Why Overprovisioning Breaks Kubernetes Autoscaling

Autoscaling is one of the most powerful features in Kubernetes. It promises to help you respond to fluctuating demand without manual intervention - saving costs when traffic is low and scaling automatically when it's high.

But what happens when… it doesn't scale?

Even when traffic increases and users experience degraded performance - the number of pods remains the same.

The culprit?

Overprovisioning.

⚠️ The Problem: No Scaling Despite Load

Imagine this: You’re running a microservice that handles frequent API calls. You run a load test and notice increasing latency and timeouts. But strangely enough, no new pods are being added by the Horizontal Pod Autoscaler (HPA).

You check the metrics and see CPU usage hovering around 20%. Autoscaling is configured to trigger at 50%. So everything looks fine... right?

Not really.

Despite clear signs of stress, Kubernetes doesn’t scale - because the app is requesting way more resources than it actually needs.

🕵️‍♂️ Root Cause: Overprovisioning

In many cases, developers and engineers allocate large CPU and memory limits "just to be safe." It’s a common habit - especially when there’s little visibility into how much an app truly needs.

For example:

You give your service 1 CPU and 1Gi memory, but in reality, it only ever uses ~200m CPU and 300Mi memory.

What happens next?

Kubernetes thinks your pod is underutilized.
HPA sees usage at only 20% of the requested value - so it does nothing.
Meanwhile, the node is overcommitted, and other workloads may also get throttled.
End-users face slower response times… and nobody knows why.

🔧 Real-World Example (Anonymized)

A team was managing a stateless API service handling product metadata. It was configured with:

1 core CPU request
1.5 cores CPU limit
1Gi+ memory

During load testing, it showed 0% CPU usage in HPA metrics, despite experiencing throttling and performance degradation. Why?

Because:

It was actually using around 200m CPU per pod.
The high request value masked this under-utilization.
Kubernetes couldn’t allocate more resources, and HPA never scaled the workload.

✅ The Fix:

Right-sized the pod to use:
- 256m CPU request
- 512m CPU limit
- ~256Mi–768Mi memory range
HPA immediately started picking up the actual usage.
KEDA was added to scale based on requests per minute, not just CPU.

💡 Result: The app handled load better, latency dropped, and resource cost dropped by nearly 60%.

🔍 Common Pitfalls in Kubernetes Autoscaling

❌ Overprovisioning Resources

When request values are too high, autoscalers see artificially low usage and don’t act - even under pressure.

❌ Scaling Only on CPU

CPU isn’t always the best indicator of load, especially for:

I/O-bound workloads
Latency-sensitive apps
APIs with fast but frequent calls

❌ Ignoring Node Throttling

Even if your pod isn't consuming much, an overcommitted node will throttle workloads to protect overall stability.

✅ Best Practices for Smart Autoscaling

1. Right-Size Your Workloads

Start small. Monitor your pods using:

kubectl top pods

Compare actual usage to requested values. If usage is consistently low, reduce the request and limit values.

2. Tune HPA Carefully

Use reasonable thresholds:

Trigger at 50–60% CPU usage
Set minimum and maximum replica ranges based on traffic expectations

3. Add Request-Based Scaling (with KEDA)

HPA works well with CPU/RAM, but doesn’t understand traffic volume.

Use KEDA to scale based on:

Requests per second
Queue length
Custom Observability metrics

Don’t reuse resource configs from unrelated services. Each service behaves differently.

5. Watch for Throttling

If you see throttling in your observability tools, it’s time to revisit your requests and limits. Even if HPA isn’t scaling, Kubernetes might still be struggling to allocate what you asked for.

❓ FAQs

Q: Why doesn’t HPA scale even under load?

A: If the app is overprovisioned, CPU usage stays low relative to the request, so the autoscaler won’t trigger.

Q: How do I know what CPU/memory to request?

A: Start with a conservative value (e.g., 200m CPU, 256Mi memory). Monitor usage under normal and peak traffic, and adjust accordingly.

Q: When should I use KEDA?

A: When CPU isn’t a reliable scaling signal. For example, use KEDA to scale on:

HTTP request rate
Queue depth
Event counts
Custom metrics

Q: Can overprovisioning affect other apps?

A: Yes. Especially in shared node pools, it can cause throttling across unrelated services and reduce overall node efficiency.

📌 Final Takeaway

🧠 Overprovisioning doesn’t protect your app - it hides the real load and breaks autoscaling.

Instead, embrace:

Right-sizing
Smart metric selection
Intentional scaling strategies

By tuning your workloads based on reality - not assumptions - you can achieve better performance, more reliable scaling, and major cost savings.

🚀 Why Overprovisioning Breaks Kubernetes Autoscaling