CAST AI Performance Guardrail Tuning

Overview

Tune from evidence across workload demand, requests, replicas, scheduling, and nodes. Keep vertical, horizontal, and node changes separate so a lower bill never hides degraded service.

Prerequisites

• Workload SLOs, error budget, traffic profile, and representative observation window
• Effective scaling policy, annotations, HPA, PDB, node templates, and cluster limits
• Metrics-server and healthy CAST AI components

Instructions

Step 1: Build the timeline

Use Read and Grep to align request rate, latency, errors, pod requests, replicas, pending time, evictions, node provisioning, and policy changes. Identify whether the symptom precedes or follows CAST AI action.

Step 2: Inspect effective workload policy