Kubernetes Cost Optimization: Key Techniques & Strategies

Among the advice that personal finance gurus tend to offer is to track small expenses that add up over time. For example, some folks have a penchant for expensive takeout coffee, and although they only spend a few bucks a day feeding their caffeine habit, they're often shocked when they discover that their caffè macchiatos are costing them hundreds of dollars in total each month.

Kubernetes cost management is similar. In most cases, there is no single, big-ticket expense that leads to outsized Kubernetes bills. Instead, it's many small things – compute costs, storage costs, cluster management costs and so on – that add up to break your Kubernetes budget.

That's the bad news. The good news is that Kubernetes cost optimization is possible – and unlike replacing expensive lattés with drip coffee you make at home, streamlining Kubernetes costs doesn't require you to settle for a simpler product that requires more work on your part. With the right approach to cost optimization, you can maintain excellent cluster performance without overpaying or increasing the complexity of cluster administration.

Keep reading for a breakdown of everything you need to know about Kubernetes cost optimization, including what it means, why it's important, which challenges businesses tend to face when managing Kubernetes costs and effective strategies for reducing Kubernetes spending without compromising performance or reliability.

What Is Kubernetes Cost Optimization?

Kubernetes cost optimization is the practice of making Kubernetes infrastructure and workloads as cost-effective as possible. To do this, you need to identify aspects of your Kubernetes environment where you are paying more than necessary, then implement changes that reduce costs while still allowing you to maintain the levels of performance and reliability that your workloads require.

Kubernetes cost optimization is often discussed within the context of FinOps, a buzzword that refers to initiatives aimed at reducing cloud costs of all types. Because many (though not all) Kubernetes environments are hosted in the cloud, it's reasonable to think of Kubernetes cost optimization as a component of cloud cost optimization in general.

That said, Kubernetes is different in many ways from other types of cloud environments, so it's important to understand the unique considerations that factor into managing costs in Kubernetes (which we'll cover in greater depth below).

The Importance of Kubernetes Cost Optimization

Obviously, no business wants to pay more than it needs to for any product or service, so keeping Kubernetes costs in check is a step toward improving the overall financial health of your business.

Kubernetes costs in check is a step toward improving the overall financial health of your business.

However, optimizing the costs of Kubernetes in particular is important for two main reasons: Kubernetes is everywhere – 96 percent of businesses are now using or exploring it – and Kubernetes is complicated. As we explain below, the complexity of a typical Kubernetes environment, which includes many different components with varying financial implications, makes it especially challenging to avoid overspending on Kubernetes infrastructure.

The bottom line here is that Kubernetes can become a major source of expense for businesses, and it's also one that is particularly challenging to tame from a cost perspective. That's why Kubernetes cost optimization deserves special focus on the part of any organization that deploys workloads on K8s.

Which Factors Contribute to Kubernetes Costs?

Again, there's no single major expense that accounts for the majority of Kubernetes costs. There are instead several smaller spending categories that add up to create your total Kubernetes bill.

Compute Costs

First, you have compute costs. Kubernetes requires compute resources both to operate the control plane (which manages workloads) and to power workloads themselves.

The exact amount of compute capacity of your cluster may vary depending on how many applications you’re running and whether you choose to set up multiple control plane nodes to increase availability. Pricing will also vary based on whether you’re running Kubernetes using servers you won outright, or via cloud-based virtual machine instances.

Either way, however, you have to pay for all the servers that you join to your clusters. The more servers you have and/or the more compute resources you allocate to them, the higher your Kubernetes bills will be.

Network Costs

If you run Kubernetes in a public cloud, you have to pay networking costs. In particular, you'll pay egress fees, which cloud providers impose when you move data out of their clouds into other infrastructure.

Total networking costs will depend on how much data you move and what your cloud costs are for egress. But in general, expect to pay about ten cents for each gigabyte of data you transfer. That may seem small, but if you’re moving hundreds of terabytes' worth of information each month, that cost adds up quickly.

