| title | Autoscale KAITO inference workloads on AKS using KEDA | ||
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| date | 2026-01-29 | ||
| description | Learn how to autoscale KAITO inference workloads on AKS with KEDA to handle varying inference requests and optimize Kubernetes GPU utilization in AKS clusters. | ||
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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.
LLM inference service is a basic and widely used feature in KAITO. As the number of waiting inference requests increases, it's necessary to scale more inference instances to prevent blocking inference requests. Conversely, if the number of waiting inference requests declines, consider reducing inference instances 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:
-
KEDA KAITO Scaler: A dedicated KEDA external scaler, eliminating the need for external dependencies such as Prometheus.
-
KAITO
InferenceSetCustomResourceDefinition (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 versionv0.8.0.
The following diagram shows how KEDA KAITO Scaler integrates KAITO InferenceSet with KEDA to autoscale inference workloads on AKS:
You could refer to the instructions on how to create an AKS cluster with GPU auto-provisioning capabilities for 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-
Option 1: Enable managed KEDA add-on For instructions on enabling KEDA add-on on AKS, you could refer to the guide 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, please refer to the guide KEDA deployment documentation.
helm repo add kedacore https://kedacore.github.io/charts
helm install keda kedacore/keda --namespace kube-systemThe 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.
- 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
EOFThis 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-scalerAfter a few seconds, a new deployment keda-kaito-scaler would be started.
# kubectl get deployment keda-kaito-scaler -n kube-system
NAME READY UP-TO-DATE AVAILABLE AGE
keda-kaito-scaler 1/1 1 1 28hThe 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.
-
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, if it's
true, KEDA KAITO scaler will automatically provision a ScaledObject based on theInferenceSetobject
- required, if it's
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
- optional, specifies the metric name collected from the vLLM pod, which is used for monitoring and triggering the scaling operation, default is
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
EOFIn 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 11mThat'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 will scale 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 will scale 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 targetThe LLM inference service in KAITO needs to 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: