Running applications on Kubernetes can be expensive if not managed properly. Clusters, on average, use only 10% of the CPU and 23% of the memory allocated to them, according to the 2025 Kubernetes Cost Benchmark Report.
In this guide, we’ll explore common cost traps and provide strategies for optimizing Kubernetes costs without sacrificing performance or reliability.
Cloud cost optimization vs. Kubernetes cost optimization
Is Kubernetes cost optimization any different from cloud cost optimization? While often discussed together, these two work at different levels of your infrastructure. Conflating them might leave significant savings on the table.
Cloud cost optimization focuses on the broader infrastructure, including selecting the right instance types, leveraging reserved instances or savings plans, right-sizing VMs, eliminating idle resources, and optimizing data transfer costs. It’s about paying less for the compute, storage, and networking you consume from your provider.
Kubernetes cost optimization focuses on how efficiently you use the resources you’ve already provisioned. This means tuning resource requests and limits, improving bin-packing efficiency, scaling workloads appropriately with HPA and VPA, and ensuring pods aren’t overprovisioned. If your containers request twice the CPU they actually use, you’re still wasting money.
On the one hand, overprovisioned Kubernetes workloads force you to run more nodes than necessary, increasing cloud expenses. On the other hand, aggressive cloud rightsizing without understanding your Kubernetes resource patterns can lead to performance issues or scheduling failures.
Now that you understand the difference between these approaches, let’s focus on the Kubernetes layer and examine the cost traps that teams encounter across nodes and workloads.
The complexities of Kubernetes cost optimization
Before containerization, allocating resources and managing costs were simpler. You can easily tag resources to a specific project or team, allowing the FinOps team to determine your typical cost structure and effectively control the budget. By mapping vendor tags and identifying the responsible team, determining the total project cost was straightforward.
However, with the widespread adoption of Kubernetes and other containerization tools, traditional approaches to cost allocation and reporting are becoming increasingly ineffective. Containers are ephemeral, and workloads can move across nodes and clusters, making it challenging to assign costs accurately.
Let’s now look at some of the most common traps that can inflate your Kubernetes bill and how to avoid them.
4 common Kubernetes cost traps
1. Overprovisioning: paying for what you don’t use
Overprovisioning is like renting a mansion when all you need is a two-bedroom apartment. You end up paying for the space you don’t actually use. This occurs when you allocate more resources than necessary, often resulting in substantial bills.
This problem arises when you set high (and fixed) resource requests for your Kubernetes workloads, anticipating peaks that might happen rarely or never. The result is paying for capacity that sits idle most of the time.
2. Improper scaling: more isn’t always better
Autoscaling is one of Kubernetes’ most powerful features, but if you’re not careful, it can lead to unnecessary expenses.
It all starts with the built-in Kubernetes autoscaling mechanisms. The tighter you configure them, the less waste there is and the lower the cost of running your clusters.
While Vertical Pod Autoscaler (VPA) automatically adjusts requests and limits configuration to reduce overhead, Horizontal Pod Autoscaler (HPA) focuses on scaling out to reach the optimal CPU or RAM allocated to an existing instance.
Depending on the scaling policies you set up, you can add too many replicas during peak times, leading to resource waste, or, if not enough, you end up with poor performance.
3. Choosing the wrong cloud instances: size matters
Selecting the right instance types in the cloud is crucial. It’s easy to choose instances that are either too powerful or too weak, leading to inefficiencies.
With containers, you can reschedule workloads across a region, zone, or instance type. A container’s lifespan is just one day, a small glimpse in time compared to the longevity of a virtual machine.
The dynamic nature of the containerized environment adds another layer of complexity to the mix. You may have initially chosen a good set of instances, but are they still suitable for your needs? They might either have more power than needed or throttle your application’s performance.
4. Cost tracking chaos: lost in the numbers
Without the right tools, tracking cloud costs can be a challenging task. You might miss hidden costs that sneak up on you. What usually happens is that you end up with vague invoices and run into difficulties pinpointing where your money is going. This is especially true for costs that are often not very transparent or granular, such as network charges.
What to monitor to avoid cost overruns
To keep your Kubernetes costs under control, it’s essential to monitor key metrics and adjust your operations accordingly. Here’s what we advise you to keep an eye on:
Daily spend and projections: keeping your budget on track
Monitoring your daily cloud spend can save you from budget headaches at the end of the month. To keep cloud expenses in check, you need all the data at hand. This helps you easily extrapolate your daily or weekly expenses into a monthly bill. The daily spend and resource usage report enables you to do just that.
This is an example of the daily spend report from Cast AI, which shows all this data in one place:
Another benefit of the daily cloud costs report is that it lets you identify areas for improvement or outliers in your usage. You can verify how much you’ve spent each day for the last two weeks and double-check that data for any outliers or cost spikes that might lead to cloud waste.
Resource utilization: overprovisioning and cost transparency
Monitoring resource utilization helps you avoid overprovisioning and ensures cost transparency. A good practice is tracking your cost per provisioned CPU and requested CPU. Why should you differentiate between these two?