Storage Costs

If you’re deploying stateful applications (meaning those that require persistent storage that remains available even if the app stops running) in Kubernetes, you'll need to pay for storage infrastructure to support them. And even if you only run stateless apps, you'll need some amount of storage to support control plane data stores, like etcd.

Here again, your storage costs will depend on which type of storage media (like on-premises storage arrays or cloud-based storage) you choose. And although the cost per gigabyte of storage is typically minimal, it can add up when you have lots of data to store.

External Cloud Services Costs

Beyond the core compute, network and storage costs associated with Kubernetes, cloud providers impose various additional fees if you deploy Kubernetes using one of their managed services. For example, Amazon EKS charges $0.10 per hour for each cluster that you have running. You may also have to pay for cloud cost monitoring or logging services that you use to support your clusters.

Kubernetes Cost Optimization Challenges

Keeping Kubernetes spending in check would be relatively easy if you could reliably predict how much of the various types of resources described above you need, allocate them to your clusters accordingly and track your spending on an ongoing basis to obtain clear-cut visibility into whether what you're spending aligns with what you expected.

Unfortunately, this is not how most Kubernetes environments work. Several factors make Kubernetes cost management uniquely challenging for many organizations.

Dynamic and Complex Infrastructure

Almost by definition, Kubernetes manages dynamic and complex workloads. After all, if your workloads didn't need to scale up and down frequently, and you didn't need to update them regularly, you probably wouldn't use Kubernetes to orchestrate them in the first place. (You'd just use a VM or some other less dynamic solution.)

But because Kubernetes environments are highly dynamic, predicting how many resources they'll need can be a real challenge. Your workloads might need one level of compute resources during periods of normal activity, for example, but their requirements will spike when traffic peaks.

Complicating this challenge is the fact that you can't always auto-scale Kubernetes infrastructure allocations. While it's possible on some cloud-based Kubernetes distributions to configure rules that will automatically add nodes to your cluster in response to fluctuations in demand, in general there is no Kubernetes-native way to scale nodes up and down automatically. Nor can you adjust the compute or storage allocations of your nodes on the fly.

As a result, you need to decide ahead of time how many nodes to add to your cluster and which resources to allocate to each one. And if you over-allocate, you'll end up paying for infrastructure that isn't used, leading to unnecessary spending. Plus, if you under-allocate, your workloads may not have the resources they need to operate, which is part of the reason why it can be easy to end up paying for more infrastructure than you need.

Limited Visibility into Kubernetes Costs

Kubernetes isn't like your credit card: You can't simply log in and see how much you owe at any given time. Kubernetes itself doesn't natively report any data whatsoever about spending (which, to be fair, it would have no way of doing, since Kubernetes doesn't know how much your compute, storage and other infrastructure costs). Some cloud providers and third-party vendors do provide Kubernetes cost optimization tools, but they don't always track every cost in real time.

So, the best you can do is estimate your costs based on total resources consumed. You can infer that data based on Kubernetes monitoring metrics, but only if you continuously and systematically track resource consumption across all elements of your cluster.

Misaligned Incentives

The people inside a given business who manage Kubernetes are typically not the same people who manage finances, and vice versa. As a result, each team tends to have misaligned incentives.

Kubernetes admins care about optimizing the performance and reliability of their environments, because they're the ones who will get chewed out if something breaks. Meanwhile, the finance people care about reining in spending, but they typically have little understanding of Kubernetes and its unique needs. If you tell them, "We can't reduce costs on compute because doing so would mean no longer operating in high-availability mode and we can't take that risk," they probably won't be swayed. They only see cost metrics and outcomes.

This isn't an insurmountable problem – it's possible to educate Kubernetes admins about finances and educate the finance team about Kubernetes – but by default, there tends to be little collaboration or shared understanding between these groups, making it even more challenging for Kubernetes admins to ensure that they are optimizing for cost in addition to performance and reliability.

6 Effective Strategies for Kubernetes Cost Optimization

Now that we've talked about the problem, let's talk solutions for keeping Kubernetes costs under control, while simultaneously ensuring that you meet your performance and reliability goals.

