Autoscale KAITO inference workloads on AKS using KEDA

Kubernetes AI Toolchain Operator (KAITO) is an operator that simplifies and automates AI/ML model inference, tuning, and RAG in a Kubernetes cluster. With the recent v0.8.0 release, KAITO has introduced intelligent autoscaling for inference workloads as an alpha feature! In this blog, we'll guide you through setting up event-driven autoscaling for vLLM inference workloads.

Introduction

LLM inference service is a basic and widely used feature in KAITO. As the number of waiting inference requests increases, scale more inference instances to prevent blocking. Conversely, reduce inference instances when requests decline to improve GPU resource utilization. Kubernetes Event Driven Autoscaling (KEDA) is well-suited for inference pod autoscaling. It enables event-driven, fine-grained scaling based on external metrics and triggers. KEDA supports a wide range of event sources (like custom metrics), allowing pods to scale precisely in response to workload demand. This flexibility and extensibility make KEDA ideal for dynamic, cloud-native applications that require responsive and efficient autoscaling.

To enable intelligent autoscaling for KAITO inference workloads using service monitoring metrics, utilize the following components and features:

• Kubernetes Event Driven Autoscaling (KEDA)

• KEDA KAITO Scaler: A dedicated KEDA external scaler, eliminating the need for external dependencies such as Prometheus.

• KAITO InferenceSet CustomResourceDefinition (CRD) and controller: A new CRD and controller were built on top of the KAITO workspace for intelligent autoscaling, introduced as an alpha feature in KAITO version v0.8.0.

Architecture

The following diagram shows how KEDA KAITO Scaler integrates KAITO InferenceSet with KEDA to autoscale inference workloads on AKS:

Getting started

Create an AKS cluster with GPU auto-provisioning capabilities for KAITO

Refer to the instructions on how to create an AKS cluster with GPU auto-provisioning capabilities for KAITO.

Enable InferenceSet controller in KAITO

The InferenceSet CRD and controller were introduced as an alpha feature in KAITO version v0.8.0. Built on top of the KAITO workspace, InferenceSet supports the scale subresource API for intelligent autoscaling. To use InferenceSet, the InferenceSet controller must be enabled during the KAITO installation.

export CLUSTER_NAME=kaito

helm repo add kaito https://kaito-project.github.io/kaito/charts/kaito
helm repo update
helm upgrade --install kaito-workspace kaito/workspace \
  --namespace kaito-workspace \
  --create-namespace \
  --set clusterName="$CLUSTER_NAME" \
  --set featureGates.enableInferenceSetController=true \
  --wait

Install KEDA

• Option 1: Enable managed KEDA add-on For instructions, refer to Install KEDA add-on on AKS

• Option 2: Install KEDA using Helm chart

The following example demonstrates how to install KEDA 2.x using Helm chart. For instructions on installing KEDA through other methods, refer to the KEDA deployment documentation.

helm repo add kedacore https://kedacore.github.io/charts
helm install keda kedacore/keda --namespace kube-system

Example Scenarios

Time-Based KEDA Scaler

The KEDA cron scaler enables scaling of workloads according to time-based schedules, making it especially beneficial for workloads with predictable traffic patterns. It is perfect for situations where peak hours are known ahead of time, allowing you to proactively adjust resources before demand rises. For more details about time-based scalers, refer to Scale applications based on a cron schedule.

Example: Business Hours Scaling

• Create a KAITO InferenceSet for running inference workloads

The following example creates an InferenceSet for the phi-4-mini model:

cat <<EOF | kubectl apply -f -
apiVersion: kaito.sh/v1alpha1
kind: InferenceSet
metadata:
  name: phi-4-mini
  namespace: default
spec:
  labelSelector:
    matchLabels:
      apps: phi-4-mini
  replicas: 1
  template:
    inference:
      preset:
        accessMode: public
        name: phi-4-mini-instruct
    resource:
      instanceType: Standard_NC24ads_A100_v4
EOF

• Create a KEDA ScaledObject

Below is an example of creating a ScaledObject that scales a KAITO InferenceSet based on business hours:

