The Horizontal Pod Autoscaler (HPA) is the engine that keeps your Kubernetes workloads responsive during traffic spikes and cost-efficient during quiet hours.

This guide walks you through exactly how HPA works, why modern workloads need autoscaling, how metrics drive scaling decisions, and how tools like KEDA unlock true event-driven scaling.

Dive in and learn how to build smarter, self-adjusting Kubernetes systems that scale exactly when your application needs it — no overprovisioning, no guesswork, no surprises.

Introduction to K8s HPA

What is HPA in Kubernetes?

The Horizontal Pod Autoscaler (HPA) is a Kubernetes mechanism that automatically increases or decreases the number of Pods for a Deployment, ReplicaSet, or StatefulSet. It watches live metrics—such as CPU usage, memory usage, or custom metrics—and adjusts the replica count whenever the actual value moves away from the target you configure.

Why do modern workloads need autoscaling?

Modern workloads don’t stay steady. Their demand swings rapidly based on user behavior and external events—sometimes within seconds.

Applications often face unpredictable load patterns such as traffic spikes triggered by trending topics, sudden bursts in messaging activity, or quick surges during flash sales and live events.

These fluctuations create peaks and valleys that fixed compute capacity simply cannot track.

Autoscaling solves this by adjusting resources automatically whenever the workload rises or drops. Instead of relying on static provisioning, the system expands during heavy activity and contracts during quieter periods, ensuring consistent performance without wasting resources.

How HPA fits into Kubernetes architecture?

HPA fits into the architecture through three main interactions:

1. API Server
   • HPA reads metrics and writes updated replica counts using the Kubernetes API. All scaling decisions pass through the API server.
2. Metrics Provider
   • HPA retrieves metric values—such as CPU, memory, or custom metrics—from the Metrics Server or any external metrics adapter (Prometheus, KEDA, Cloud provider adapters).
3. Controller Manager
   • The HPA logic runs inside the Kubernetes Controller Manager. It executes the control loop that computes how many Pods should be running based on the configured targets.

Types of Metrics Supported by HPA

Kubernetes HPA supports four categories of metrics. Each category defines how HPA reads values and decides the desired number of Pods.

1. Resource Metrics
   • Kubernetes HPA supports four categories of metrics. Each category defines how HPA reads values and decides the desired number of Pods.
2. Pods Metrics
   • Metrics aggregated across all Pods of the target workload. Examples include requests-per-second, queue depth per Pod, or custom runtime metrics emitted by each Pod.
3. Object Metrics
   • Metrics attached to a Kubernetes object, such as the length of a queue in a Queue resource or the requests-per-second value of an Ingress. HPA scales based on the metric value of that object.
4. External Metrics
   • Metrics coming from outside the cluster. Examples include cloud provider metrics, Kafka lag, API rate usage, or any data exposed through a metrics adapter.

These metric types allow HPA to scale workloads using built-in signals, application-level metrics, Kubernetes object metrics, or external ecosystem data.

Kubernetes HPA Configuration Explained

Configuring the Horizontal Pod Autoscaler is essentially about defining how Kubernetes should interpret load and how aggressively it should react.

Instead of being just a YAML object, the HPA configuration acts like a rulebook that tells Kubernetes when to add Pods, when to remove them, and how to avoid scaling mistakes.

Here’s what the configuration actually shapes inside the autoscaling process:

1. What workload should react to metrics
   • HPA configuration links a metric to a specific workload. Kubernetes will only scale the Pods belonging to that target, ignoring the rest of the cluster.
2. The allowed scaling boundaries
   • minReplicas and maxReplicas set the safe operating range so the application never scales too low (breaking availability) or too high (overloading nodes).
3. The signals Kubernetes uses for decision-making
   • Different sections of the config define where the “truth” comes from:
     • Resource usage from Metrics Server
     • Custom metrics from adapters
     • Queue or object values from other Kubernetes resources
     • External sources like cloud monitoring or Kafka lag
   • This tells Kubernetes what kind of load matters for this application.
4. The exact point at which scaling should begin
   • Every metric in the configuration carries a target—CPU %, memory value, queue length, or custom metric.
   • This value becomes the “trigger line” for scaling events.
5. How fast scaling is allowed to happen
   • How quickly Pods can increase
   • How cautiously Pods should decrease
   • Whether K8s should smooth out metric spikes
   • These rules prevent sudden jumps that might destabilize workloads.
6. The continuous feedback loop
   • Once the configuration is applied, HPA runs a loop that repeatedly:
     • Collects metrics using the sources you defined
     • Applies the scaling rules
     • Stays within the boundaries
     • Updates the replica count
   • All of this is powered completely by the configuration fields you set.

