AI Server docs / Kubernetes & Helm

Kubernetes & Helm

The production topology: an AI Gateway Deployment fronting a StatefulSet of workers, discovered by DNS, licensed by a Secret, upgraded with zero downtime.

Install with Helm (recommended)

helm repo add softwaretailor https://softwaretailor.com/charts
helm repo update

kubectl create namespace aisuite
kubectl -n aisuite create secret generic aisuite-license --from-literal=key=<your-license-key>
kubectl -n aisuite create secret generic aisuite-worker-keys --from-file=server-keys.json

helm install aisuite softwaretailor/aisuite -n aisuite \
  --set license.existingSecret=aisuite-license

The chart deliberately refuses to render configurations that would install cleanly and then not work — no license, no API keys, an autoscaler pointed at a static pool, CPU-based autoscaling on GPU workers, a real engine with no GPU. If helm install rejects your values, the message tells you which invariant you broke; that is the chart doing its job.

Kustomize users: the same topology ships as reference manifests (base + autoscale and gpu overlays) in the public deploy repository — kubectl apply -k after replacing the placeholder Secrets.

Why the topology looks the way it does

The zero-downtime contract

livenessProbe:
  httpGet: { path: /livez, port: 8080 }
  periodSeconds: 10
readinessProbe:
  httpGet: { path: /readyz, port: 8080 }
  periodSeconds: 5
terminationGracePeriodSeconds: 40   # must be >= drain hold + shutdown budget

On SIGTERM the daemon flips /readyz to 503, holds for the drain window (AISUITE_DRAIN_SECONDS, default 8) so load balancers deregister it, then finishes in-flight requests within the shutdown budget (AISUITE_SHUTDOWN_SECONDS, default 30). Keep terminationGracePeriodSeconds at or above the sum, or the kubelet SIGKILLs a pod mid-drain — that single misconfiguration is the most common cause of dropped requests during rollouts.

Autoscaling

GPU nodes

Operational invariants worth pinning

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