• Scale up to 5 replicas from 6:00 AM to 8:00 PM (peak hours)

• Scale down to 1 replica otherwise (off-peak hours)

cat <<EOF | kubectl apply -f -
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kaito-business-hours-scaler
  namespace: default
spec:
  # Target KAITO InferenceSet to scale
  scaleTargetRef:
    apiVersion: kaito.sh/v1alpha1
    kind: InferenceSet
    name: phi-4-mini
  # Scaling boundaries
  minReplicaCount: 1
  maxReplicaCount: 5
  # Cron-based triggers for time-based scaling
  triggers:
  # Scale up to 5 replicas at 6:00 AM (start of business hours)
  - type: cron
    metadata:
      timezone: "America/New_York"  # Adjust timezone as needed
      start: "0 6 * * 1-5"          # 6:00 AM Monday to Friday
      end: "0 20 * * 1-5"           # 8:00 PM Monday to Friday
      desiredReplicas: "5"          # Scale to 5 replicas during business hours
  # Scale down to 1 replica at 8:00 PM (end of business hours)
  - type: cron
    metadata:
      timezone: "America/New_York"  # Adjust timezone as needed
      start: "0 20 * * 1-5"         # 8:00 PM Monday to Friday
      end: "0 6 * * 1-5"            # 6:00 AM Monday to Friday (next day)
      desiredReplicas: "1"          # Scale to 1 replica during off-hours
EOF

Metric-Based KEDA Scaler

Install KEDA KAITO Scaler

This component is required only when using metric-based KEDA scaler, ensure that KEDA KAITO Scaler is installed within the same namespace as KEDA.

helm repo add keda-kaito-scaler https://kaito-project.github.io/keda-kaito-scaler/charts/kaito-project
helm upgrade --install keda-kaito-scaler -n kube-system keda-kaito-scaler/keda-kaito-scaler

After a few seconds, the keda-kaito-scaler deployment starts.

# kubectl get deployment keda-kaito-scaler -n kube-system
NAME                READY   UP-TO-DATE   AVAILABLE   AGE
keda-kaito-scaler   1/1     1            1           28h

The keda-kaito-scaler provides a simplified configuration interface for scaling vLLM inference workloads, it directly scrapes metrics from inference pods, eliminating the need for a separate monitoring stack.

Example: Create a KAITO InferenceSet with annotations for running inference workloads

• The following example creates an InferenceSet for the phi-4-mini model, using annotations with the prefix scaledobject.kaito.sh/ to supply parameter inputs for the KEDA KAITO scaler.

  • scaledobject.kaito.sh/auto-provision
    • required, when set to true, the KEDA KAITO scaler automatically provisions a ScaledObject based on the InferenceSet object
  • scaledobject.kaito.sh/max-replicas
    • required, maximum number of replicas for the target InferenceSet
  • scaledobject.kaito.sh/metricName
    • optional, specifies the metric name collected from the vLLM pod, which is used for monitoring and triggering the scaling operation, default is vllm:num_requests_waiting, find all vllm metrics in vLLM Production Metrics
  • scaledobject.kaito.sh/threshold
    • required, specifies the threshold for the monitored metric that triggers the scaling operation

cat <<EOF | kubectl apply -f -
apiVersion: kaito.sh/v1alpha1
kind: InferenceSet
metadata:
  annotations:
    scaledobject.kaito.sh/auto-provision: "true"
    scaledobject.kaito.sh/max-replicas: "5"
    scaledobject.kaito.sh/metricName: "vllm:num_requests_waiting"
    scaledobject.kaito.sh/threshold: "10"
  name: phi-4-mini
  namespace: default
spec:
  labelSelector:
    matchLabels:
      apps: phi-4-mini
  replicas: 1
  template:
    inference:
      preset:
        accessMode: public
        name: phi-4-mini-instruct
    resource:
      instanceType: Standard_NC24ads_A100_v4
EOF

In just a few seconds, the KEDA KAITO scaler automatically creates the scaledobject and hpa objects. After a few minutes, once the inference pod runs, the KEDA KAITO scaler begins scraping metric values from the inference pod. The system then marks the status of the scaledobject and hpa objects as ready.