Kubernetes HPA on Custom Metrics

Horizontal Pod Autoscaler can scale workloads using custom metrics when CPU or memory usage isn’t a reliable indicator of application load. Custom metrics let the autoscaler read signals that are specific to your application’s behavior rather than relying on generic resource consumption.

In this model, Kubernetes doesn’t calculate anything on its own (for custom metrics). Instead, an external metrics adapter (often backed by Prometheus, Datadog, KEDA, or cloud monitoring systems) exposes metric values to the cluster. HPA then fetches those values and adjusts the replica count based on the scaling rules you define.

Custom metrics are useful when load is better represented by application-level indicators, such as:

• the number of active requests inside the service
• queue depth for background workers
• messages waiting in Kafka or RabbitMQ
• transactions processed per second
• concurrent user sessions
• lag, backlog, or unprocessed events

Using custom metrics gives you control over how your application perceives pressure, allowing more accurate scaling behavior for workloads that aren’t CPU-bound or memory-heavy.

Event-Driven Autoscaling with HPA + KEDA

Kubernetes HPA works well when scaling decisions are based on continuous metrics like CPU, memory, or custom application values. But many modern systems don’t operate on steady patterns—they react to events. Message spikes, queue backlogs, scheduled jobs, IoT data bursts, and Kafka lag can increase suddenly and disappear just as fast. Traditional HPA alone doesn’t handle these situations well.

This is where KEDA (Kubernetes Event-Driven Autoscaling) comes in. KEDA extends HPA by turning external event sources into scalers that K8s can react to. Instead of relying only on resource metrics, KEDA feeds event-driven metrics into HPA, enabling Pods to scale precisely when an event occurs.

With KEDA, autoscaling becomes tied to triggers such as:

• Kafka partition lag
• RabbitMQ queue depth
• Azure Service Bus message count
• AWS SQS backlog
• Cron-based scheduled workloads
• HTTP request spikes captured by external monitoring
• Database row counts or processing queues

KEDA monitors these event sources continuously. When it detects pressure—like messages piling up or lag increasing—it exposes those values to HPA through metrics adapters. HPA then treats them as valid scaling signals and adjusts replica counts accordingly.

Troubleshooting HPA

Horizontal Pod Autoscaler issues almost always come down to missing metrics, slow reactions, or misaligned configuration.

The most common problem is that HPA simply doesn’t scale at all, usually because the cluster isn’t receiving the metrics it needs. If the metrics-server is unhealthy or not installed, HPA cannot read CPU or memory values, so it reports “0% utilization” even when the application is under heavy load.

Even when HPA does scale, it often reacts more slowly than expected. This delay comes from how K8s collects metrics (roughly every 30 seconds) and from stabilization windows that intentionally prevent rapid fluctuations. These timing controls help avoid thrashing, but they can be problematic for sudden traffic spikes where every second matters.

Scale-down failures also confuse many teams. HPA may maintain a high replica count long after traffic decreases because of default five-minute stabilization windows, Pod Disruption Budgets, or failing health probes.

Troubleshooting HPA means following one simple mindset: verify metrics, verify scheduling, and verify behavior windows. If any of those three pieces fail, the autoscaler won’t act the way you expect. Once you understand how each part contributes to a scaling decision, diagnosing issues becomes far more predictable.

Summary

Kubernetes Horizontal Pod Autoscaler (HPA) automatically adjusts the number of Pods based on real-time metrics like CPU, memory, or custom values. It exists because modern workloads fluctuate heavily—traffic spikes, queue surges, and user-driven bursts make static provisioning unreliable.

HPA works by continuously pulling metrics from the Metrics Server or external adapters and running a control loop inside the Controller Manager. It uses configuration fields such as targets, thresholds, boundaries, and scaling policies to decide when to add or remove Pods.

HPA supports four metric types: resource, pod-level, object-based, and external metrics. This flexibility enables autoscaling based on both infrastructure signals and application-specific indicators. For event-driven architectures, HPA pairs with KEDA, which converts message backlogs, Kafka lag, queue depth, cron schedules, and more into autoscaling triggers.

Most HPA issues stem from missing metrics, delays from stabilization windows, or scheduling constraints. Troubleshooting usually involves checking metrics availability, verifying node capacity, and understanding the timing logic behind scale-up and scale-down events.