By comparing the number of requested vs. provisioned CPUs, you can discover a gap and calculate how much you spend per requested CPU. This will make your cost reporting more accurate and boost your understanding of actual resource utilization.
If you’re running a Kubernetes cluster that isn’t optimized for cost, you will see a significant difference between how much you’re provisioning and requesting. You’ll know that you’re spending money on provisioned CPUs and only end up requesting a tiny amount of them.
Let’s illustrate this with an example:
- Your cost per provisioned CPU is $2. Due to a lack of optimization, you waste a significant amount of resources.
- As a result, your actual cost per requested CPU rises to $10.
- This means that you’re running your cluster at a price that is 5 times higher than expected.
Here’s an example of a reporting solution that shows the current overprovisioning percentages as well as a breakdown of average hourly cost across provisioned, requested, and used resources:
Historical cost allocation visibility across multiple levels
Having visibility at various organizational levels helps identify cost drivers and areas for optimization.
Ideally, you should be able to have an overview of costs and resource consumption across multiple levels, starting from the organization and digging deep into specific clusters at the node, deployment, pod, and even container levels.
Let’s say you’re an engineering manager who gets asked by the FinOps team why the cloud bill has overrun again. What cost you more than expected?
This is where historical cost allocation makes a difference. A report like the one below can save you hours, if not days, of investigating where the extra costs originate.
This report shows costs across allocation groups set by Kubernetes users:
By checking last month’s Kubernetes spending dashboard, you can instantly view the cost distribution between namespaces or workloads in terms of dollars spent. Do you see some workloads running that used a lot of money but weren’t doing anything? These are idle workloads – the primary driver of cloud waste.
Automate Kubernetes cost optimization
Manually managing Kubernetes costs can be a nightmare. You need a solid cost analytics process based on reliable data sources to avoid these common traps.
Here’s an example of what it could look like:
- Find a cost visibility tool to track costs in detail (for example, at the workload level)
- Set precise budgets and monitor elements such as traffic costs to understand them better
- Allocate your costs by namespace, pod, deployment, and label
- Analyze the pricing information to predict how much you’ll pay next month
- Keep monitoring costs against your estimates and pinpointing cost or usage anomalies to analyze them further
- Use tools to monitor resource usage and adjust your requests and limits accordingly. Automation solutions can help you resize resources dynamically based on current demand.
Betting on manual strategies for controlling your Kubernetes cloud costs is risky. A manual approach is usually time-consuming, error-prone, and difficult to maintain as you scale.
Luckily, automation solutions can perform fine-tuning of resources for you in real-time.
Deploying an automated Kubernetes cost optimization tool can save you a lot of headaches. Most importantly, it can help you focus on what matters most to your business: delivering quality service to customers.
Even if you’ve been doing fantastic manual optimization, automation produces even better results while demanding less effort and time from teams.
Automated Kubernetes cost optimization in the real world: Bud
The UK-based financial services company Bud enhances financial data to provide actionable insights and comprehensible inputs for LLMs in the financial services sector. Read the full case study →
Bud initially aimed to gain more visibility into the allocation and spending of cloud costs. However, once the team started taking action to optimize these expenses, the focus shifted to preventing the need for manual checks in the future while increasing resource utilization. That led to the idea of using an automation solution.
Using Cast, Bud automatically scales resources up and down, setting workload requests and limits to boost resource utilization and eliminate cloud waste. Our Workload Autoscaler rightsizes workloads, reduces CPU requests, and unlocks new cost savings, leading to a dramatic improvement in CPU and Memory utilization of up to 93%.
The image above shows the impact of Workload Autoscaler on the compute cluster cost.
The image above shows the drop in requested CPU per hour after integrating Cast.
Wrap up
Engineers spend hours, if not days, configuring requests and limits, as well as understanding the complexities of cloud provider services. DevOps teams have become so engrossed in technical implementation details that they lose track of their projects’ financial progress.
Automating DevOps tasks relieves the pressure of repetitive, volume-intensive work, allowing for greater creativity and innovation. It also enables teams to focus their expertise on solving more complex problems, developing new features, and enhancing customer value.
Kubernetes cost optimization
Monitor organization-wide and cluster-level resource spending. Automate resource allocation and scale instantly with zero downtime.
FAQ
What is Kubernetes cost optimization?
Kubernetes cost optimization involves strategies to reduce infrastructure spending while maintaining performance. It matters because organizations typically waste a big part of their Kubernetes cloud spending on unused or underutilized resources.
What are the most effective strategies?
Key strategies include rightsizing resource requests based on actual usage, implementing cluster autoscaling, utilizing Spot Instances for fault-tolerant workloads, and regularly terminating unused resources, such as orphaned volumes or idle load balancers.
How do I get started with Kubernetes cost optimization?
Start by gaining visibility into your current spending using monitoring tools to identify which workloads consume the most resources. Then, prioritize quick wins, such as removing idle resources and adjusting oversized resource requests, before implementing more advanced strategies, like autoscaling policies.
