Running applications on Kubernetes can be expensive if not managed properly. As more businesses adopt Kubernetes for their cloud infrastructure, handling costs becomes increasingly significant and more complex. In this guide, we’ll explore some common cost traps and provide strategies for Kubernetes cost optimization.
The Complexities of Kubernetes Cost Optimization
Understanding the unique challenges Kubernetes poses is essential for optimizing and reducing cloud costs.
Before containerization, allocating resources and managing costs were simpler. You could easily tag resources to a specific project or team, allowing the FinOps team to determine your typical cost structure and control the budget effectively. By mapping vendor tags and identifying the team responsible, figuring out the total project cost was straightforward.
However, with the widespread adoption of Kubernetes and other containerization tools, traditional approaches to cost allocation and reporting no longer work. Containers are ephemeral, and workloads can move across nodes and clusters, making it challenging to assign costs accurately.
4 Common Kubernetes Cost Traps
Managing Kubernetes can sometimes feel like trying to tame a wild beast. It’s powerful, but your costs can spiral out of control if you don’t keep an eye on it. Let’s look at some of the most common traps that can inflate your Kubernetes bill and how to avoid them.
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 happens when you allocate more resources than necessary, often leading to hefty bills.
This problem arises when you set high (and fixed) resource requests for your Kubernetes workloads, anticipating peaks that might rarely or never happen. The result is paying for capacity that sits idle most of the time.
Setting resource requests and limits can feel like walking a tightrope. Too much, and you waste money. Too little, and your apps might crash.
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 lower overhead, Horizontal Pod Autoscaler (HPA) focuses on scaling out to reach the optimum 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 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 how long a virtual machine can last. More and more people run functions and cron jobs on Kubernetes, and their lifetimes range from seconds to minutes.
The dynamic nature of the containerized environment adds another layer of complexity to the mix. You may have chosen a good set of instances initially, but are they still proper 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
Tracking cloud costs without proper tools is like finding a needle in a haystack. 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 some costs that are usually not very transparent or granular, like network charges.
What to Monitor to Avoid Cost Overruns
To keep your Kubernetes costs under control, you need 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 the lack of optimization, you waste a lot of resources.
As a result, your actual cost per requested CPU rises to $10.
This means that you’re running your cluster for a price that is 5x higher than expected.
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 a cost and resource consumption overview across multiple levels, starting from the organization and digging deep into a specific cluster at node, deployment, pods, 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, on investigating where the extra costs come from.
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 prime driver of cloud waste.
Automate Kubernetes Cost Optimization
Manually managing Kubernetes costs can be a nightmare. To avoid falling into one of these common traps, you need a solid cost analytics process based on reliable data sources.
Here’s an example to show you what that process 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. These strategies are usually time-consuming, error-prone, and difficult to maintain.
Luckily, automation solutions can do resource fine-tuning 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 Optimization in Real Life: NielsenIQ
Azure offers a wide range of instance types and sizes. Given the enormous size of Azure’s portfolio, many teams struggle to select the correct server for a Kubernetes application.
By using an automation solution, NielsenIQ teams no longer have to worry about selecting the best virtual machine to meet their cost and performance goals, improving engineers’ productivity and well-being. As a result, NielsenIQ generated up to 80% cost savings on deployments.
Wrap Up
Before the introduction of DevOps automation technologies, Kubernetes infrastructure management required precise manual input for every decision about instances, availability zones, families, and sizes.
Engineers spend hours, if not days, configuring requests and restrictions and 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 lets teams focus their expertise on solving more complex problems, developing new features, and increasing customer value.If you’re curious about how automated Kubernetes cost optimization works in practice, check out how we used our technology to reduce our Amazon EKS costs by 66% for one of our clusters: 8 Tips for Amazon EKS Cost Optimization.
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