Managing CPU and memory resources efficiently is one of the most critical aspects of running applications in Kubernetes.
That’s where Vertical Pod Autoscaling (VPA) comes in — it helps your workloads get the right amount of resources automatically without manual tuning.
⚙️ What is Vertical Pod Autoscaling (VPA)?
Vertical Pod Autoscaling (VPA) automatically analyzes and adjusts the CPU and memory requests and limits of your Pods.
It helps you ensure that each Pod always has the resources it needs — not too little, not too much.
🧩 How it Works
VPA continuously monitors your Pod’s usage and suggests (or applies) new resource requests and limits.
When an update is required, VPA may evict and recreate the Pod with optimized resource values.
There are two main modes:
🔹 1. Manual Mode
- VPA only recommends CPU and memory values.
- You can view these recommendations and manually update your Deployment or StatefulSet YAML.
- Useful when you want to maintain full control.
🔹 2. Automatic Mode
- VPA automatically adjusts the CPU and memory requests and limits.
- If required, it evicts and restarts the Pod with the updated configuration.
- Ideal for dynamic workloads that change resource usage over time.
🏗️ VPA Architecture in GKE
In GKE, VPA consists of three main components:
VPA Recommender → Analyzes metrics and suggests resource updates.
VPA Updater → Evicts Pods when updates are needed.
VPA Admission Controller → Modifies Pod specs with new recommendations.
💡 GKE Advantage:
In GKE, these components run as control plane processes, not as user-managed Deployments — meaning less overhead for you.
☁️ Standard vs Autopilot Clusters
| Feature | Standard Cluster | Autopilot Cluster |
|---|---|---|
| VPA Configuration | Needs to be enabled manually per workload | Enabled by default |
| Node Management | Managed by user or autoscaler | Fully managed by GKE |
| Ideal For | Fine-grained control | Fully managed, hands-off scaling |
💪 Benefits of Vertical Pod Autoscaling
✅ Optimal Resource Usage
Each Pod uses exactly the CPU and memory it needs — no waste, no shortage.
✅ Reduced Manual Effort
No need for manual performance benchmarking to find the right resource requests.
✅ Improved Scheduling
Pods are automatically placed on nodes that have suitable resources.
✅ Better Cluster Efficiency
Works alongside Cluster Autoscaler for smarter node utilization.
⚠️ Important Note
Before enabling VPA, make sure your Cluster Autoscaler is turned on.
This allows VPA and Cluster Autoscaler to work together — if VPA increases a Pod’s resource request and nodes run out of space, the Cluster Autoscaler can automatically add more nodes.
🧠 Example Use Case
Imagine a web application that receives traffic spikes during the day and stays quiet at night.
- VPA monitors its CPU and memory usage.
- It increases resources when demand grows.
- It scales down when usage drops.
All of this happens automatically — keeping performance stable while saving costs.
🧩 Summary
| Key Feature | Description |
|---|---|
| Purpose | Adjust Pod CPU and memory requests/limits automatically |
| Modes | Manual (recommend only) and Automatic (apply updates) |
| Integration | Works with Cluster Autoscaler |
| Benefit | Cost-efficient and reliable workload performance |
| Default Behavior | Enabled automatically in Autopilot clusters |
✅ In Short
VPA helps your Pods right-size themselves automatically.
It’s like having a smart assistant that constantly tunes your app’s performance and cost-efficiency behind the scenes.
🌟 Thanks for reading! If this post added value, a like ❤️, follow, or share would encourage me to keep creating more content.
— Latchu | Senior DevOps & Cloud Engineer
☁️ AWS | GCP | ☸️ Kubernetes | 🔐 Security | ⚡ Automation
📌 Sharing hands-on guides, best practices & real-world cloud solutions
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