What is Kubernetes HPA?: How to Use Horizontal Pod Autoscaling

The Horizontal Pod Autoscaler is one of the most practical tools for improving application responsiveness and resource efficiency. Kubernetes HPA automatically adjusts the number of pod replicas in a deployment based on real-time metrics like CPU or memory usage. Applications can then scale up or down, as needed, without manual intervention. 

Although autoscaling may appear straightforward on the surface, it requires enterprises to implement and maintain a tightly coordinated system. Accurate telemetry, automated node provisioning and consistent policy enforcement must work in harmony to keep clusters stable and secure.

Gartner forecasts that by 2027, three-quarters of the Fortune 1000 will have dedicated infrastructure platform teams — a fourfold increase from 2023. Yet many of these teams face challenges with fragmented telemetry and limited visibility. As a result, IT operations may come across as a bottleneck during traffic surges or cost overruns. Fortunately, emerging platform tools are beginning to close these gaps by unifying metrics, automating policy application and simplifying autoscaling management.

Understanding Kubernetes autoscaling

Autoscaling is a core capability of Kubernetes. It allows workloads to dynamically adapt based on fluctuating demand. 

Horizontal scaling describes automatic adjustments to the number of pods, while vertical scaling describes altering the resources allocated to each pod. In addition to scaling pods or resources, Kubernetes can increase or decrease the number of worker nodes through a process known as cluster autoscaling.

In contrast to vertical scaling or cluster autoscaling, horizontal pod autoscaling focuses on fine tuning the number of active pods. This approach helps infrastructure and operations teams accommodate real-time application needs, minimizing latency and optimizing resource use.

What is Kubernetes horizontal pod autoscaler?

HPA is a native Kubernetes resource controller that continuously evaluates the performance metrics of workloads. It then adjusts the number of pod replicas accordingly, enabling applications to automatically scale based on demand. HPA targets scalable objects like Deployments, ReplicaSets and StatefulSets. 

For horizontal pod autoscaling, Kubernetes relies on real-time telemetry from monitoring tools such as metrics-server and Prometheus. These tools gather resource usage data, which the autoscaler translates into informed scaling decisions. Robust Kubernetes monitoring ensures these metrics are accessible, accurate and actionable across different workloads.

How does HPA work in Kubernetes?

Kubernetes HPA operates through a control loop that collects metrics, evaluates them against pre-set thresholds and updates the number of replicas in the workload. 

This decision-making process follows a formula, which illustrates the fundamentals of how HPA works in Kubernetes: 

desiredReplicas = currentReplicas * (currentMetricValue / targetMetricValue)

The autoscaler can draw metrics from several types of sources, depending on the complexity and specificity of your scaling needs. The metrics-server provides default support for basic CPU and memory metrics. For more advanced or application-specific insights, the Prometheus Adapter enables custom metrics collection. Additionally, external metrics APIs can integrate signals that Kubernetes doesn’t natively expose. External metrics may include queue depth from an external message broker or service-level indicators from third-party tools.

Accuracy is critical for Kubernetes HPA metrics, as poor metric quality can lead to oscillations or latency in scaling responses. Integrating reliable Kubernetes monitoring tools helps with visualizing metrics trends, setting thresholds appropriately and tracking real-time scaling behavior.

When to use horizontal pod autoscaling

Horizontal pod autoscaling is particularly useful in environments where demand fluctuates rapidly or unpredictably. Consumer-facing flows like retail checkouts, ticketing queues or food delivery order intake are well-known examples of workloads that must stretch in these ways. HPA aligns capacity to those bursts, meaning teams do not have to chase thresholds by hand. It also supports high responsiveness, which can be critical for companies with strict service level agreements or user-facing components.

Autoscaling helps APIs handle burst traffic, specifically because of its ability to absorb sudden user load without compromising performance. Batch processing tasks, which often see spikes in job submissions, can scale worker pods up or down based on workload volume. Microservices that support asynchronous pipelines gain flexibility from autoscaling, as workloads can scale independently in response to demand. Similarly, user-facing applications — especially front-end services — can adjust pod counts dynamically to align with real-time user activity. This dynamic scaling ensures responsiveness while avoiding overprovisioning.

