What Is Horizontal Pod Autoscaling? How HPA Works, Its Benefits, and How to Implement It in Kubernetes

In Kubernetes environments, the ability to dynamically scale resources is essential for maintaining application performance while optimizing infrastructure costs. One of the most powerful features that enables this capability is Horizontal Pod Autoscaling (HPA).

HPA automatically adjusts the number of Pods based on real-time resource utilization, allowing applications to respond seamlessly to fluctuating workloads without manual intervention.

In this guide, Viettel IDC explains what Horizontal Pod Autoscaling is, how it works, its key benefits, implementation methods, and best practices for deploying HPA in Kubernetes.

What Is Horizontal Pod Autoscaling (HPA)?

Horizontal Pod Autoscaling (HPA) is a Kubernetes feature that automatically scales the number of Pods in a Deployment, ReplicaSet, or StatefulSet based on observed resource metrics such as:

- CPU utilization

- Memory (RAM) utilization

- Custom metrics (e.g., request rate, queue length, latency)

- External metrics from monitoring systems

Instead of manually monitoring workloads and adjusting replica counts, HPA continuously evaluates application demand and automatically increases or decreases the number of Pods to maintain optimal performance while minimizing resource waste.

Example

Suppose the target CPU utilization for an application is 70%.

- If the average CPU usage across Pods exceeds 70%, HPA automatically creates additional Pods to distribute the workload.

- When traffic decreases and CPU utilization falls below the target threshold, HPA removes unnecessary Pods to reduce infrastructure costs.

How Horizontal Pod Autoscaling Works

HPA Architecture and Working Principle

Horizontal Pod Autoscaling continuously monitors resource metrics collected by the Metrics Server running inside the Kubernetes cluster.

By default, Kubernetes evaluates these metrics approximately every 15 seconds.

During each evaluation cycle, HPA compares the application's current resource consumption against the desired target values.

If utilization exceeds or falls below the configured threshold, Kubernetes recalculates the required number of Pod replicas and adjusts the workload accordingly.

Horizontal Scaling Process

The HPA scaling process typically consists of four steps:

1. Collect Metrics

HPA retrieves CPU and memory metrics from running Pods through the Kubernetes Metrics Server.

2. Compare with the Target Threshold

The system compares the average utilization against the configured target.

For example:

- Current average CPU utilization: 90%

- Target CPU utilization: 70%

Since actual utilization exceeds the target, Kubernetes determines that additional Pods are required.

3. Calculate the Desired Number of Pods

HPA estimates the required number of replicas using the following calculation:

Desired Replicas = Current Replicas × (Current Metric ÷ Target Metric)

Example:

- Current Pods: 3

- Current CPU utilization: 90%

- Target CPU utilization: 70%

Calculation:

3 × (90 ÷ 70) ≈ 4 Pods

HPA therefore scales the deployment from three Pods to four.

4. Automatically Scale the Workload

Kubernetes creates or removes Pod replicas based on the calculated result, ensuring the application maintains consistent performance while efficiently utilizing infrastructure resources.

Benefits of Horizontal Pod Autoscaling

Automatically Scale Based on Real Workloads

Unlike static resource allocation, HPA enables applications to expand or shrink according to actual demand.

This is especially valuable for workloads with highly variable traffic, including:

- E-commerce platforms

- Video streaming services

- Public APIs

- SaaS applications

- Microservices

Optimize Infrastructure Costs

Because HPA automatically removes unnecessary Pods during periods of low demand, organizations avoid paying for idle compute resources.

This reduces consumption of:

- CPU

- Memory

- Worker nodes

- Cloud infrastructure

As a result, businesses pay only for the resources they actually need.

Improve Application Performance and Availability

When workloads increase unexpectedly, HPA performs horizontal scaling (scale-out) to maintain application responsiveness.

Benefits include:

- Lower response times

- Reduced risk of bottlenecks

- Higher availability

- Improved user experience

- Better resilience during traffic spikes

Simplify Operations and DevOps Automation

HPA integrates seamlessly with modern DevOps ecosystems.

It works alongside technologies such as:

- CI/CD pipelines

- Prometheus

- Grafana

- Kubernetes monitoring platforms

- GitOps workflows

This enables fully automated infrastructure management with minimal operational overhead.

How to Configure Horizontal Pod Autoscaling in Kubernetes

Prerequisites

Before deploying HPA, ensure your Kubernetes cluster satisfies several requirements.

Install Metrics Server

The Kubernetes Metrics Server collects CPU and memory utilization for every Pod.

