are shared fairly, reducing the overall cluster size. Without limitations, one person could use all energy. This would prevent others from working, resulting in a need for more computational resources overall.
However, be careful not to limit your resources without any balance. Engineers and software cannot function properly if resource limits are too low. On the other hand, they are often worthless if they are too high. Some Kubernetes cost optimization tools, like Prometheus and Kubecost, can help you decide the balance with your resources.
To find out more about limiting resources for containers, check [this page of the Kubernetes documentation}(https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/).
Autoscaling means paying for what you need. That’s why you have to adjust the size of your clusters to your specific needs. You can allow Kubernetes autoscaling to be able to adapt to quick variations.
Horizontal and vertical autoscaling are the two types of autoscaling available. In a nutshell, horizontal autoscaling involves inserting and removing pods depending on whether the load is above or below a specified level. The scale of individual pods balances with vertical autoscaling.
Both methods of autoscaling are useful for dynamically adapting the usable computational capacity to your real needs. This approach, though, is not necessarily ideal as it does not function with all use cases. For example, when something is requiring computational resources it is therefore not automatically downscaled.
AWS Kubernetes costs are under a direct impact by the AWS instance developers use to manage Kubernetes clusters. Instances come in a number of different forms, with varying memory and compute resource combinations. Kubernetes pods are the same way, with different resource allocation. The key to keeping AWS Kubernetes costs under check is to make sure pods stack effectively on your AWS instances. The AWS instance should match the size of your pod.
The scale, number, and historical resource utilization trends of pods all play a role in deciding which AWS instance to use. Applications may have different storage or CPU requirements, which affects the type of instance to use.
Ensuring that the Kubernetes pods’ resource consumption correlates to the overall CPU and memory available on the AWS instances they use is critical for optimizing resource use and lowering AWS costs.
Check the Amazon EC2 instance types here and choose the one that suits your needs best.
When it comes to AWS instances, they are available in several billing profiles: on-demand, reserved, and spot instances. On-demand instances are the most costly but have the best degree of flexibility. Spot instances have the lowest price. However, they can be terminated with a 2-minute warning. You may also get reserved instances for a set amount of time to save costs. As a result, the choice of instance form has a direct effect on the cost of operating Kubernetes on AWS.
You can utilize spot instances for workloads that you don’t permanently need and that can handle a lot of interruptions. AWS claims that spot instances will help you save up to 90% on your EC2 on-demand instance prices.
If spot instances aren’t a choice for your application since it must still run without delay, you may get a discount if you agree to use the services for a fixed period of time. You will get a substantial discount if you agree to a one- or three-year usage term. According to AWS, this could be between 40% and 60%.
No matter if you run the Kubernetes clusters on on-demand, reserved, or spot instances, ensuring that underutilized clusters are terminated is crucial for cost management. You can calculate the expense of AWS EC2 by the period of time you are provisioning them. Even though underutilized instances have a much greater resource impact than necessary, they still cost you the full expense of running an instance.
To put it simply, if a development team uses a cloud-based Kubernetes environment, they only use it during business hours. If they work 40 hours a week, and the environment is still working the rest of the time, they don’t have to pay for the remaining 128 hours when they aren’t using it. This, of course, won’t be the case in every team, especially if they have flexible working hours, but turning off the environment when no one is working could significantly enhance Kubernetes cost optimization.
Developers can set this up by automating a sleeping schedule and waking up the environments only when they need them. Setting up this schedule means that the system will automatically scale down unused resources. This guarantees that the environment’s condition is saved. Moreover, the environment will “wake up” easily and automatically when the engineer needs it again, meaning that there is no disruption in the workflow.
If you give engineers full access to build namespaces on demand or use Kubernetes for CI/CD, you can end up with a lot of unused objects or clusters that are still costing you money. And if you have a sleep mode that decreases computational resources, it is only for momentarily inactive resources, still retaining storage and configuration. That’s why, when you notice that some of your resources have been inactive for a very long time, removing them would be a smart thing to do.
Managing a Kubernetes cluster is different for each case. There are various methods for correctly sizing your cluster, and it is important to develop your application for consistency and durability. As a programmer, you’ll frequently need to consider the specifications for the applications you’ll be running on your cluster before building it.
Right-sizing your nodes is very important when designing apps for scale. A large number of small nodes and a small number of large nodes are two very different things. That’s why the best approach would be to find the right balance between these two ends.
However, different requirements of your apps require different numbers and sizes of nodes. Check this article to find out what size and number you need for various app cases.
In any environment, whether cloud, on-premises, or containers, tagging resources is a smart idea. Services are bound to go unnoticed in enterprise Kubernetes environments with numerous test, staging, and development environments. These services become a chronic burden on AWS prices, even though they aren’t used. Companies should use tagging to guarantee that all services are controlled.
AWS provides a robust tagging scheme that you can use to mark services belonging to Kubernetes. You may use these tags to stay on top of resources, resource holders, and resource usage. Effective tagging allows you to easily classify and eliminate unused services. You’ll be able to assign costs and view expense breakdowns for various services once these tags are enabled in the AWS Billing dashboard.
The first step in your Kubernetes cost optimization is to create an outline and begin monitoring them. Then, to avoid unnecessary computational resource usage, you can set limits, which would make the costs more manageable.
Determining the best size for your resources critical for cost reduction, and autoscaling will also help. If you use AWS, you can check their less costly options, like spot instances. Additional steps to remove idle resources include an automated sleep schedule and cleaning unused Kubernetes resources. Finally, adjust pod size and implement resource tagging for even better Kubernetes cost optimization.
Incorporating these tips into your processes will result in a cost-optimized Kubernetes system. This will save your money for more crucial business operations and product improvements.