Complete Guide on Kubernetes HPA (Horizontal Pod Autoscaler)

Optimizing Kubernetes Horizontal Pod Autoscaler (HPA) requires a strong understanding of scaling metrics, resource allocation, and application behavior. Choosing the right scaling parameters can significantly impact application performance and resource efficiency. By configuring accurate resource requests, setting min/max replica limits, and integrating custom metrics, you can ensure optimal scaling without over-provisioning. HPA automates scaling, adjusting resources to maintain peak performance while controlling costs, ensuring workloads always align with demand.

Managing a Kubernetes cluster and scaling it to handle changing traffic demands often becomes a delicate balancing act. Teams frequently face challenges maintaining performance while controlling costs and allocating resources effectively, especially when autoscaling configurations are inefficient or misconfigured.

Industry data from a 2024 report shows that nearly 83% of container spending across organizations is tied to idle resources. This shows how quickly misconfigured autoscaling and over-provisioning can inflate cloud bills without delivering real value.

That’s why understanding how the Horizontal Pod Autoscaler (HPA) works is so important. Knowing when to rely on custom metrics and how to define sensible scaling limits can make a measurable difference.

In this blog, you’ll explore practical HPA best practices that help you scale more intelligently, maintain consistent performance, and avoid the common mistakes that lead to wasted resources.

What is Kubernetes Horizontal Pod Autoscaler (HPA)?

The Horizontal Pod Autoscaler (HPA) in Kubernetes is a built-in controller that automatically adjusts the number of pod replicas in a deployment, replica set, or stateful set based on observed metrics such as CPU utilization, memory usage, or custom metrics.

The value of HPA lies in its ability to dynamically scale applications in response to real-time demand, ensuring both resource efficiency and application performance without manual intervention. Here’s why it matters:

• Cost Optimization: HPA automatically scales the number of pods based on real-time demand, reducing unnecessary resource consumption. You can avoid over-provisioning resources, which helps lower cloud costs in environments where traffic fluctuates.
• Performance and Reliability: HPA ensures application performance stays consistent as load varies. Scaling pods up when resource usage increases prevents resource starvation and maintains responsiveness during peak times.
• Scalability Without Complexity: In large Kubernetes environments, manually scaling every workload variation quickly becomes impractical. HPA simplifies this by automating pod scaling, making it easier for you to scale applications efficiently without adding operational complexity.
• SLA Compliance and Application Health: HPA maintains your application’s performance by adjusting resource allocation dynamically, ensuring it continues to meet performance targets even during traffic surges. If you're responsible for uptime and customer experience, HPA reduces the risk of downtime and performance degradation.

Once you understand the basics of Kubernetes HPA, it’s useful to see how it compares with VPA and KEDA to understand their key differences and use cases.

HPA vs VPA vs KEDA: What are the Key Differences?

Understanding the differences between the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Kubernetes Event-Driven Autoscaler (KEDA) is essential for making informed scaling decisions in your Kubernetes environment.

Each tool serves a different purpose, and knowing when to use them can help you optimize resource management, performance, and cost. Here’s a clear breakdown of how they differ:

1. Horizontal Pod Autoscaler (HPA)

HPA scales the number of pod replicas in a Kubernetes deployment based on metrics like CPU utilization, memory usage, or custom application metrics such as request count or latency.

How It Works:

• HPA continuously watches your chosen metrics and adjusts the replica count to meet the thresholds you define.
• It fetches data from the metrics server or custom metrics adapters like Prometheus.
• Scaling decisions are made by comparing the average metric value across pods. For example, if CPU utilization exceeds 80 percent, HPA automatically scales up your application.

Key Use Cases:

• Stateless applications: HPA works best for stateless workloads like APIs, microservices, and web applications, where traffic can fluctuate, and additional replicas can be added easily.
• Elastic Scaling: It helps your application scale smoothly in response to variable load patterns without needing manual intervention.

2. Vertical Pod Autoscaler (VPA)

VPA adjusts the CPU and memory requests and limits of individual pods based on real usage. Instead of adding more pods as HPA does, VPA increases or decreases the resource allocation for each pod.

How It Works:

• VPA monitors resource consumption and suggests or applies updated CPU and memory values.
• Depending on how you configure it, VPA can provide recommendations or automatically enforce new settings.
• When VPA updates resource limits, it may need to restart pods so they can be recreated with the updated configuration.

