What is Azure Kubernetes Service cost optimization?

Cost optimization in AKS means right-sizing your cluster capacity, using event-driven autoscaling, leveraging Spot instances, and eliminating idle resources. It's about matching your infrastructure spend to actual workload demand instead of keeping phantom capacity "just in case."

Should I use HPA or KEDA for scaling?

Use HPA for metric-based scaling (CPU, memory) on continuous workloads. Use KEDA for event-driven workloads (queues, streams, HTTP requests). Best teams use both—HPA for baseline load, KEDA for precise event-triggered scaling down to zero.

How much can I save with Spot instances on AKS?

A 70/30 split between On-Demand and Spot nodes typically saves 25-40% on compute costs, depending on your workload distribution and regional capacity. The savings are real but only if you architect for failures (pairing with stable System node pools).

🏗️ DevOps & Infrastructure

Azure Kubernetes Service: Why Your Cost Optimization Strategy Is Probably Broken

You're probably overspending on Azure Kubernetes Service by 40%. Here's what the vendor won't tell you about scaling, security, and keeping your cloud bill sane.

Open Source Beat Apr 03, 2026 6 min read 15 views

Azure Kubernetes Service cluster dashboard showing pod scaling metrics and cost attribution across node pools

⚡ Key Takeaways

KEDA event-driven autoscaling can reduce AKS compute costs by 30%+ by scaling workloads down to zero when idle—but most teams never discover it 𝕏
Azure Spot Instances at 90% discount work, but only when paired with stable On-Demand system pools; going all-in on Spot creates unnecessary risk 𝕏
HPA with default settings creates phantom costs and thrashing; real savings come from understanding actual traffic patterns and implementing right-sizing before autoscaling 𝕏

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#AKS security #Azure Kubernetes Service #KEDA autoscaling #Kubernetes scaling #cloud cost optimization

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Originally reported by DZone

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