Scaling Kubernetes Workloads with HPA and VPA

Stop overcomplicating Kubernetes cost optimization — right-sizing pods and autoscaling strategies. I've reviewed hundreds of implementations. The best ones? Dead simple. The pattern: - Start with the boring solution - Measure actual bottlenecks - Only then add complexity Premature optimization is real, and it kills projects. What's the simplest solution you've shipped that just worked? #DevOps #CloudComputing #Kubernetes

🚀 Post 1: Kubernetes Journey When you start learning Kubernetes, everything feels simple… ➡️ Run a container ➡️ Create a Pod ➡️ Deploy your first app But give it some time… ⏳ You’ll soon be navigating: 🔹 Multi-cluster architectures 🔹 Service meshes 🔹 Auto-scaling & observability 🔹 CI/CD integrations 🔹 Security & networking complexities From Single Pod ➝ Multi-Cluster Management, the journey is real 😄 💡 The key? Don’t rush. Master the basics, then scale your knowledge step by step. #Kubernetes #DevOps #CloudComputing #LearningJourney #Containers #PlatformEngineering

What if you could separate control planes from worker nodes? 🤔 That’s exactly what HyperShift enables. A modern way to run Kubernetes clusters efficiently across multiple environments. 🔹 Hosted control planes 🔹 Multi-cloud worker nodes 🔹 Better scalability & cost optimization Step into the future of Kubernetes with HawkStack. https://lnkd.in/g7QSNA7V #HyperShift #Kubernetes #OpenShift #MultiCloud #DevOps #CloudArchitecture

Auto-scaling in Kubernetes 🚀 Microservices shine when demand spikes and the system scales on its own. In Kubernetes, this is achieved by combining two powerful tools. How it works in practice: - You create a Deployment defining how many replicas you want - You configure the Horizontal Pod Autoscaler (HPA) to monitor CPU usage Kubernetes automatically adjusts the number of pods — scaling up during peaks, scaling down when things quiet down The result? A more resilient, efficient application, better prepared to handle load variations without manual effort. Simple, automatic, and essential for anyone working with distributed systems. #Kubernetes #DevOps #Microservices #CloudComputing #Scalability

It is time again: flawless Kubernetes update, done. Control plane and all worker nodes are Ready, the cluster is healthy, and the rollout to v1.35.4 went exactly as planned. Moments like this are a good reminder that solid infrastructure is built on consistency, preparation, and repeatable processes. Quiet upgrades are the best upgrades. #Kubernetes #DevOps #CloudEngineering #CloudNative #SRE #PlatformEngineering

Today Kubernetes almost broke our production. And the reason was embarrassingly simple. Traffic suddenly started spiking.📈 Monitoring dashboards lit up. Latency increased. Pods began restarting. And the cluster autoscaler started spinning up nodes aggressively. At first glance, it looked like a classic scaling issue. But the real problem? A misconfigured resource limit in a Helm chart. One of our services had a memory limit that was far too low. Under higher traffic, the pods kept getting OOMKilled, which triggered a restart loop. Kubernetes was doing exactly what it was supposed to do. The system wasn’t broken. Our configuration was. After fixing the memory limits and redeploying, everything stabilized within minutes. No scaling changes needed. Just better resource configuration. Lesson learned: Many Kubernetes “failures” aren’t Kubernetes problems. They’re configuration problems. Monitoring tools help you detect incidents. But understanding how your system behaves under pressure is what helps you solve them. Curious how other engineers approach this: Do you stress test your Kubernetes resource limits before production traffic hits? #DevOps #Kubernetes #SRE #AWS #EKS #CloudEngineering #PlatformEngineering #ReliabilityEngineering

Kubernetes v1.36 One thing that always felt limiting with HPA was simple that it could scale down… but never all the way to zero. You’d still have at least one pod running, even when there was no traffic. Which means you’re still paying for something that isn’t doing any work. That finally changes in Kubernetes v1.36. HPA can now scale from N > 0 pods And just as importantly, scale back from 0 > N when traffic returns. This is a big shift, especially for real-world workloads. Why this matters: 1. you get true cost optimization (no idle pods sitting around) 2. better resource utilization across the cluster 3. a much cleaner fit for event-driven and bursty workloads For teams running APIs, background jobs, or anything with unpredictable traffic, this removes a long-standing trade-off. Feels like a small feature… but it changes how you think about scaling. #Kubernetes #CloudNative #DevOps #PlatformEngineering #Autoscaling #CloudComputing

👉 If you’re not tracking Kubernetes costs, you’re probably overspending. Kubernetes made scaling easy. But it also made overspending invisible. In many clusters I’ve seen, 30–50% of resources are underutilized. Here’s what actually moves the needle: • Right-size workloads (requests ≠ actual usage) • Kill idle resources (unused namespaces, orphan pods) • Use autoscaling smartly (HPA + cluster autoscaler) • Add cost visibility with tools like Kubecost The role of a platform engineer is evolving: It’s no longer just about uptime. It’s about running efficient platforms at scale. 💡 Takeaway: Start treating your Kubernetes platform like a business — every CPU and GiB has a cost. #Kubernetes #FinOps #PlatformEngineering #CloudCost #OpenShift #DevOps #CloudNative #SRE

☸️ Kubernetes v1.36.0 is out — and this is a major release with real changes you should pay attention to. This isn’t just “new features” — it’s also about what’s changing or going away. ⚠️ One important change: The long-deprecated gitRepo volume has finally been removed. 👉 If you still rely on it, workloads will break after upgrading — and you’ll need to migrate to alternatives like init containers or external sync tools. (Kubernetes) ✨ On the feature side: • Mutating Admission Policies → now stable (less reliance on webhooks) • User Namespaces → improved isolation for containers • Dynamic Resource Allocation (DRA) → continues evolving for advanced workloads These are the kinds of changes that impact: • security posture • workload isolation • cluster extensibility Kubernetes v1.36 is a good reminder: 👉 Major releases are not just about what’s new — they’re about what might break and what needs migration. At Relnx, we track these changes so you can quickly understand: ✅ breaking changes ✅ new capabilities ✅ upgrade impact 🔎 Full release breakdown: https://lnkd.in/g3PEecwm For platform teams — What’s your first step when a new Kubernetes version drops: 👉 Check features 👉 Or check breaking changes first? #Kubernetes #CloudNative #SRE #DevOps #PlatformEngineering #Relnx

 Horizontal Pod Autoscaler (HPA). Horizontal Pod Autoscaler automatically scales applications based on demand in Kubernetes. It ensures performance, reduces downtime, and optimizes resource usage without manual intervention during traffic spikes. #DevOps #Kubernetes #HPA #Autoscaling #AKS #CloudNative #K8s #CostOptimization #SRE #PlatformEngineering #Day9