Key Use Cases:

• Stateful applications: VPA is ideal for workloads like databases or caching systems, where scaling out isn’t always possible or necessary. You typically keep a fixed number of pods but need more (or fewer) resources per pod.
• Resource Optimization: VPA prevents over-provisioning by ensuring that CPU and memory values reflect actual usage, which helps manage costs and improve resource efficiency.

3. Kubernetes Event-Driven Autoscaler (KEDA)

KEDA focuses on event-driven scaling. Instead of looking at CPU or memory usage, it scales pods based on external event sources like Kafka topics, message queues, or HTTP request rates.

How It Works:

• KEDA listens to external event triggers, for example, queue length in RabbitMQ, Kafka lag, or the number of messages in an SQS queue.
• When the event threshold is exceeded, KEDA scales your workloads up or down.
• It can also work alongside HPA, allowing you to combine resource-based and event-based scaling.

Key Use Cases:

• Event-driven applications: KEDA is perfect for workloads that depend on queue activity, message streams, or asynchronous tasks such as stream processing or background job workers.
• Hybrid scaling: You can integrate KEDA with HPA so that your application scales not just based on CPU or memory, but also based on external events, making your scaling strategy much more responsive and intelligent.

Once you are clear about the difference, it’s important to understand the limitations of HPA and where it may not be the ideal solution.

Suggested Read: Kubernetes Cluster Scaling Challenges

Limitations of Horizontal Pod Autoscaler

While the Horizontal Pod Autoscaler (HPA) is a powerful tool for managing Kubernetes scalability, it still has several limitations you should be aware of when planning dynamic scaling in production environments. Below are those limitations:

After understanding the limitations of the Horizontal Pod Autoscaler, you can move on to setting it up in practice.

Also Read: Kubernetes Autoscaling in 2025: Best Practices, Tools, and Optimization Strategies

How to Set Up Kubernetes HPA?

Setting up the Horizontal Pod Autoscaler (HPA) in Kubernetes requires careful configuration to make sure your applications scale smoothly and respond to real-time demand.

Here’s a step-by-step guide that walks you through exactly what you need to configure HPA effectively in a production environment.

1. Ensure Prerequisites Are Met

Before you set up HPA, double-check that the following foundational components are in place:

• Metrics Server: HPA relies on the Metrics Server to collect CPU and memory metrics from pods and nodes. Make sure it’s installed and running by checking:

kubectl top nodes

kubectl top pods

• Resource Requests and Limits: Define the CPU and memory requests and limits for every pod in your deployment. HPA depends on these values to understand how each pod is using resources and when to trigger scaling.

2. Define the HPA Object

Once the prerequisites are covered, you can define your HPA configuration and specify the target metric and scaling behavior.

Create an HPA YAML file (for example, hpa.yaml):

apiVersion: autoscaling/v2

kind: HorizontalPodAutoscaler

metadata:

  name: my-web-app-hpa

  namespace: default

spec:

  scaleTargetRef:

    apiVersion: apps/v1

    kind: Deployment

    name: my-web-app

  minReplicas: 3

  maxReplicas: 10

  metrics:

    - type: Resource

      resource:

        name: cpu

        target:

          type: Utilization

          averageUtilization: 80

Here’s what each field controls:

• scaleTargetRef: Points to the resource HPA will scale (in this case, your Deployment).
• minReplicas: Ensures the application always has at least 3 pods running.
• maxReplicas: Limits scaling to a maximum of 10 pods.
• metrics: Defines the metric HPA should use for scaling. Here it’s CPU utilization with an 80 percent target.

Apply the HPA configuration:

kubectl apply -f hpa.yaml

3. Verify HPA Setup

To make sure everything is working correctly, check the current status of your HPA:

kubectl get hpa my-web-app-hpa

You should see the number of current replicas along with observed CPU utilization. This confirms that HPA is active and watching metrics.

4. Monitor Scaling Behavior

HPA evaluates metrics at regular intervals, usually every 30 seconds. To see how it handles scaling decisions in real-time, run:

kubectl describe hpa my-web-app-hpa

In the Metrics section, you’ll find information on whether the HPA is scaling up or down based on current usage.

