The Cast AI blog
Guides, tutorials, and tips on Kubernetes automation, from cost optimization to cloud security and everything in between.
Karpenter Best Practices: 10 Tips for Production Clusters
Karpenter’s defaults aren’t production-ready. This guide covers 10 specific practices to prevent real cluster failures: SQS interruption handling, NodePool isolation, disruption budgets, AMI pinning, and Prometheus…
Demystifying Quantizations: Guide to Quantization Methods for LLMs
Quantization is key to running large language models efficiently, balancing accuracy, memory, and cost. This…
Kubernetes Requests and Limits: How to Right-Size Pods Without Breaking Reliability
In Kubernetes, requests define the resources a pod is scheduled for, while limits cap usage.…
LLM Inference Cost Optimization: Run AI Inference for Less
Most LLM inference spend is idle GPU capacity. This guide covers five concrete optimization levers…
Kubernetes Spot Instances: How to Cut Compute Costs Without Gambling on Reliability
Spot Instances cut compute cost sharply but can be reclaimed with little notice. Used for…
What Is EKS Auto Mode? Managed Karpenter Node Autoscaling
EKS Auto Mode delivers Karpenter-powered node autoscaling without managing the controller. It reduces operational overhead…
Karpenter Disruption and Drift: How to Consolidate Nodes Safely
Karpenter disruption is how Karpenter removes or replaces nodes: through consolidation, drift, and expiration. Drift…
Karpenter Best Practices for Cost, Reliability, and Safe Scaling
Karpenter best practices balance cost and reliability: design focused NodePools, prefer spot with safe fallback,…
Karpenter Spot Instances: Cut Cost Without Interruptions
Karpenter can provision Spot Instances by setting the capacity type in a NodePool, which cuts…
How to Migrate from Cluster Autoscaler to Karpenter
Migrating from Cluster Autoscaler to Karpenter means replacing managed node groups with Karpenter NodePools that…
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