CAST AI Performance Tuning
Overview
Tune CAST AI for faster node provisioning, more responsive autoscaling, and efficient API usage. Covers headroom configuration, instance family selection, and API caching for multi-cluster dashboards.
Prerequisites
- CAST AI Phase 2 (full automation) enabled
- Understanding of workload scheduling patterns
- Access to autoscaler policy configuration
Instructions
Step 1: Optimize Node Provisioning Speed
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
-H "Content-Type: application/json" \
"https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
-d '{
"enabled": true,
"unschedulablePods": {
"enabled": true,
"headroom": {
"enabled": true,
"cpuPercentage": 15,
"memoryPercentage": 15
}
}
}'
Headroom pre-provisions spare capacity so pods schedule immediately instead of waiting 2-5 minutes for new nodes.
Step 2: Instance Family Optimization
# Terraform: Prefer instance families with fast launch times
resource "castai_node_template" "fast_launch" {
cluster_id = castai_eks_cluster.this.id
name = "fast-launch-workers"
constraints {
spot = true
use_spot_fallbacks = true
fallback_restore_rate_seconds = 300
# Newer instance types launch faster and have better availability
instance_families {
include = ["m6i", "m7i", "c6i", "c7i", "r6i", "r7i"]
}
# Enable spot diversity for faster provisioning
spot_diversity_price_increase_limit_percent = 25
architectures = ["amd64"]
}
}
Step 3: Evictor Tuning for Faster Consolidation
helm upgrade castai-evictor castai-helm/castai-evictor \
-n castai-agent \
--reuse-values \
--set evictor.aggressiveMode=true \
--set evictor.cycleInterval=120
Step 4: API Performance for Multi-Cluster Dashboards
import { LRUCache } from "lru-cache";
const cache = new LRUCache<string, unknown>({ max: 100, ttl: 60_000 });
interface ClusterSummary {
id: string;
name: string;
savings: number;
savingsPercent: number;
nodeCount: number;
spotPercent: number;
}
async function getClusterSummary(clusterId: string): Promise<ClusterSummary> {
const cacheKey = `summary:${clusterId}`;
const cached = cache.get(cacheKey) as ClusterSummary | undefined;
if (cached) return cached;
const [cluster, savings, nodes] = await Promise.all([
castaiGet(`/v1/kubernetes/external-clusters/${clusterId}`),
castaiGet(),
(),
]);
spotNodes = nodes..(
n. ===
).;
: = {
: clusterId,
: cluster.,
: savings.,
: savings.,
: nodes..,
: nodes.. >
? (spotNodes / nodes..) *
: ,
};
cache.(cacheKey, summary);
summary;
}
(): <[]> {
.(clusterIds.(getClusterSummary));
}
Step 5: Workload Autoscaler Tuning
metadata:
annotations:
autoscaling.cast.ai/cpu-headroom: "10"
autoscaling.cast.ai/memory-headroom: "15"
autoscaling.cast.ai/apply-type: "immediate"
Performance Benchmarks
| Metric | Default | Tuned |
|---|
| Node provision time | 3-5 min | 1-3 min (with headroom) |
| Empty node removal | 5 min | 2 min (aggressive evictor) |
| Workload resize | 5 min cooldown | Immediate |
| API response (cached) | 200ms | <5ms |
Error Handling
| Issue | Cause | Solution |
|---|
| Headroom over-provisioning | Percentage too high | Reduce to 5-10% |
| Aggressive evictor causing disruptions | PDB not set | Add PodDisruptionBudgets |
| Cache stale data | TTL too long | Reduce cache TTL to 30s |
| Instance type unavailable | Too narrow constraints | Add more instance families |
Resources
Next Steps
For cost optimization strategies, see castai-cost-tuning.