5. Fine-Tune Scaling Behavior

To avoid aggressive or inconsistent scaling, it’s important to tweak a few settings:

• Cooldown Period: Prevents rapid scaling changes in short intervals. For example:

behavior:

  scaleUp:

    stabilizationWindowSeconds: 300

  scaleDown:

    stabilizationWindowSeconds: 300

• Custom Metrics:  If CPU or memory aren’t accurate indicators of your workload’s demand, use custom metrics instead. Integrate Prometheus or another metrics adapter to expose metrics such as queue depth, request rate, and application latency.

6. Set Appropriate Resource Requests and Limits

Since HPA scales based on utilization of the requested resources, your pod specs need correct CPU and memory values. Misconfigured requests and limits often lead to poor scaling behavior.

Example configuration:

resources:

  requests:

    cpu: "500m"

    memory: "512Mi"

  limits:

    cpu: "1"

    memory: "1Gi"

Make sure these values align with how your application actually behaves under load.

Once HPA is set up in your cluster, it's helpful to know how Kubernetes HPA is calculated.

How is Kubernetes HPA Calculated?

Kubernetes HPA determines the required number of Pod replicas by comparing real-time resource usage against the target utilization specified in the HPA configuration.

It retrieves metrics from the configured metrics source, evaluates them against Pod resource requests, and adjusts the workload by scaling Pods up or down as needed.

Core formula = desiredReplicas = ceil(

currentReplicas × ( currentAverageUsage / targetUsage )

)

Once you know how HPA is calculated, following best practices ensures it runs efficiently and scales your workloads effectively.

6 Best Practices for Using Kubernetes HPA

For you, using Horizontal Pod Autoscaler (HPA) effectively requires more than just enabling it in your Kubernetes cluster. To fully leverage HPA while maintaining performance, cost efficiency, and stability, here are the key best practices to follow:

1. Define Appropriate Resource Requests and Limits

HPA depends on accurate resource usage metrics like CPU and memory to make scaling decisions. Resource requests and limits define the minimum and maximum resources a pod can use. Make sure these values reflect realistic application needs to avoid inefficient scaling.

Tip: Regularly review and update requests and limits based on actual workload patterns to prevent over-provisioning or resource starvation.

2. Use Custom Metrics for More Accurate Scaling

CPU and memory don’t always reflect the true workload, especially for event-driven or stateful applications. If your workloads are event-based, integrating KEDA allows scaling based on triggers like message queue length or external event sources.

Tip: Only track metrics that directly impact performance to avoid unnecessary scaling fluctuations.

3. Set Min/Max Replicas Thoughtfully

Your minReplicas and maxReplicas Settings determine how far HPA can scale.

• minReplicas: Guarantees a minimum number of pods even during low traffic, preventing performance dips.
• maxReplicas: Protects your cluster from scaling too aggressively and wasting resources.

To choose the right values, assess your traffic patterns and cluster capacity. Underestimating limits can lead to slow response times, while overestimating may inflate costs.

Tip: Analyze traffic patterns and cluster capacity to choose realistic min/max values.

4. Use Stabilization and Cooldown Periods

Without stabilization or cooldown settings, HPA can react too quickly to short-lived spikes, causing scaling “flapping.” These settings ensure smoother scaling behavior and reduce sudden performance swings.

Tip: Configure cooldown periods to match the duration of typical workload spikes, reducing unnecessary pod churn.

5. Handle Stateful Workloads Carefully

HPA works best for stateless applications. If you’re using it for stateful workloads like databases, keep these points in mind:

• Use StatefulSets to maintain stable identities.
• Consider Vertical Pod Autoscaler (VPA) for adjusting CPU/memory instead of scaling horizontally.
• Combine HPA with custom logic if the workload has unpredictable behavior.

Tip: Monitor stateful pods closely to ensure scaling doesn’t disrupt consistency or storage performance.

6. Test HPA in a Staging Environment

Before rolling changes into production, test HPA behavior under realistic load in staging. This helps you:

• Validate that scaling triggers work correctly
• Adjust thresholds and resource settings
• Identify delays or misconfigurations early

Tip: Simulate peak traffic scenarios to ensure scaling responds correctly without overloading nodes.

Must Read: Autonomous Optimization for Kubernetes Applications and Clusters

How Sedai Delivers Autonomous Optimization for Kubernetes HPA?

Many tools claim to optimize Kubernetes clusters, but most still depend on basic Horizontal Pod Autoscaler (HPA) configurations driven by fixed CPU or memory thresholds.

