How To Optimize the Cost of Kubernetes


Cloud vendors provide a broad selection of embedded services, flexibility, quick operations and service-level agreements (SLAs) that ensure service availability. The next phase for organizations transitioning to cloud-native will come when they advance their Kubernetes implementations.

Kubernetes is a Greek word that means helmsman or pilot. It is sometimes shortened to K8s, with the 8 standing for the eight letters between the “K” and the “s." It is an open-source container orchestration platform that automates many of the manual tasks required in containerized application deployment, management and scaling. As software for container orchestration and the foundation of cloud-native application infrastructure, Kubernetes has dismantled the traditional application configuration and deployment boundaries.

A study from the Cloud Native Computing Foundation (CNCF) found that the use of containers in production has increased to 92%, up from 84% in 2019, and up 300% from the first survey in 2016.

This number could increase further as a result of the open-source community's support and DevOps teams' adoption of Kubernetes. And, even if the present prices stay the same, these users still represent a sizeable share of the Kubernetes market.

What Is a Container?

Simply said, a container packages a program and its dependencies so the application can operate smoothly without relying on the operating system's specs, removing the need for virtual machines and, thereby, conserving resources. Many applications are now broken into microservices to make them easier to maintain and update. These microservices are placed in containers before being deployed. A fairly effective container-using enterprise must handle hundreds of them.

Even though Kubernetes simplifies many tasks, problems inevitably arise. Networking, storage, monitoring, surveillance, lack of planning and, of course, cost management are among the most common issues.

With the containerization of an application comes the challenges of management, which become substantially simpler to address using Kubernetes. Kubernetes scales and manages pods, which consist of containers, and containers run applications.

Let's say you have a web application that you want to deploy on a cloud infrastructure. You have two options: you can either deploy the application on a standard virtual machine (VM) instance, or you can containerize the application and deploy it on a container orchestration platform like Kubernetes.

If you deploy the application on a standard VM instance, you have a relatively static environment. You can provision the VM with a certain amount of CPU, memory and storage, and then monitor and manage the resource utilization to ensure you are staying within your budget. For example, you can set up alerts to notify you if the CPU utilization exceeds a certain threshold, and then take action to scale up the VM if needed.

But if you containerize the application and deploy it on a container orchestration platform, you have a more dynamic environment. The platform automatically manages the deployment and scaling of the containers based on the demand for the application. For example, if the application experiences a sudden increase in traffic, the platform can automatically spin up additional containers to handle the load. This can be more efficient than scaling up a VM instance because you are only using the resources you need, but it can also make cost management more challenging.

In this case, it can be more difficult to track and manage the costs of the containers because the deployment is constantly changing based on the demand for the application. Additionally, the cost of the container orchestration platform itself can be higher than the cost of a standard VM instance. However, with careful monitoring and management, containerization can still be a cost-effective option for many workloads.

The benefits of Kubernetes can be seen in the following areas:

  • Scaling: Depending on the load, oftentimes we need to add/delete instances of applications. Kubernetes provides auto-scaling, up or down, of container pods.
  • Networking: Applications and their users need to interact with other applications. Kubernetes exposes the pods in the form of services so users and other pods running inside the cluster can access them.
  • Availability: While doing crucial operations like updating the kernel of the system(s) on which container pods are placed, the pods may cease to function or, worse, be erased. You can remove this danger using Kubernetes Replication Controller and ensure the application remains operational.
  • Multi-cloud deployment: Kubernetes allows users to run workloads on multiple cloud providers. Its portability provides flexibility to shift workloads from one cloud to another and eliminate vendor lock-in.

Cost Monitoring in Kubernetes

This is the first step to managing your Kubernetes costs. Monitoring Kubernetes should show you how you're spending your money. It’s also important to look for ways to save money.

Here's a basic rundown of how charges are incurred:

  • Cloud service providers charge for each server instance that makes up a cluster; charges are incurred when a container is deployed into a cluster and begins to consume the cluster's resource capacity. 
  • A charge is incurred each time a container process runs, which is similar to provisioning a server with a cloud service provider.
  • Traditional billing for managed Kubernetes is per cluster per hour plus any underlying resources consumed by the cluster. This makes container cost control difficult since you can’t always tell which resources are being used.

Though cloud companies give billing summaries that explain the costs for which you're paying, they generally just give a brief overview that is only marginally beneficial for multi-tenant Kubernetes clusters. As a result, it's common to employ third-party software to track Kubernetes use. Prometheus, Kubecost, Microtica and Replex are some tools that can do this.

How and Why to Constrain Resources

Effective resource limitations ensure that no Kubernetes system operator or application consumes excessive amounts of computing power, thereby saving you from unpleasant surprises like unforeseen billing adjustments. The resource limit you specify for a container is the maximum it can consume. If you set the memory cap for a specific container to, say, 4GB, the kubelet (and container runtime) will enforce it. The runtime of the container prevents it from going over the set resource limit. When a process in a container attempts to use more memory than is permitted, the system kernel aborts the process with an out-of-memory (OOM) error.

If many of your developers have direct access to Kubernetes, restricting resources is essential. By making sure resources are distributed properly, they help shrink the size of the cluster as a whole. Without restrictions, one individual could use all the resources and prevent others from working, which would increase the total demand for computational resources.

Be sure not to restrict your resources without a balance. If resource constraints are too restrictive, engineers and software cannot operate effectively. On the other hand, if resources are excessively high, they can be useless. Prometheus and Kubecost, two tools for Kubernetes cost optimization, can help strike the right resource balance.

Autoscaling with Kubernetes

With autoscaling in the cloud, you only pay for what you use. Because of this, you must modify the size of your clusters to suit your unique requirements. By autoscaling with Kubernetes, you can respond to sudden changes.

