Kubernetes is an open-source platform designed to automate deploying, scaling, and managing containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Because of this, it’s a great way to build an automated infrastructure that supports application reliability while improving development speed.
In fact, this is such a powerful service that the businesses using Kubernetes for Big Data has grown by 35% from 2022 to 2023. You can think of it as the “operating system” of the cloud as it has built-in deployment, scaling, and resiliency capabilities.
Running Kubernetes on AWS, specifically through the Amazon Elastic Kubernetes Service (EKS), gives a business a lot of options when it comes to managing their data workloads. While the basic price of $0.10 per hour per Kubernetes cluster for EKS might seem very affordable, these costs can soon get out of hand if not managed correctly — especially as this is on top of the usual costs for other AWS services such as EC2 instances.
Optimizing Your EKS Costs With Proven Data-Driven Approaches
As well as increasing overheads, there’s often a worry with containerized services such as Kubernetes that it’ll over-provision resources on the orchestration layer as it scales to meet demand. On top of this, Kubernetes’ intricate architecture requires additional development efforts to configure and run, this additional time and expertise should be considered when looking at the overall cost of Kubernetes, as you’ll likely need to hire an EKS expert if you don’t have one on your team already.
There are ways to mitigate the direct costs of Kubernetes, from properly configuring the service to delegating to managed services to optimizing the workload. Here are some effective cost-reduction methods.
Rightsizing EC2 Instances
This is one of the most effective ways to cut cloud costs. By matching the right size EC2 instance with the needs of your workload, you avoid paying for unused resources. This starts by analyzing your usage to gain insight into performance and other patterns. This will help you to identify idle instances as well as those that are underutilized.
As EC2 instances are fixed, you pay for the entire instance, regardless of how much is actually being used. By removing idle instances and right-sizing those that are under-utilized, you can save money.
Use Spot Instances For Non-Production Workloads
For anything non-critical, using a spot instance could help you save as much as 90% on your costs. However, the reason for this discount is that AWS could reclaim those spot instances at any time, so they are often only used for non-production workloads. If you want to use spot instances for more critical work, it becomes even more necessary to leverage automated management and cost optimization tools.
Spot instances can also be used in production workloads. However, it’s integral that there be a strategic ratio between spot and on demand on a prod cluster, in order for it to perform optimally.
Enable Autoscaling and HPA
Autoscaling can automatically adjust the number of worker nodes based on CPU and memory utilization. The EKS Cluster Autoscaling tool searches your cluster for pods that are underutilized and then reschedules them onto nodes that lack resources. This tool can work out of the box but you may need to configure it to fit your needs.
HPA, or Horizontal Pod Autoscaling, is also an effective strategy for AWS Kubernetes cost reduction for several reasons. Firstly, it automatically adjusts the number of running pods in a deployment or replica set based on observed CPU utilization or other select metrics, ensuring that resources are used efficiently and not over-provisioned, thus reducing costs.
Secondly, by allowing the system to dynamically scale according to the actual demand, it minimizes the need for manual intervention, thus saving administrative time and efforts, which further adds to the cost savings.
Use AWS Fargate for stateless workloads
For stateless workloads, AWS Fargate might be a better option than EC2 as it only charges based on what’s used. While EC2 instance costs are fixed, Fargate workloads are priced based on the CPU and memory consumed for each task.
This allows you to be more flexible and lets you deploy your workloads on EKS without the need for EC2 instances as Fargate automatically configures node infrastructure. This pricing model allows you to save money on unpredictable workloads or those with variable resource requirements by avoiding over-provisioning.
Use cost optimization tools
Tools such as Intel-Granulate’s Capacity Optimization solution can help reduce Kubernetes costs by automatically optimizing use to avoid over-provision. The capacity optimization provided by this tool is ongoing and doesn’t need human intervention to work. This means it can adapt to changing situations no matter what happens in the environment — from managing peak usage to handling an infrastructure upgrade.
Claroty, an IoT cybersecurity company, saved 23% on EKS costs using Intel-Granulate. It was able to do this in just two weeks with its Spark cluster running multiple nodes on EKS in AWS. This reduced memory use by 50%, which led to Claroty deploying Intel-Granulate across the entirety of its Big Data clusters.
Navigating the cost landscape of Kubernetes can be complex, but with the right strategies and tools, businesses can effectively manage and reduce their expenses. Right-sizing EC2 instances, leveraging AWS Fargate for stateless workloads, utilizing spot instances for non-production workloads, and enabling autoscaling are all effective strategies for cost reduction. Additionally, tools such as Intel-Granulate can provide autonomous orchestration and capacity optimization, leading to substantial savings, as demonstrated by companies like Claroty.
By understanding and implementing these strategies, businesses can harness the power and flexibility of Amazon EKS without letting costs get out of hand. If you’d like to learn more about how Intel-Granluate can help you optimize Kubernetes, book a free assessment here.