By leveraging HPA in Kubernetes, teams can align resources with real-world usage patterns, reducing waste and improving application reliability. In larger environments, these effects compound quickly and can result in tangible savings and better overall cluster hygiene.

What are the benefits of using Kubernetes HPA?

When implemented effectively, horizontal pod autoscaling can significantly enhance both application performance and infrastructure efficiency. Kubernetes HPA has the potential to reduce overhead and support alignment with governance goals. It offers benefits in technical, operational and financial domains alike.

Resource efficiency

By adjusting the number of pods based on real workload demand, HPA prevents the waste that comes from running idle resources. Instead of provisioning for peak traffic at all times, teams can trust HPA to match capacity to actual usage. As a result, HPA helps free up compute power for other needs.

Cost optimization

The capacity to scale on demand can reduce infrastructure costs. Specifically, when paired with cost tagging practices, HPA enables teams to link resource usage to specific services or departments. These practices can support financial forecasting, improve accountability and help control budgets.

Application performance

HPA helps services stay responsive during sudden load increases or usage spikes. By automatically scaling in real time, applications can avoid latency, maintain service level targets and continue operating smoothly without the need for manual changes.

System resilience

When a pod crashes or a workload becomes overloaded, HPA can quickly restore capacity by launching new replicas. This automatic approach to recovery helps minimize downtime and keeps services running reliably.

Audit readiness

The traceability of system changes is essential in regulated and security-sensitive environments. HPA’s compatibility with policy tools and version control systems ensures that scaling decisions are documented and reviewable.

Policy inheritance

HPA settings can follow pre-approved rules, such as security standards or scaling policies defined by the platform team. This capability keeps autoscaling consistent across clusters, reducing configuration mistakes while maintaining flexibility where required.

Are there limitations to Kubernetes HPA?

HPA can introduce challenges, especially in complex, multi-tenant environments. These limitations should not deter your use of horizontal pod autoscaling, but they do highlight the importance of deliberate design, validation and governance.

Metric lag

HPA decisions are only as timely as their metrics. HPA relies on telemetry collected at intervals, and the default interval is 15 seconds. In fast-moving environments, there is a risk of lag between real-world changes and scaling responses. The delay may result in scaling actions that are too late or no longer necessary.

Cold starts

Newly added pods need time to pull images, initialize containers and pass readiness checks. During this warm-up period, applications may experience degraded performance — even though HPA has initiated the necessary scale-out.

Missing policy defaults

HPA does not enforce minimums, maximums or stabilization windows by default. Without explicitly defined boundaries, workloads may scale unpredictably. Brief metric fluctuations may cause the autoscaler to grow or shrink the number of pod replicas too quickly.

Rapid replica churn

In scenarios where pod counts shift rapidly, security and governance policies can fall out of sync. Pods may be scheduled before network policies, PodSecurityContexts, Role-Based Access Control rules or other controls have a chance to apply. These situations can lead to gaps in enforcement.

Cluster resource contention

When workloads autoscale without shared guardrails or centralized visibility, they are more likely to compete for the same compute resources. Uncoordinated scaling can lead to overutilized nodes, pending pods and performance issues. These risks are especially high in clusters lacking quotas or node autoscaling.

Security misalignment

New replicas that don’t inherit appropriate permissions or policy contexts can fail silently or trigger operational issues. These mismatches often require manual intervention, interrupting the flow of automated scaling.

By proactively layering observability, guardrails and access controls into HPA configurations, platform teams can manage these limitations and autoscale with confidence.

What do you need to run Kubernetes HPA?

To successfully run Kubernetes HPA, your cluster needs the right combination of infrastructure, telemetry, and configuration. 

First, it’s essential to be on a supported Kubernetes version, as newer releases offer improved autoscaling features and compatibility. At the core of HPA’s operation is the metrics-server, which gathers CPU and memory data from the kubelets and supplies it to the HPA controller. This component depends on proper RBAC permissions; without them, the system can’t read metrics or adjust workloads as needed.

