Kubernetes 1.37 HPA Scale to Zero: External Metrics Setup and Gotchas
Configure Kubernetes 1.37 HorizontalPodAutoscaler minReplicas 0 with external metrics, understand ScaledToZero, and avoid workloads that never wake up.
35 articles
Configure Kubernetes 1.37 HorizontalPodAutoscaler minReplicas 0 with external metrics, understand ScaledToZero, and avoid workloads that never wake up.
Use the Kubernetes 1.37 PVC Unused condition and lastTransitionTime to identify idle volumes without turning a useful signal into unsafe automatic deletion.
Terraform plan output tells you what will change, not what it'll cost. Build a bot that comments the estimated monthly cost delta directly on every infrastructure PR, with Claude API explaining which specific resources drive the change.
Namespace ResourceQuotas either get set once and forgotten (too tight, blocking legitimate scaling) or left unset entirely (no protection against a runaway team). Build a tool that recommends per-namespace quotas from actual usage patterns with Claude API.
HorizontalPodAutoscaler scales up fine but never scales back down, leaving you paying for pods you don't need? Here is exactly how to diagnose stabilization windows, metric server lag, and pod disruption budgets blocking scale-down.
Detecting a cloud cost spike is the easy part. Build an agent that investigates the anomaly, identifies the specific orphaned resource or misconfiguration causing it with Claude API, and safely remediates the low-risk cases automatically.
Capacity planning across dozens of Kubernetes clusters used to mean spreadsheets and quarterly guesswork. AI agents that correlate usage trends across clusters, predict when a cluster will run out of headroom, and recommend rebalancing are moving from research to real platform teams in 2026.
Build a tool that scans untagged or inconsistently tagged AWS resources, infers the correct team/project/environment tags from naming patterns and context, and opens a PR to apply them — closing the FinOps visibility gap without a manual tagging sprint.
Use Claude API and AWS Cost Explorer data to build an AI tool that forecasts your cloud infrastructure costs for the next 30-90 days, identifies cost drivers, and recommends optimization actions before the bill arrives.
Process thousands of LLM requests at 50% lower cost using Anthropic's Message Batches API. Complete guide with Python implementation, error handling, polling patterns, and production use cases for DevOps automation.
Build an AI tool using Claude API that analyzes your Kubernetes pod resource requests and limits, identifies over-provisioned workloads, and generates right-sized recommendations — saving 20-40% on cloud costs.
Build a multi-step AI agent using Claude API and LangGraph that analyzes your AWS costs, identifies waste, and autonomously applies rightsizing recommendations — cutting cloud bills by 20-40% with minimal human involvement.
Step-by-step tutorial to build an AI-powered AWS cost anomaly detector using Claude API and AWS Cost Explorer. Automatically identify unusual spending patterns, find the responsible service, and get plain-English explanations with fix recommendations.
Honest hands-on review of Kubecost and OpenCost for Kubernetes cost allocation. What each tool actually does, installation via Helm, free tier limits, and when to pay for Kubecost.
Honest comparison of Hetzner, AWS, and DigitalOcean for DevOps teams. Real pricing for a 3-node Kubernetes cluster, what you give up with Hetzner, and when each platform is the right choice.
How to control LLM costs at scale — token counting, prompt compression, semantic caching with Redis, tiered model routing, and cost attribution dashboards. Python code included.
Compare Cloudflare R2, AWS S3, and Backblaze B2 pricing, egress fees, S3 compatibility, and performance to choose the right object storage service.
Automatically detect unusual cloud cost spikes, identify the cause, and get an AI-generated explanation and recommended fix using Claude API and Prometheus metrics.
One Anthropic API key, ten teams using it, one bill at the end of the month with no idea who spent what. Here's how to attribute LLM costs per team, per feature, and per customer so finance can actually chargeback.
OpenCost is the open source CNCF project; Kubecost is the company that built it, offering a paid product on top. Here's what you actually get with each, and when the free tier stops being enough.
Reactive autoscaling fixes problems after they happen. Build a forecasting tool using Facebook's Prophet library on historical Prometheus metrics to predict capacity needs days ahead — before traffic spikes hit.
Your Kubernetes cluster is probably wasting 40-60% of its compute cost on over-provisioned resources. Build an AI-powered cost optimizer that reads Prometheus metrics and gives specific rightsizing recommendations.
FinOps keeps showing up in job descriptions and team meetings. Here's what it actually means, what DevOps engineers need to know about it, and practical techniques to implement it.
Use AI to automatically analyze your Kubernetes resource usage, detect waste, and generate optimization recommendations. Full Python project with Claude API.
Before you run terraform apply, wouldn't you want to know how much it'll cost? Build an AI cost estimator that reads your Terraform plan output and gives you a detailed cost breakdown using Claude as the reasoning engine.
AWS Cost Anomaly Detection catches spikes but gives no context. Build a system that detects anomalies, uses Claude to explain what caused them, and posts actionable Slack alerts with a fix recommendation.
Build an AI-powered bot that analyzes your Kubernetes cluster, finds idle resources, oversized pods, and unused namespaces — and gives cost-cutting recommendations.
Karpenter replaces Cluster Autoscaler with faster, more cost-efficient node provisioning. Learn architecture, NodePools, disruption budgets, Spot integration, and production best practices.
Everything you need to know about Kubernetes VPA. Covers installation, recommendation modes, right-sizing strategies, VPA vs HPA, and production best practices for resource optimization.
How to use AI and machine learning for Kubernetes capacity planning. Covers predictive autoscaling, cost optimization, tools like StormForge and Kubecost, and building custom ML models for resource forecasting.
How AI and LLMs are being used to analyze cloud spending, right-size resources, detect waste, and automate cost optimization across AWS, GCP, and Azure in 2026.
Cloud vendors are raising prices due to AI infrastructure costs. Here's a practical FinOps guide with specific strategies to cut your cloud bill by 30-50% in 2026.
NVIDIA has dominated GPU computing in Kubernetes for years. But AMD, Intel, and custom accelerators are breaking that monopoly. Here's why GPU diversification is inevitable.
Cloud costs are out of control at most companies. FinOps is the discipline that fixes it — and DevOps engineers are the most important people in any FinOps implementation. Here is everything you need to know.
Running Kubernetes in production can get expensive fast. Here are 10 battle-tested strategies to cut your K8s cloud bill by 40–70% without sacrificing reliability.