# kubectl get scaledobject
NAME           SCALETARGETKIND                  SCALETARGETNAME   MIN   MAX   READY   ACTIVE    FALLBACK   PAUSED   TRIGGERS   AUTHENTICATIONS           AGE
phi-4-mini     kaito.sh/v1alpha1.InferenceSet   phi-4-mini        1     5     True    True     False      False    external   keda-kaito-scaler-creds   10m

# kubectl get hpa
NAME                    REFERENCE                   TARGETS      MINPODS   MAXPODS   REPLICAS   AGE
keda-hpa-phi-4-mini     InferenceSet/phi-4-mini     0/10 (avg)   1         5         1          11m

That's it! Your KAITO workloads will now automatically scale based on the average number of waiting inference requests(vllm:num_requests_waiting) across all workloads associated with InferenceSet/phi-4-mini in the cluster.

In the example below, if vllm:num_requests_waiting exceeds the threshold (10) for over 60 seconds, KEDA scales up by adding a new replica to InferenceSet/phi-4-mini. Conversely, if vllm:num_requests_waiting remains below the threshold (10) for more than 300 seconds, KEDA scales down the number of replicas.

Every 2.0s: kubectl describe hpa
Name:                                                     keda-hpa-phi-4-mini
Namespace:                                                default
Labels:                                                   app.kubernetes.io/managed-by=keda-operator
                                                          app.kubernetes.io/name=keda-hpa-phi-4-mini
                                                          app.kubernetes.io/part-of=phi-4-mini
                                                          app.kubernetes.io/version=2.18.1
                                                          scaledobject.keda.sh/name=phi-4-mini
Annotations:                                              scaledobject.kaito.sh/managed-by: keda-kaito-scaler
CreationTimestamp:                                        Tue, 09 Dec 2025 03:35:09 +0000
Reference:                                                InferenceSet/phi-4-mini
Metrics:                                                  ( current / target )
  "s0-vllm:num_requests_waiting" (target average value):  58 / 10
Min replicas:                                             1
Max replicas:                                             5
Behavior:
  Scale Up:
    Stabilization Window: 60 seconds
    Select Policy: Max
    Policies:
      - Type: Pods  Value: 1  Period: 300 seconds
  Scale Down:
    Stabilization Window: 300 seconds
    Select Policy: Max
    Policies:
      - Type: Pods  Value: 1  Period: 600 seconds
InferenceSet pods:  2 current / 2 desired
Conditions:
  Type            Status  Reason            Message
  ----            ------  ------            -------
  AbleToScale     True    ReadyForNewScale  recommended size matches current size
  ScalingActive   True    ValidMetricFound  the HPA was able to successfully calculate a replica count from external metric s0-vllm:num_requests_waiting(&Lab
elSelector{MatchLabels:map[string]string{scaledobject.keda.sh/name: phi-4-mini,},MatchExpressions:[]LabelSelectorRequirement{},})
  ScalingLimited  True    ScaleUpLimit      the desired replica count is increasing faster than the maximum scale rate
Events:
  Type    Reason             Age   From                       Message
  ----    ------             ----  ----                       -------
  Normal  SuccessfulRescale  33s   horizontal-pod-autoscaler  New size: 2; reason: external metric s0-vllm:num_requests_waiting(&LabelSelector{MatchLabels:ma
p[string]string{scaledobject.keda.sh/name: phi-4-mini,},MatchExpressions:[]LabelSelectorRequirement{},}) above target

Summary

KAITO's LLM inference service must scale inference instances dynamically to handle varying numbers of waiting requests: scaling up to prevent blocking when requests increase, and scaling down to optimize GPU usage when requests decrease. With the newly introduced InferenceSet CRD and KEDA KAITO scaler, configuring this setting in KAITO has become much simpler.

We're just getting started and would love your feedback. To learn more about KAITO inference workloads autoscaling and AI model deployment on AKS, check out the following links:

Resources

• KEDA Auto-Scaler for inference workloads
• KAITO InferenceSet
• vLLM Production Metrics