These static approaches don’t reflect how modern workloads behave in real time, often resulting in inefficient resource allocation, inconsistent performance, and unexpected cloud costs.

Sedai takes a fundamentally different approach with true autonomous optimization. Its advanced machine learning framework continuously learns from live workload behavior across your Kubernetes clusters and dynamically adjusts HPA settings in real time.

By proactively managing scaling decisions, Sedai ensures your Kubernetes environment scales in line with actual demand, maintaining consistent performance while avoiding unnecessary overprovisioning.

What Sedai Offers:

• Dynamic pod-level rightsizing (CPU and memory): Sedai continuously analyzes real workload usage and dynamically adjusts pod requests and limits to avoid both over- and under-provisioning. This proactive rightsizing reduces cloud costs by 30% or more while improving application performance.
• Intelligent scaling decisions: Powered by machine learning, Sedai adjusts pod replicas and scaling thresholds using real demand patterns instead of static configurations. This results in fewer failed interactions, as scaling actions are driven by actual workload behavior rather than predefined limits.
• Continuous performance monitoring and adjustment: Sedai constantly monitors cluster health and automatically fine-tunes HPA parameters to optimize resource allocation. This reduces the time teams spend managing and troubleshooting scaling issues, increasing engineering productivity by up to 6x.
• Full-stack performance and cost optimization: Sedai continuously tunes compute, storage, and network resources to align with your specific HPA requirements. This ensures autoscaling remains cost-efficient without compromising performance.
• Autonomous remediation: Sedai detects early signs of resource pressure, pod instability, or performance degradation and resolves issues before they impact workloads. This proactive remediation minimizes downtime and removes the need for manual intervention by engineering teams.
• SLO-driven scaling: Sedai aligns scaling decisions with your application’s Service Level Objectives (SLOs), ensuring consistent performance during traffic spikes and low-demand periods while maintaining reliability and responsiveness.

With Sedai, Kubernetes clusters scale efficiently and autonomously, responding faster to workload changes and keeping resources aligned with real demand. By removing guesswork from scaling decisions, Sedai helps clusters operate at peak efficiency while significantly reducing unnecessary cloud spend.

If you’re looking to improve HPA autoscaling with Sedai, use our ROI calculator to estimate potential savings from eliminating inefficiencies, improving performance, and reducing manual tuning.

Final Thoughts

Optimizing the Kubernetes HPA is about continuously refining your scaling strategy to match your application’s needs. One key area often overlooked is predictive scaling. By analyzing historical traffic patterns and using predictive models, you can identify future load, scale pods in advance, and prevent performance bottlenecks before they occur.

This proactive approach is where platforms like Sedai really shine. By automatically analyzing workload behavior and predicting resource needs in real time, Sedai keeps your Kubernetes clusters aligned with demand, preventing scaling issues before they arise.

Achieve complete insight into your Kubernetes HPA configuration and start improving efficiency and reducing expenses right away.

FAQs

Q1. What are the limitations of Kubernetes Horizontal Pod Autoscaler (HPA)?

A1. HPA scales pods based on CPU and memory utilization, but it doesn’t consider pod placement, node capacity, or disk/network I/O constraints. Pairing HPA with the Cluster Autoscaler ensures enough node resources are available to support scaled pods.

Q2. How can I fine-tune HPA to improve application responsiveness?

A2. Adjust metrics thresholds, cooldown periods, and stabilization windows to avoid rapid scaling fluctuations. Using custom metrics like request latency or queue length lets HPA scale based on real application demand, improving responsiveness under dynamic traffic.

Q3. Can HPA be used with stateful applications?

A3. Yes, when combined with StatefulSets. For more precise resource allocation in stateful workloads, consider using Vertical Pod Autoscaler (VPA) to adjust CPU and memory per pod instead of scaling out pods, which may not always be feasible for stateful applications.

Q4. How does Kubernetes HPA interact with custom application metrics?

A4. HPA can scale based on custom metrics. By monitoring application-specific metrics such as request count or error rates, HPA makes decisions that reflect real demand rather than just CPU or memory usage.

Q5. What is the role of the Cluster Autoscaler when using HPA?

A5. While HPA scales pods at the workload level, the Cluster Autoscaler adjusts the number of nodes in the cluster. This ensures the cluster always has sufficient capacity to accommodate scaled pods.