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Most LLM inference spend is idle GPU capacity. This guide covers five concrete optimization levers -- GPU sharing, continuous batching, quantization, right-sizing, and autoscaling -- with specific configs, tools, and metrics for each. Includes a working KEDA ScaledObject for vLLM queue-depth autoscaling. The post LLM Inference Cost Optimization: Run AI Inference for Less appeared first on Cast AI .

Spot Instances cut compute cost sharply but can be reclaimed with little notice. Used for fault-tolerant workloads with automated fallback to on-demand, they are one of the biggest purchase-cost levers once utilization is fixed. The post Kubernetes Spot Instances: How to Cut Compute Costs Without Gambling on Reliability appeared first on Cast AI .

EKS Auto Mode delivers Karpenter-powered node autoscaling without managing the controller. It reduces operational overhead while trading some control and adding a per-resource fee. Self-managed Karpenter remains the better choice for teams that need maximum flexibility and cost optimization. The post What Is EKS Auto Mode? Managed Karpenter Node Autoscaling appeared first on Cast AI .

Karpenter disruption is how Karpenter removes or replaces nodes: through consolidation, drift, and expiration. Drift occurs when a node no longer matches its NodePool or NodeClass spec. Disruption budgets and the do-not-disrupt annotation control how aggressively Karpenter acts, so you get cost savings without destabilizing workloads. The post Karpenter Disruption and Drift: How to Consolidate Nodes Safely appeared first on Cast AI .

Karpenter best practices balance cost and reliability: design focused NodePools, prefer spot with safe fallback, enable consolidation with disruption budgets, set sensible limits, and use well-known labels and taints to control placement. Done well, Karpenter cuts node cost while keeping workloads stable. The post Karpenter Best Practices for Cost, Reliability, and Safe Scaling appeared first on Cast AI .

Karpenter can provision Spot Instances by setting the capacity type in a NodePool, which cuts compute cost sharply. The risk is interruption, so the pattern is to diversify instance types, handle the spot interruption signal gracefully, and fall back to on-demand when spot is unavailable, returning to spot when capacity stabilizes. The post Karpenter Spot Instances: Cut Cost Without Interruptions appeared first on Cast AI .

Migrating from Cluster Autoscaler to Karpenter means replacing managed node groups with Karpenter NodePools that provision nodes just in time. The migration is incremental: install Karpenter alongside Cluster Autoscaler, map your node groups to NodePools, shift workloads, then remove the old groups, with a clear rollback path at each step. The post How to Migrate from Cluster Autoscaler to Karpenter appeared first on Cast AI .

GPU cloud pricing varies widely by provider, region, and instance type, and it moves with AI demand. This data-led guide summarizes 2026 GPU pricing across the major clouds, including H100 and H200, drawing on our Kubernetes GPU Trends and Cost Report, so teams can plan AI infrastructure cost realistically. The post GPU Cloud Pricing in 2026: What AI Compute Really Costs appeared first on Cast AI .

GPU cost monitoring tracks how much of each expensive GPU is actually used and attributes that cost to the teams, workloads, and applications consuming it. Since GPUs average about 5% utilization, visibility is the first step: you cannot reclaim idle GPU spend you cannot see or assign to an owner. The post GPU Cost Monitoring: Track Utilization and Attribute AI Spend appeared first on Cast AI .

Multi-cloud GPU capacity lets a single Kubernetes cluster source scarce GPUs, TPUs, and CPU from any cloud or region through one control plane, so AI inference and batch workloads run wherever capacity is available without application code changes. This is how teams deploy more AI on fewer GPUs and get past regional shortages. The post Multi-Cloud and Cross-Region GPU Capacity for Kubernetes AI appeared first on Cast AI .

A Kubernetes node in NotReady state has stopped reporting healthy to the control plane, so it cannot run new pods and may evict existing ones. Causes include a failed or stopped kubelet, network or CNI problems, resource pressure (memory, disk, PID), or a cloud instance issue. This guide diagnoses and fixes each. The post Kubernetes Node NotReady: Why Nodes Go NotReady and How to Fix It appeared first on Cast AI .