For HPA to function correctly, pods must also define resource requests and limits — these act as baselines for calculating utilization percentages. To go beyond default CPU-based scaling, many teams use the Prometheus Adapter, which unlocks custom metrics such as queue length or latency for more nuanced autoscaling.

Reliable Kubernetes monitoring helps ensure these components stay aligned and observable. Tools like SUSE Rancher Prime further simplify operations by offering a visual interface to manage HPA configurations and track real-time scaling behavior.

Kubernetes HPA example

Before you enable HPA, make sure your cluster already meets a few basics: 

• The Kubernetes metrics-server (or an equivalent Prometheus Adapter) is running.
• Workloads have sensible CPU or memory requests. 
• You have permission to create autoscaler objects.

HPA’s control loop depends on the resource-usage metrics — CPU, memory or other signals — that are collected and exposed by the metrics-server. Without this telemetry, the controller cannot calculate replica counts. 

Let’s look at a specific example of creating an HPA within the Rancher UI.

Creating an HPA with Rancher

Instead of writing YAML, you can create an autoscaler in just a few clicks.

1. From the Cluster Management page, select the cluster you want to configure and click Explore.
2. In the left navigation bar, go to Service Discovery > HorizontalPodAutoscalers.
3. Click Create, then select a Namespace and provide a Name for your HPA.
4. Choose the Target Reference for the workload you want to scale.
5. Specify the Minimum Replicas and Maximum Replicas for the workload.
6. Configure the metric. You can choose CPU or memory usage as the trigger and set a target percentage. For example, you could set a floor of three replicas and a ceiling of nine, with a 60% CPU target. This directs HPA to keep average pod utilization near 60% while never dropping below three or rising above nine replicas.

Once you click Create, the HPA is deployed to the chosen namespace. If you need richer signals like queue length or request latency, you can still leverage custom Prometheus metrics. Note that this process requires using kubectl.

Once the autoscaler is live, you can generate a short burst of traffic against the service — a simple curl loop or test harness works — and watch the HPA’s status dashboard refresh. You should see the controller raise the replica count as usage climbs and, conversely, trim it back as load subsides.

Because SUSE Rancher Prime rolls these stats into a shared, cross-cluster view, platform teams can overlay FinOps alerts. As a result, it is easier to spot unexpected cost spikes and confirm alignment with budget expectations. This visibility turns a basic HPA configuration into an auditable, cost-aware scaling workflow.

Best practices for Kubernetes horizontal pod autoscaling

Achieving reliable, safe and performant autoscaling takes more than switching on HPA. Teams should pair configuration with disciplined practices that improve visibility, align autoscaling behavior with application needs and support collaboration across infrastructure and development roles.

Set realistic thresholds

Thresholds should reflect actual application usage and known performance baselines. Instead of relying on arbitrary targets, use historical metrics to define CPU or memory thresholds that prevent reactive or jittery scaling behavior.

Monitor multiple metrics

Default HPA setups typically focus on CPU utilization. A narrow scope can mean scaling too late — or not at all — if a bottleneck is related to memory pressure, queue depth or request latency. Expanding observability to include custom signals can improve the relevance and timeliness of scaling actions.

Alert on drift and anomalies

Well-configured alerts can flag when HPA behaves in unexpected ways. These signals may indicate misaligned thresholds, metric noise or unstable workloads, and they offer early clues for tuning or investigation.

Combine with cluster autoscaling

HPA can only schedule more pods; it cannot provision more nodes. If the cluster is already at capacity, HPA will be unable to schedule new pods. Combining horizontal pod autoscaling with cluster autoscaling ensures the infrastructure can scale in tandem with application needs.

Track and review scaling activity

Logging HPA decisions helps teams understand scaling patterns over time. Reviewing these logs can surface recurring issues, validate tuning efforts and support incident investigations when performance doesn’t meet expectations.

