Your cloud-native stack has full potential, but your monthly cloud bill is frightening. You have heard Kubernetes is efficient, but you find yourself, time and again, wasting money on over-provisioned resources. To make things worse, applications crashing with OOMKilled errors during sudden traffic spikes is a nightmare. This is seemingly a story repeating itself for teams managing dynamic, containerized workloads with a static reserve of CPU and memory.
You have perhaps heard that rightsizing and autoscaling are suggested as solutions to your Kubernetes cost optimization issues. The problem is that these terms are mostly conflated or used in the wrong context, which adds complexity instead of clarity. Countless teams attempt one without the other and grow sullen as their cloud bills show no signs of getting lower.
In this article, we hope to resolve such issues. We will set out to define Kubernetes rightsizing and autoscaling, analyze both of them in a focused manner, and then offer the point that the actual answer is not to pick one or the other, but rather to use them in tandem.
What is Kubernetes Rightsizing?
Think of rightsizing is like selecting the perfect-sized box for a single item. A box that is too big is a waste of space, not to mention the costs associated with shipping air.
In Kubernetes, rightsizing means defining accurate requests and limits for Memory and CPU pods for each workload and analyzing their actual core needs.
Rightsizing eliminates these two major burdens:
Over-provisioning: This is a situation where the amount of memory and CPU allocated for the pod is much higher than what is actually needed. This is a massive cloud spend waste, as you are paying for computing resources that are not being utilized.
Under-provisioning: This is not allocating sufficient resources. This can result in your pods being OOMKilled (Over and buried memory, killed) or experiencing performance issues due to CPU throttling.
This is only to say, when requests and limits of individual pods are set, they get just enough resources to function optimally.
What is Kubernetes Autoscaling?
If rightsizing is picking the right-sized box, autoscaling is the factory that keeps sending empty boxes when demand is high and retrieves them when demand is low, automatically ordering more boxes.
Kubernetes autoscaling is a response to real-time metrics, such as CPU usage, and is concerned with the real-time metrics, such as dynamic increase or decrease in the number of resources. Autoscaling is concerned with the number of pods or nodes in a cluster. It's all about versatility.
There are three main types of autoscalers you should know:
Horizontal Pod Autoscaler: The HPA is an autoscaler that determines the number of replicas in a single deployment based on the percentage of resources allocated and used. Most people out and in scale are pods worth paying for, or whose resources are consumed, on less.
Vertical Pod Autoscaler: The VPA adjusts the CPU and memory requests/limits for individual pods, ensuring there is no guesswork for pod resources. VPA is like a manager who resizes workers so that each has the right capacity for the job. HPA and VPA can work together, but they should not use the same metric for the relationship in order to avoid conflict.
Cluster Autoscaler: The CA manages nodes in your cluster, adding or removing them as needed. Whenever the HPA has to add new pods but is unable to, the CA is responsible for adding a new node. If nodes are over-provisioned, then the CA consolidates the pods and removes the excess nodes.
Rightsizing vs Autoscaling: A Head-to-Head Comparison
Asking whether to use rightsizing or autoscaling is like asking a driver to choose between their engine's fuel efficiency and the gas pedal. One makes the car efficient, and the other makes it go. You need both to have an effective vehicle.
Rightsizing makes your pods efficient. Autoscaling makes your efficient pods elastic.
Here’s a simple breakdown of how they differ:
Feature | Rightsizing | Autoscaling |
Main Goal | Efficiency (Cost per pod) | Elasticity (Handling load) |
What It Adjusts | pod requests & limits (size of the pod) | number of replicas (how many pods) |
Scope | Individual containers/pods | Deployments / replica sets |
Data Used | Historical usage (P50/P90/P99) | Real-time metrics |
The Dangerous Mistake: Autoscaling Without Rightsizing
This underscores the importance of cost optimization in Kubernetes. If you implement autoscaling without first rightsizing your pods, you will simply scale your waste.
Assume your pod has been rightsized such that it is configured with a request of 1 CPU but only ever consumes 0.1 CPU. You have provisioned 10x too much in that case. If you configured your HPA with a threshold of 70% CPU utilization for scaling, it will only kick in when the pod is at 0.7 CPU, a state that they may never achieve.
Even when traffic increases, the HPA will continue to trigger, and at that point in time, you will have a lot of resources in the system that are being wasted. What is even worse is that the HPA has scaled and added pods in bulk. It does not even take into consideration their size and efficiency. You end up with even more unused capacity than before, and your cloud costs skyrocket.
Remember that you will have to right-size first in order to set effective and accurate parameters for the scaling process.
Best Practices: Why You Need Both
The moment you combine rightsizing and autoscaling becomes a point in time where advanced feedback loops can be leveraged with minimal cost:
Rightsize Your Pods: First, you analyze your application's performance and set an optimal efficiency score whenever you come across a request equal to 0.5 CPU.
Set Your Autoscaler: You then configure your HPA with a policy. For example, Aim for the average CPU consumption of all the pods at 70%.
Watch the Magic Happen: When traffic is low, you will only need a handful of pods with a fraction of the requested CPU serving most basic functions. As traffic surges, the remaining, optimally sized pods will be at 70% CPU usage (which corresponds to 0.35 CPU). HPA will detect this and will automatically deploy additional pods to handle the spike. During low traffic periods, HPA will also automatically remove the excess pods.
Most efficient and economical use of Kubernetes. You only deploy what is absolutely necessary, and at the time of absolute necessity.
How to Start: A 3-Step Action Plan
Ready to stop wasting money? Here is a straightforward strategic approach to take.
Step 1: Get Visibility
You can't rightsize what you can't see. A practical approach is to ascertain your actual pod resource usage. Start with Kubernetes resource monitoring like AWS CloudWatch and Grafana. These tools will show your CPU and memory usage over time. This will give you an initial look at which workloads are the most over-provisioned.
Step 2: Start Rightsizing with VPA
Avoiding estimating resource limits and requests is wise. Use the Vertical Pod Autoscaler in “recommendation mode.” This mode analyzes your pod's historical usage and makes suggestions on optimal requests and limits without machine-level control. This is a safe tool to use if you want accurate recommendations on how to do rightsizing. Also available are other Kubernetes tools like Goldilocks, which can be used as a starting point.
Step 3: Implement HPA on Your Rightsized Pods
Your deployments are now efficient after VPA's automated recommended changes. The next step is to define the parameters of elastic resources. Configure HPA to manage the excess resources to the newly optimized deployment. Set a CPU or memory utilization target of your choice (for example, 70-80%) and Kubernetes will manage the rest.
Conclusion
The debate over Kubernetes rightsizing and autoscaling is a misconception. Both are key components in any Kubernetes resource management plan. Rightsizing your pods reduces the resource utilization of each individual pod to the most efficient level. With autoscaling, your pods sit in an efficient cluster. Happy CFO, happy life.
Doing this will make you understand the role of rightsizing and autoscaling in Kubernetes. Instead of “vs”, broaden your perspective to “and” to reap the benefits of these features.
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