Without it, HPA cannot retrieve the metrics required for automatic scaling.

Define Resource Requests and Limits

Each Pod or Deployment should specify:

- CPU requests

- CPU limits

- Memory requests

- Memory limits

These resource definitions allow Kubernetes to accurately calculate utilization percentages and make intelligent scaling decisions.

Create an HPA Using kubectl

Once your environment is properly configured, creating an HPA requires only a single command.

Example:

kubectl autoscale deployment my-app --cpu-percent=70 --min=2 --max=10

This command configures Kubernetes to:

- Monitor CPU utilization for the my-app Deployment.

- Maintain an average CPU utilization target of 70%.

- Keep at least 2 Pods running.

- Scale up to a maximum of 10 Pods when necessary.

To verify the autoscaler:

kubectl get hpa

To view detailed information:

kubectl describe hpa my-app

Configure HPA Using a YAML Manifest

For production environments and Infrastructure as Code (IaC) workflows, defining HPA through a YAML configuration file is the preferred approach.

A typical Horizontal Pod Autoscaler manifest includes:

- API Version: autoscaling/v2

- Kind: HorizontalPodAutoscaler

- HPA name (for example, my-app-hpa)

- Target Deployment (my-app)

- Minimum replicas: 2

- Maximum replicas: 10

- Target CPU utilization: 70%

After creating the configuration file (for example, my-app-hpa.yaml), deploy it using:

kubectl apply -f my-app-hpa.yaml

Kubernetes automatically provisions the Horizontal Pod Autoscaler according to the specified configuration.

Monitoring Horizontal Pod Autoscaling

After deployment, monitoring HPA behavior is essential to ensure optimal scaling performance.

Several tools can be used, including:

- Prometheus

- Grafana

- Kubernetes CLI

For example:

kubectl describe hpa

 

This command displays information such as:

- Current CPU utilization

- Target utilization

- Current replica count

- Desired replica count

- Scaling events

For more comprehensive visibility, Grafana dashboards provide real-time visualizations of HPA activity and cluster resource utilization.

Through continuous monitoring, organizations can fine-tune scaling thresholds and optimize application performance.

HPA vs. Vertical Pod Autoscaling (VPA)

 

Real-World Enterprise Use Cases

Many organizations rely on HPA to maintain application performance during peak traffic while minimizing infrastructure costs during off-peak periods.

E-commerce Platforms

Automatically scale application Pods during:

- Flash sales

- Promotional campaigns

- Holiday shopping seasons

FinTech Applications

Maintain stable transaction processing performance during periods of heavy financial activity.

SaaS Platforms

Automatically expand backend services when user logins or API requests increase unexpectedly.

By dynamically adjusting compute capacity, HPA enables organizations to deliver consistent service quality while optimizing cloud spending.

Common Challenges of Using HPA

Metrics Collection Latency

HPA depends on metrics collected by the Kubernetes Metrics Server.

Because metrics are updated at regular intervals, scaling decisions may lag behind sudden traffic spikes by several seconds.

For highly dynamic workloads, this delay should be considered during capacity planning.

Improper Scaling Thresholds

Poorly configured CPU or memory targets can result in excessive scaling activity.

For example:

- Thresholds set too low may trigger frequent scaling events.

- Thresholds set too high may delay scaling and reduce application responsiveness.

Careful performance testing and threshold tuning are essential for achieving stable autoscaling behavior.

Dependency on Metrics Infrastructure

HPA cannot function correctly if:

- Metrics Server is unavailable.

- Metrics collection fails.

- External metrics adapters are misconfigured.

Using custom metrics—such as request rate or queue depth—typically requires additional integrations with monitoring platforms like Prometheus Adapter.

Conclusion

Horizontal Pod Autoscaling (HPA) is one of Kubernetes' most powerful capabilities, enabling applications to automatically scale in response to real-time demand while maximizing performance and minimizing operational costs.

Thanks to its flexibility, automation capabilities, and seamless integration with cloud-native technologies, HPA has become an essential component of modern Kubernetes and microservices architectures.

If your organization is looking for a scalable, enterprise-grade Kubernetes platform, explore Viettel Open Kubernetes Service from Viettel IDC. Built on a robust cloud infrastructure, it provides reliable container orchestration, intelligent resource scaling, and the flexibility required for modern cloud-native applications.

Learn more about Viettel Open Kubernetes Service here:

https://viettelidc.com.vn/viettel-kubernetes-service

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