AI Rightsizing for Kubernetes: Start with the Boring Baseline
Before you trust ML to resize pods, fix your signals, budgets, and guardrails. Otherwise AI just automates bad guesses.
You don't need AI-driven anomaly detection to save money. You need to fix your Node Rightsizing and Requests. Here is the strategy.
I see teams waste weeks trying to shave 2% off their bill by optimizing serialization formats or tweaking garbage collection settings.
Meanwhile, their clusters are running at 15% utilization.
In Kubernetes cost optimization, the Pareto Principle (80/20 rule) is brutally effective. 80% of your wasted spend comes from just two sources.
Engineers are risk-averse. If an app needs 200m CPU, they request 1000m “just to be safe.”
Kubernetes reserves that 1000m on the node. It cannot be used by anyone else, even if the app is idle.
The Fix:
Look at your Max Usage over the last 7 days. Set your Request to Max Usage + 20%. That’s it. You just saved 60% of your capacity without touching a line of code.
“We use m5.2xlarge because that’s what we’ve always used.”
If your pods are memory-heavy (Java, Python), running them on Compute Optimized (c5) nodes is throwing money away. You’ll run out of RAM while half your CPU sits idle.
The Fix: Bin-packing. Look at your aggregate cluster requirements.
c6i or c7g (Graviton).r6i or r7g.Matching your node shape to your workload shape is the single biggest infrastructure win you can make.
Things that matter less than you think:
If you want to cut your bill in half, follow this order of operations:
Don’t let perfect be the enemy of profitable. Fix the big leaks first.
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Before you trust ML to resize pods, fix your signals, budgets, and guardrails. Otherwise AI just automates bad guesses.
Stop guessing your CPU and RAM requests. A data-driven guide to right-sizing your pods without killing performance.
Quantify zombie capacity, catch misconfigured autoscalers, and automate remediation with ClusterCost.
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