The Horizontal Pod Autoscaler (HPA) in Kubernetes automatically scales the number of pods in a deployment or replica set based on observed resource usage, such as CPU or memory.

This article breaks down how HPA works, when to use it, its pros and cons, and how to get started with it in your Kubernetes cluster.

1. What is HPA?

HPA dynamically adjusts the number of pods in a Kubernetes workload (like a Deployment or StatefulSet) based on metrics from the Kubernetes Metrics Server.

Typical scaling metric: CPU utilization, but it can be extended to custom or external metrics.

Example HPA manifest:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 75

2. Benefits of HPA

  • ⚖️ Automatic scaling: Matches workload demand in real-time
  • 💸 Cost savings: Reduces overprovisioning by scaling down
  • 📈 Improves performance: Adds replicas during high load
  • 🛠️ Flexible: Supports custom and external metrics (via Prometheus Adapter)

3. Drawbacks of HPA

  • ⏱️ Metrics lag: Based on averages, so reaction time may lag behind real spikes
  • ⚠️ Not ideal for short-lived spikes: Quick surges may not trigger scaling in time
  • 🔄 Pod creation time: Scaling adds pods, but they still take time to start
  • 🔧 Requires Metrics Server or external adapter
  • ❗ Doesn’t manage resource requests/limits: Combine with VPA for full optimization

4. When to Use HPA

  • Stateless applications like APIs or web servers
  • Applications with predictable traffic patterns
  • Microservices where horizontal scaling is preferred
  • Scenarios where uptime and responsiveness matter

5. When HPA Might Not Fit

  • State-heavy or persistent applications
  • Latency-critical workloads with sub-second spikes
  • Environments without metrics infrastructure

6. Conclusion

HPA is one of Kubernetes’ core autoscaling features. When used right, it enables elastic, responsive, and cost-efficient scaling. Pair it with VPA or KEDA for advanced scenarios.