HPA Is Not Enough for Your Kubernetes Operation—Here’s Why

Author: Hwansoo Kim (opens in a new tab)

Date: February 12, 2025

Introduction

Kubernetes is the most popular way to run containerized applications, but managing scalability is still a challenge. Many teams rely on Horizontal Pod Autoscaler (HPA) to automatically scale their applications, assuming it will keep everything running smoothly. However, HPA has serious limitations that can lead to slow scaling, wasted resources, and poor performance during traffic spikes.

In this post, we’ll cover:
✅ What HPA is and how it works
✅ Why HPA alone is not enough for modern Kubernetes workloads
✅ How to overcome its limitations
✅ How Wave Autoscale goes beyond HPA to provide AI-powered, efficient scaling

1. What Is HPA?

Horizontal Pod Autoscaler (HPA) is a built-in feature in Kubernetes that automatically adjusts the number of running pods based on CPU or memory usage.

Why Do People Use HPA?

HPA is popular because it:

• Helps handle sudden spikes in traffic
• Prevents crashes due to overloaded servers
• Reduces costs by removing unnecessary pods

Example HPA Configuration (Scaling Pods Based on CPU Usage)

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: 70

This tells Kubernetes: 👉 Keep CPU usage around 70% by adjusting the number of pods between 2 and 10.

2. How HPA Works

HPA constantly monitors the resource usage of your application and scales it based on predefined rules.

HPA Scaling Process

1. Collects Metrics – Monitors CPU or memory usage every 15 seconds
2. Compares Usage vs. Target – Checks if usage is above or below the threshold
3. Calculates the Number of Pods Needed – Decides how many new pods should be added or removed
4. Adjusts the Deployment – Updates the Kubernetes Deployment to reflect the new pod count

Example:

• A shopping app gets a traffic spike on Black Friday.
• HPA sees that CPU usage is over 70% and adds 3 more pods.
• Once traffic slows down, HPA removes extra pods to reduce costs.

🚀 Sounds great, right? But here’s the problem…

3. Why HPA Is Not Enough

HPA only looks at CPU and memory usage, which is not always the best indicator of actual demand.

🔴 Problem #1: HPA Reacts Too Slowly

HPA waits for CPU or memory usage to increase before taking action. By the time it reacts, the traffic spike may have already caused slowdowns or failures.

Example:

• A video streaming app suddenly gets 100,000 new viewers.
• HPA takes 30+ seconds to detect the load and start scaling.
• By the time new pods are ready, viewers are already experiencing buffering issues.

🔴 Problem #2: HPA Doesn’t Consider Traffic Patterns

HPA only scales based on past CPU/memory usage, but it doesn’t predict traffic spikes.

Example:

• A news website gets massive traffic after breaking news.
• HPA doesn’t pre-scale the pods before the traffic surge.
• The site slows down or crashes until scaling catches up.

🔴 Problem #3: HPA Can Waste Resources

HPA scales up aggressively but is slow to scale down. This leaves unnecessary pods running longer than needed, increasing cloud costs.

Example:

• A food delivery app adds 50 pods during lunch rush.
• Traffic drops after 2 PM, but HPA keeps extra pods running for too long.
• The company pays for unused resources.

🔴 Problem #4: HPA Doesn’t Scale Nodes

HPA only adds pods. But if your Kubernetes cluster runs out of space, new pods stay pending because there aren’t enough worker nodes to run them.

Example:

• A gaming server experiences a traffic surge.
• HPA adds 20 pods, but the cluster is full.
• The new pods fail to start, leading to downtime.

🚨 HPA is helpful, but it doesn’t solve all scaling problems.

4. How to Overcome HPA’s Weaknesses

To improve Kubernetes scaling, we need a smarter solution that:

• ✅ Predicts scaling needs ahead of time instead of reacting too late.
• ✅ Considers actual traffic patterns instead of just CPU/memory.
• ✅ Scales worker nodes and pods together to prevent pending workloads.
• ✅ Optimizes cost efficiency by scaling down quickly when traffic drops.

Some teams try to fix HPA by adding custom metrics, but configuring them is complex. What if scaling was fully automated and AI-driven?

5. How Wave Autoscale Replaces HPA (and Does More!)

What Is Wave Autoscale?

Wave Autoscale is an AI-powered Kubernetes scaling solution that replaces HPA, VPA, and Cluster Autoscaler with a fully automated and predictive system.

How Wave Autoscale Solves HPA’s Problems

Key Features of Wave Autoscale

• AI-Driven Scaling – Learns from traffic patterns and system logs.
• Proactive Scaling – Prepares for spikes in advance instead of reacting late.
• Cost Optimization – Avoids over-provisioning, reducing cloud waste.
• Full Kubernetes Automation – No manual tuning needed.

Wave Autoscale in Action:

Imagine an AI-driven autoscaler that:

• Detects an upcoming traffic surge before it happens.
• Scales your pods & nodes automatically without delays.
• Removes extra resources quickly to cut cloud costs.

🚀 That’s the power of Wave Autoscale!

Conclusion: Kubernetes Needs More Than Just HPA

• HPA is useful, but it’s not enough for modern cloud applications.
• It reacts too late, doesn’t predict traffic, and can waste money.
• Wave Autoscale replaces HPA with AI-powered, real-time scaling.

Ready to simplify your Kubernetes operations? Learn how advanced automation can streamline your workflows, save resources, and boost performance.

Take the first step toward an easier, more scalable Kubernetes environment. Click here (opens in a new tab) to explore Wave Autoscale and schedule a personalized demo today.

Don’t let Kubernetes complexity hold you back. Embrace automation and unlock the full potential of your cloud infrastructure!