The two forms of autoscaling that are offered are horizontal and vertical. Horizontal autoscaling adds and deletes pods based on whether the load is above or below a predetermined amount. Vertical autoscaling maintains scale equilibrium for each individual pod. You can dynamically adjust your useable compute capacity to your actual demands using any of the two autoscaling techniques. This strategy, however, is not always the best because it does not work in all circumstances.

Selecting the Appropriate Instance

All the cloud providers now support Kubernetes solution on their platform. For instance, Microsoft offers Azure Kubernetes Service (AKS), AWS has Elastic Kuberenetes Service (EKS) and Google uses Google Kubernetes Engine (GKE). Let’s take the example of AWS to further explain the importance using the appropriate instance.

The AWS instance type that developers use to administer Kubernetes clusters has a direct influence on AWS Kubernetes prices. There are several types of instances, each having a distinct combination of memory and computation resources. The same is true for Kubernetes pods, albeit with different resource distribution. Make sure pods stack properly on your AWS instances to keep AWS Kubernetes expenses in check. The size of your pod should align to the AWS instance type.

When choosing which AWS instance to employ, consider the pod size, quantity and past resource use trends. The type of instance to employ may change depending on the storage or CPU needs of the application.

To optimize resource utilization and reduce AWS expense, ensure the CPU and memory usage of the Kubernetes pods correlate to the overall amount of CPU and memory available on the AWS instances they utilize.

Utilizing Spot Instances

The three different billing profiles for AWS instances are on-demand, reserved and spot instances. Although on-demand instances are the most expensive, they are also the most flexible. Spot instances have the lowest cost but can be stopped after a two-minute notice. To save money, you may purchase reserved instances for a predetermined period of time. As a result, the cost of running Kubernetes on AWS depends completely on the instance form selection.

Spot instances are useful for tasks that can withstand several interruptions but are not required continuously. Spot instances, according to AWS, may reduce your usage of the AWS Elastic Compute Cloud (EC2) on-demand instance costs by up to 90%.

Regularly Clean Up Kubernetes

When engineers are given permission to create namespaces, they can spin up clusters in the development phase or for testing, which many times aren’t cleaned up. Failing to clean up resources while using Kubernetes for continuous integration and continuous delivery (CI/CD) will result in a lot of wasted objects/clusters that contribute to the cost. It is wise, therefore, to remove any resources that have been dormant for an extended period. Such clean-ups can be automated depending on parameters like idle time or tags provided by Kubernetes. Temporary usage resources can be tagged with a specific key.

Optimize the Size of Your Kubernetes Cluster

Each circumstance requires a unique approach to managing a Kubernetes cluster. Before establishing your cluster, evaluate the specs for the application(s) you'll be running on it as a programmer. Some of the common metrics to consider when allocating resources to your cluster include the scale you need to reach, the number of requests per second received and the number of requests per second that a single instance of the application can handle, the memory needed by the application to run.

When developing apps for scale, it is critical to size your nodes correctly. A big number of small nodes is not the same as a small number of big nodes. As a result, finding the correct balance between these two aims would be the ideal way. The following are the attributes to keep in mind:

  • Resource requirements: Consider the resource requirements of your application, including CPU, memory and storage. Be sure to choose nodes that have enough resources to handle your application's workload. If your application is resource-intensive, you may need to choose nodes with high-performance CPUs, large amounts of memory and fast storage.
  • Performance expectations: Consider the performance expectations of your application, including response time and throughput. Choose nodes that can provide the necessary performance to meet your expectations. If your application requires low latency and high throughput, you may need to choose nodes with fast network connectivity and low network latency.
  • Availability requirements: Consider the availability requirements of your application, including uptime and fault tolerance. Be sure to choose nodes that can provide the necessary availability to meet your requirements. If your application requires high availability, you may need to choose nodes in multiple availability zones or regions to ensure your application can continue to run even if there is an outage in one of the zones.
  • Cost considerations: Consider the cost of the nodes you are choosing, including the cost of the hardware, software and any associated services. Choose nodes that are cost-effective for your application's workload. If your application has a variable workload, you may need to consider using auto-scaling to ensure you are paying only for the resources you need.

Overall, choosing the appropriate nodes to host your application requires careful consideration of the application's resource requirements, performance expectations, availability requirements and cost considerations. By taking these factors into account, you can choose nodes that are well-suited for your application and provide the necessary performance, availability and cost-effectiveness.

Optimizing Kubernetes for Your Resources

The first step in optimizing Kubernetes costs is to build a framework to start monitoring costs. Then, to avoid oversizing resources, establish restrictions that contain expenditures.

Choosing the optimal size for your resources is crucial for cost savings, and autoscaling may help. If you use AWS, you may look at its less expensive choices, such as spot instances. An automatic sleep schedule and cleaning idle Kubernetes resources are two further approaches to optimize costs. Finally, for even greater Kubernetes cost optimization, modify pod size and use resource tagging.

By incorporating these recommendations into your procedures, you will get a cost-effective Kubernetes system. This will free up funds for other important business activities and product developments.

ISG helps enterprises navigate a changing cloud market with the right strategies and tools. Contact us to find out how we can help.


About the author

Shuchi Pandey

Shuchi Pandey

Shuchi is a skilled professional with a diverse background in data analytics, management consulting, and business advisory. She is currently supporting the team in benchmarking, cost optimization, cloud transformation, and digital transformation in ISG.

She is currently involved in the opportunities for cost savings, full spend benchmarking, implementing new technologies, and leading large-scale organizational change. With a strong focus on collaboration and communication, she works closely with clients to understand their unique needs and develop tailored solutions that meet their specific goals.
She believes in staying up-to-date with the latest industry trends and best practices to provide the most suitable solution to the clients.