By embedding these Kubernetes HPA best practices into daily operations, organizations can move beyond reactive scaling. They will  be ready to adopt more predictable, transparent and resilient autoscaling strategies.

Troubleshooting for Kubernetes horizontal pod autoscaler issues

Effective HPA troubleshooting requires clear visibility into the flow of autoscaling signals through your environment, including where they might break down. One of the most common causes of HPA inaction is a missing or misconfigured metrics-server. Without accurate telemetry, HPA can’t evaluate pod performance. Even when the metrics-server is running, scaling may still fail if pods lack defined CPU or memory requests, which are essential for calculating resource usage.

Unexpected scale-downs often stem from brief dips in metrics that trigger premature scaling decisions. Without stabilization windows or cooldown periods, HPA may shrink workloads too aggressively, hurting performance. On the other hand, if HPA attempts to scale out but the cluster lacks available nodes, new pods can get stuck in a Pending state. In these cases, HPA has done its job, but the infrastructure hasn’t kept pace. These situations often require cluster autoscaling support.

Custom metrics introduce additional complexity. If Prometheus Adapter data isn’t surfacing, teams should look for RBAC misconfigurations, incorrect labels or broken query paths. Without a working metrics pipeline, custom signals can silently fail.

In large or multi-cluster environments, shared dashboards and cross-cluster alert hooks help surface these problems more quickly. Centralized observability lets teams connect HPA behavior to infrastructure patterns, correlate anomalies across clusters and reduce time to resolution. When FinOps alerts are part of the same telemetry stream, teams can also spot and accurately attribute cost anomalies caused by autoscaler misbehavior. These shared insights improve responsiveness and accountability, turning HPA into a more reliable and transparent scaling tool.

Manage Kubernetes HPA with SUSE Rancher Prime

While HPA makes dynamic scaling accessible, not every team or environment is naturally set up for Kubernetes scalability. For example, teams may struggle with blind spots in observability, inconsistent policy enforcement and security gaps during rapid replica churn. SUSE Rancher Prime directly addresses these issues by combining visibility, auditability and governance into a cohesive autoscaling experience.

In addition, SUSE Rancher Prime provides a single, transparent pane of glass. Organizations can surface metrics from across clusters through unified dashboards, bringing clarity to scaling triggers and trends. These insights help eliminate guesswork, reduce response time to anomalies and ensure operators are never flying blind. Git-backed HPA configurations enable version control and traceability, so teams can audit every scaling event and satisfy compliance requirements with ease. Critically, SUSE Rancher Prime supports the consistent application of RBAC policies and CIS-aligned security controls — even during rapid scale-outs. By baking in policy inheritance and supporting multi-cluster governance, SUSE Rancher Prime curbs the cluster sprawl and security stall-outs that can accompany decentralized scaling.

SUSE Rancher Prime enables smarter, more user-controlled scaling across clusters and clouds. Teams gain elasticity without sacrificing control, and autoscaling evolves from a tactical fix into a strategic advantage. 

Ready to replace black-box bursts with self-service, observable automation? Learn more about SUSE Rancher Prime.

Kubernetes HPA FAQs

Why should you use Kubernetes HPA?

You should use Kubernetes HPA because it enables applications to scale automatically in response to real-time metrics. Kubernetes helps teams maintain consistent performance without manual oversight. It is particularly valuable in distributed or multi-cluster environments and also when services must maintain responsiveness under load.

What is the difference between Kubernetes HPA and Kubernetes VPA?

Kubernetes HPA and Kubernetes VPA address different scaling dimensions. The Horizontal Pod Autoscaler automatically adjusts the number of pods, while the Vertical Pod Autoscaler alters the CPU and memory requests within each pod. 

How do I know if HPA is enabled in Kubernetes?

You will know if HPA is enabled in Kubernetes by checking your management interface or monitoring dashboards for active autoscaler configurations. The presence of defined thresholds, associated workloads and recent scaling activity will indicate that HPA is active and operational.

If you do not see these indications, and you do not know how to enable HPA in Kubernetes, reach out to the team responsible for managing foundational systems and services that support and run your applications.