Rightsize Your Kubernetes Cluster

First and foremost, make sure to rightsize your Kubernetes cluster by selecting the right node configurations. This is the single most important factor in Kubernetes cost management because over-provisioned nodes will lead to extraneous costs.

Of course, as we noted above, the dynamic nature of Kubernetes can make it challenging to predict exactly how to size your cluster. But if you monitor your cluster operations over time, you can gain an accurate understanding of what its resource consumption trends look like and which resources you need to allocate to it.

Fine-Tune Service Horizontal Scaling

Although Kubernetes doesn't provide a native way to add nodes to a cluster, it can autoscale workloads so that the resources available to them change based on their actual needs. This practice, which is known as horizontal scaling, helps ensure that workloads have the resources they require to operate well at times of high demand, but that they don't receive unnecessary allocations during other periods.

Horizontal scaling doesn't take place automatically, but you can set it up by configuring a HorizontalPodAutoscaler.

Properly Adjust Rates and Limits

Setting the right rates and limits is another key step in Kubernetes cost optimization. Since rates and limits define ranges of CPU, memory, and storage resources that workloads can consume, configuring them properly helps ensure that you don't assign some workloads more resources than they require, leading to bloated infrastructure spending.

Here again, the challenge is that you don't always know exactly how many resources a given workload will require, so setting rates and limits can be tricky. But if you monitor workloads over time, you can gain a good sense of which resources to assign to them.

Use Kubernetes Multi-Tenancy

In general, the more clusters you operate, the higher your costs will be. Each additional cluster comes with additional costs due to the need to operate the control plane. Plus, with separate clusters, you can't easily share nodes from one cluster to support another one.

For that reason, embrace multi-tenancy where feasible. Multi-tenancy means running multiple workloads in a single cluster. In some cases, this is not desirable because you may need strict boundaries between workloads for compliance or reliability reasons (you may not want a buggy application that you're testing to run alongside a production app). But in general, if multi-tenancy makes sense, go for it to reduce spending.

Optimize Storage Usage 

The more efficient your Kubernetes storage strategy, the more cost-effective your cluster will be. Practices like deleting old log files or moving outdated data to a less costly storage solution (such as a cold object storage service in a public cloud) can reduce what you pay for storage in Kubernetes. So can making changes to your applications to avoid having them write unnecessary data to disk.

Implement Cost Monitoring and Reporting

You can't optimize what you can't see, and Kubernetes spending is no exception. As we mentioned, there are no native Kubernetes cost optimization tools or cost monitoring and reporting features. But you can track your spending by monitoring resource consumption continuously, then mapping it to your resource costs.

How to Achieve Kubernetes Cost Optimization with groundcover

Kubernetes observability and resource consumption monitoring – practices that help optimize Kubernetes costs – are where groundcover excels. By adding groundcover to your collection of Kubernetes cost optimization tools, you can use eBPF to track resource usage across all layers of your Kubernetes cluster so that you'll know what you're spending in real time.

Plus, because groundcover can track resource usage based on individual Kubernetes objects (like containers, Pods and nodes), you can quickly see which components are costing you the most, or at least more than you planned for. By attaching the set limits and requests to the actual measured resource usage, groundcover provides insights into underutilized nodes,or rogue workloads that exceed their estimated requests and take far too much resources. Both ends of this problem (under or over utilization) end up in a suboptimal Kubernetes cost management and requires attention.

Going further, groundcover allows you to generate a profile of your Kubernetes environments' resource requirements over time. With that insight, you can rightsize your clusters and node configurations, allowing you to achieve the best performance outcomes at the lowest possible costs.

With the right cost optimization techniques, Kubernetes doesn't have to be costly

By gaining deep insight into the resource requirements of your clusters and workloads, you can accurately track and predict how many resources you actually need, then adjust your configurations so that you avoid wasting money on infrastructure you don't require, while still ensuring that your workloads always have the resources they need to perform at their best.