Provisioning and scaling are two administrative operations that can be automated with Kubernetes. Users can design automated processes that will save time, respond swiftly to requests, and save cost by scaling back when resources are not needed rather than statically allocating resources. By giving only the required resources, it can also be utilized in conjunction with the cluster autoscaler.

For enterprises where costs are determined only by the resources used, the Kubernetes autoscaling feature is advantageous. The capacity to scale up and down allows organizations to scale up on demand and release them after use. In addition, dynamic resource management will enable businesses to automate, scale, and manage the state of applications and pods, clusters, and entire deployments, going beyond merely managing individual containers.

The auto-scaling method consists of two layers:

  • Pod-based scaling is supported by the Horizontal Pod Autoscaler (HPA) and the Vertical Pod Autoscaler (VPA).
  • Node-based scaling that is supported by the cluster autoscaler.

Scaling Pods

Pods are groups of containers that are controlled as a unit. They incorporate one or more containerized service. A pod can only contain a single application or a single microservice. Pod scaling is one of the easiest ways to scale a deployment. Organizations scale a pod when they require extra resources for a specific pod instance or when they need to generate more models of a pod to distribute the load over several different container instances.

Pods can be scaled with current resources. For example, if a node has 8GB of spare RAM that is not being used, another pod can be added to that node. When the load on a cluster running a set of pods gets too high, the entire cluster may be scaled up.

To make things more transparent, let’s look at the Kubernetes Autoscaling ways.

  • Horizontal Pod Autoscaler (HPA)
  • Vertical Pod Autoscaler (VPA)
  • Cluster Autoscaler (CA)

Horizontal Pod Autoscaler

The HPA adjusts the number of pods available in a cluster to meet an application’s current computational workload needs. It calculates the number of pods required depending on the metrics specified by the client and creates or deletes pods based on the thresholds. In most situations, these metrics are CPU and RAM utilization, although custom metrics can also be specified. The HPA monitors the CPU and memory measurements generated by the metrics server deployed in the Kubernetes cluster continually.

If one of the parameters specified is reached, the deployment controller’s number of pod replicas will be updated. The deployment controller will scale the number of pods up or down until the number of replicas matches the required number, then the number of updated replicas. Suppose the client wishes to utilize custom metrics to establish the rules for how the HPA manages to scale. In that case, the cluster must be connected to a time-series database containing the metrics desired by the company.

Vertical Pod Autoscaler

The Vertical Pod Autoscaler (VPA), unlike the HPA, automatically changes the CPU and Memory parameters for the pods. The VPA recreates the pods with the appropriate CPU and Memory properties. This will free up CPU and Memory for other pods, allowing the Kubernetes cluster to be used more efficiently. In addition, the Kubernetes worker nodes are used effectively since the pod only utilizes what they require. If the user enables it, the Vertical Pod Autoscaler (VPA) may provide Memory/CPU demands and restrictions and automatically update them. This will save engineers time when doing Performance/Benchmark testing to establish the correct numbers for CPU and memory demands.

Cluster Autoscaler

The cluster automatically adjusts the number of Kubernetes nodes to meet the client’s needs. For example, CA adds extra nodes when the number of pending or un-schedulable pods rises, suggesting a shortage of resources in the cluster. Conversely, it can also downsize nodes that have been underutilized over an extended period of time.

Kubernetes VPA Vs. HPA

The main distinction between VPA and HPA is in how they scale. HPA scales by adding or deleting pods, resulting in horizontal capacity scaling. VPA, on the other hand, grows vertically by adding or lowering CPU and memory capabilities within existing pod containers. Here is a detailed comparison between Kubernetes VPA and HPA.

The main distinction between VPA and HPA is in how they scale. HPA scales by adding or deleting pods, resulting in horizontal capacity scaling. VPA, on the other hand, grows vertically by adding or lowering CPU and memory capabilities within existing pod containers. Here is a detailed comparison between Kubernetes VPA and HPA.

More resources Add more pods Increase CPU or memory resources of existing pod containers
Less resources Remove pods Decrease CPU or memory resources of existing pod containers


Auto Scaling refers to the process of scaling your application horizontally or vertically based on indicators such as CPU or memory utilization without the need for human involvement. Businesses want auto-scaling to adapt to increased traffic as rapidly as possible, to prevent any business disruptions. Organizations can also save money by running as few instances as possible with as few resources as possible. To achieve autoscaling, use the Vertical Pod Autoscaler (VPA) and the Horizontal Pod Autoscaler (HPA).


From the ground up, our APPZ SRE platform and the premade picture templates we call APPZ stacks are cloud-agnostic. Furthermore, these templates have been meticulously selected, hardened, tested, and protected with all the standards required to achieve your multi-cloud objectives as quickly as possible and at the lowest possible cost of ownership.

Cloud Control Solutions (CCS) offers clients Node and Pod Autoscaling using APPZ. The two key components are node and pod autoscaling, and the main aim of auto-scaling is to allow Kubernetes to raise and reduce the number of worker nodes. For example, assume two nodes are operating, and there is a lot of CPU or memory utilization, which can lead to performance deterioration. In this case, it will also increase the number of nodes and pods to handle the demand and resource use. This is similar to resource management in several ways. 

Here’s an example of how APPZ helps with autoscaling; A working company will have several requests they must manage simultaneously, i.e. the organization must react to the request in less than one second. So autoscaling is used to attain the desired node and pod size.


To summarize the benefits, node autoscaling increases the number of worker nodes in a cluster, whereas pod autoscaling modifies the number of replicas. One of the primary benefits is operational efficiency, which means that since everything is done automatically, you may add and decrease requests on demand, resulting in zero resource waste, zero down time. Another significant benefit is cost savings; cloud costs depend on the number of nodes and other factors. Therefore, when demand rises, so do the prices, and vice versa.

Another point is improved availability; autoscaling guarantees enough pods and nodes to handle incoming demand. It also boosts performance, allowing the application to manage massive traffic levels without degrading performance. As a result, the cluster can scale up to meet rising demand while maintaining optimal application performance by increasing the number of pods and nodes. 


With the capacity to auto-scale pods and clusters, Kubernetes delivers on the cloud’s promise of built-in intelligence to monitor system loads and automatically scale up or down to meet demand at any given time.

When developing an architecture based on container-based microservices and Kubernetes, it is critical to consider the big picture rather than just portions and accessible capabilities in isolation. For example, one of the problems of maintaining a Kubernetes deployment is getting all of the ecosystem’s components to operate together correctly, especially when running hybrid systems such as multi-cloud and on-premises installations. Overall, autoscaling is a critical feature of Kubernetes that allows organizations to perform their applications efficiently, economically, effectively, and dependably.

CCS APPZ makes cluster setup, management, and monitoring more accessible and automated. Autoscaling is readily established from the control plane and works across clouds and on-premises. Discover how CLOUD CONTROL SOLUTIONS can help you with your Kubernetes implementation using APPZ.

About the Author

Node and Pod Autoscaling with AppZ

Dr. Anil Kumar

VP Engineering, Cloud Control
Founder | Architect | Consultant | Mentor | Advisor | Faculty

Solution Architect and IT Consultant with more than 25 years of IT Experience. Served in various roles with both national and international institutions. Expertise in working with both legacy and advanced technology stacks and business domains.