Andrii Bidochko

• Updated: March 18, 2026
• 5 min read

Configuring Horizontal Pod Autoscaling and Cluster Autoscaler for the OpenClaw Rating API

To efficiently handle traffic spikes for the OpenClaw Rating API, configure both Horizontal Pod Autoscaling (HPA) and the Cluster Autoscaler on your Kubernetes cluster, then fine‑tune them for cost‑effective scaling.

1. Introduction

Developers and DevOps engineers building the OpenClaw Rating API often face unpredictable request volumes. Without automatic scaling, you risk either throttling users or over‑provisioning resources, which inflates cloud bills. This guide walks you through a step‑by‑step setup of Horizontal Pod Autoscaling and the Cluster Autoscaler on a Kubernetes cluster, highlights best‑practice tips, and shares cost‑optimization strategies that align with the UBOS homepage philosophy of “scale smart, spend less.”

2. Prerequisites

• Kubernetes v1.21+ with kubectl access.
• Metrics Server installed (required for HPA).
• Cluster Autoscaler compatible with your cloud provider (AWS, GCP, Azure, or on‑prem).
• Docker image of the OpenClaw Rating API pushed to a registry.
• Basic familiarity with Kubernetes pods.

3. Setting up Horizontal Pod Autoscaler (HPA)

3.1. Deploy the OpenClaw Rating API

First, create a Deployment that runs the API. Save the following manifest as openclaw-deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: openclaw-rating-api
spec:
  replicas: 2
  selector:
    matchLabels:
      app: openclaw
  template:
    metadata:
      labels:
        app: openclaw
    spec:
      containers:
      - name: rating-api
        image: your-registry/openclaw-rating-api:latest
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: "250m"
            memory: "256Mi"
          limits:
            cpu: "500m"
            memory: "512Mi"

Apply the manifest:

kubectl apply -f openclaw-deployment.yaml

3.2. Expose the Service

apiVersion: v1
kind: Service
metadata:
  name: openclaw-service
spec:
  selector:
    app: openclaw
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: LoadBalancer

kubectl apply -f openclaw-service.yaml

3.3. Create the HPA Object

The HPA will scale pods based on CPU utilization (you can also use custom metrics). Save this as openclaw-hpa.yaml:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: openclaw-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: openclaw-rating-api
  minReplicas: 2
  maxReplicas: 15
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 60

kubectl apply -f openclaw-hpa.yaml

3.4. Verify HPA Status

kubectl get hpa openclaw-hpa

You should see something like:

NAME          REFERENCE                     TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
openclaw-hpa  Deployment/openclaw-rating-api  60%/60%   2         15        2          5m

3.5. Best‑practice Tips for HPA

• Set realistic CPU targets. A 60‑70% target balances responsiveness and cost.
• Use autoscaling/v2 API. It supports multiple metrics (CPU, memory, custom).
• Warm‑up periods. Add behavior policies to avoid rapid scaling flaps.
• Test with load generators. Tools like k6 help you validate thresholds.
• Monitor with Prometheus. Correlate HPA decisions with real‑world latency.

4. Configuring Cluster Autoscaler

4.1. Install the Cluster Autoscaler

Choose the version that matches your Kubernetes distribution. Below is an example for an AWS EKS cluster:

kubectl apply -f https://github.com/kubernetes/autoscaler/releases/download/cluster-autoscaler-1.23.0/cluster-autoscaler-autodiscover.yaml

After deployment, edit the cluster-autoscaler deployment to add your node group name:

kubectl edit deployment cluster-autoscaler -n kube-system

Append the following flag to the command array (replace my-node-group with your actual group):

--nodes=2:10:my-node-group

4.2. Enable Scale‑Down and Scale‑Up Policies

Fine‑tune the autoscaler with a ConfigMap (optional but recommended):

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-autoscaler-config
  namespace: kube-system
data:
  cluster-autoscaler.yaml: |
    scaleDown:
      enabled: true
      delayAfterAdd: 10m
      delayAfterDelete: 1m
      delayAfterFailure: 3m
    scaleUp:
      enabled: true
      delayAfterAdd: 1m

kubectl apply -f cluster-autoscaler-config.yaml

4.3. Verify Cluster Autoscaler Activity

kubectl logs -f deployment/cluster-autoscaler -n kube-system

Look for log lines such as Scale-up: adding 2 nodes or Scale-down: removing node.

4.4. Best‑practice Tips for Cluster Autoscaler

• Set appropriate min/max node counts. Prevent runaway scaling.
• Use multiple instance types. Improves bin‑packing and reduces cost.
• Enable --balance-similar-node-groups. Keeps workloads evenly distributed.
• Tag nodes with purpose. Helps you apply pod‑affinity rules for critical services.
• Monitor with CloudWatch or GCP Monitoring. Alerts on unexpected scale‑up events.

5. Cost‑Optimization Considerations

Scaling automatically is powerful, but unchecked scaling can explode your cloud spend. Follow these cost‑saving tactics:

For a holistic view of cost‑impact, pair the autoscaling metrics with the UBOS pricing plans calculator. This helps you forecast monthly spend based on projected request volume.

6. Embedding the Internal Link

If you plan to host the OpenClaw Rating API on a managed environment, UBOS offers a dedicated hosting solution. Learn more about the OpenClaw hosting package that bundles autoscaling, monitoring, and security patches.

7. Publishing the Article on UBOS

Once the guide is ready, follow these steps to publish it on the UBOS homepage blog platform:

1. Log in to the UBOS admin console.
2. Navigate to Content → New Post.
3. Paste the HTML content into the editor. Ensure the <body> wrapper is retained.
4. Set the SEO meta title to “Configuring Horizontal Pod Autoscaling and Cluster Autoscaler for the OpenClaw Rating API”.
5. Add the primary keyword “Horizontal Pod Autoscaling” in the meta description.
6. Select relevant tags: Kubernetes, Autoscaling, OpenClaw, DevOps.
7. Enable the “Featured” flag to surface the post on the UBOS portfolio examples page.
8. Click Publish and verify the article renders correctly on mobile and desktop.

8. Conclusion

By combining Horizontal Pod Autoscaling with the Cluster Autoscaler, you give the OpenClaw Rating API the agility to respond to traffic bursts while keeping cloud spend under control. Apply the best‑practice tips, monitor cost metrics, and leverage UBOS tools such as the AI marketing agents for automated alerts on scaling anomalies. With a well‑tuned autoscaling pipeline, your API stays performant, resilient, and cost‑effective—exactly what modern SaaS teams need.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.