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🚀 Autoscaling in Kubernetes: HPA vs VPA vs KEDA — Explained from Basics to Pro

When you run applications in Kubernetes, one of your biggest concerns is:

“How do I scale my app to handle more traffic automatically — without over-provisioning?”

That’s where Kubernetes autoscaling comes in.

In this blog, we’ll dive deep into HPA, VPA, and KEDA — the three most important autoscaling mechanisms in the Kubernetes world. You’ll learn:

• What each one does
• When (and when not) to use them
• How they compare
• Real-world examples
• YAML samples and architecture diagrams

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☸️ What is Autoscaling in Kubernetes?

Autoscaling lets your Kubernetes cluster adjust workloads dynamically based on metrics like:

• CPU or memory usage
• Queue depth
• Number of requests
• Custom metrics
• External events (messages, schedules)

Without autoscaling, you’d have to manually add or remove pods, which defeats the whole point of container orchestration.

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🔄 1. Horizontal Pod Autoscaler (HPA)

📌 What is HPA?

HPA automatically adds or removes pods in a deployment, replica set, or stateful set based on CPU usage, memory usage, or custom metrics.

Think of it as:

“When load increases, spin up more pods. When load drops, scale down.”

✅ Use Case:

• Web apps with fluctuating user traffic
• APIs with request-based workloads

🔧 How It Works:

• HPA controller watches pod metrics (from metrics-server)
• Compares current usage with the target
• Adjusts replicas up or down

🧪 Example YAML:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: webapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: webapp
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 60

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📏 2. Vertical Pod Autoscaler (VPA)

📌 What is VPA?

While HPA scales out (pods), VPA adjusts the resources of a single pod — it increases or decreases CPU and memory requests/limits for containers.

“Make the pod stronger instead of multiplying it.”

✅ Use Case:

• Backend jobs or batch workloads
• ML training tasks
• Apps with fluctuating but non-concurrent loads

🔧 How It Works:

• VPA monitors pod performance
• Suggests or updates resource settings
• Can either just “recommend” or “automatically apply” changes (updateMode)

🧪 Example YAML:

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: myapp-vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind:       Deployment
    name:       myapp
  updatePolicy:
    updateMode: "Auto"

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⚡ 3. KEDA (Kubernetes-based Event-Driven Autoscaler)

📌 What is KEDA?

KEDA enables event-driven scaling in Kubernetes — scaling based on:

• Kafka topic lag
• RabbitMQ queue depth
• Azure Blob count
• AWS SQS, Google Pub/Sub, Prometheus queries, etc.

It’s perfect for workloads that don’t rely on CPU or memory but respond to external triggers.

✅ Use Case:

• Serverless, event-driven apps
• Message consumers, background workers
• Event-based microservices (e.g., IoT, stream processors)

🔧 How It Works:

• Uses Scalers (prebuilt integrations)
• Deploys a ScaledObject to define autoscaling logic
• Works with HPA under the hood, but enables external metrics

🧪 Example YAML (for Kafka):

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: kafka-consumer-scaler
spec:
  scaleTargetRef:
    name: kafka-consumer
  pollingInterval: 30
  cooldownPeriod: 60
  minReplicaCount: 1
  maxReplicaCount: 20
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: my-cluster-kafka:9092
      topic: my-topic
      lagThreshold: "100"

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🔍 Comparison: HPA vs VPA vs KEDA

Feature	HPA	VPA	KEDA
Scales By	Pod count	CPU/memory limits	Event metrics
Direction	Horizontal	Vertical	Horizontal
Metrics Type	Resource/Custom	Resource usage	External sources (Kafka, SQS)
Update Frequency	Constant	Scheduled or trigger-based	Event-based
Use With	Web apps, APIs	Batch jobs, DBs	Message queues, IoT, Serverless
Built-In?	✅ Native	✅ Native	❌ External (install via Helm)

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🧠 Can You Combine Them?

Yes!

✅ HPA + VPA (with caveats):

• You can run both, but HPA scales pods while VPA changes resource requests
• Might need tuning to avoid conflicts

✅ KEDA + HPA:

• KEDA uses HPA under the hood with external triggers
• You can also use KEDA + VPA for fine-grained control

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💼 Real-World Scenarios

Scenario	Best Autoscaler
E-commerce site scaling with traffic	HPA
ML model training with dynamic resource needs	VPA
Kafka-based order processing system	KEDA
Hybrid pipeline (e.g., APIs + queues)	HPA + KEDA
Cost optimization for idle apps	VPA + KEDA

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🔐 Gotchas & Best Practices

Tip	Why It Matters
Always set minReplica	Prevent scaling to zero unexpectedly
Use resource limits	VPA relies on actual usage for decisions
Monitor HPA cooldowns	Too aggressive = flapping pods
Don’t use HPA on CPU-bound single-threaded apps	Won’t scale as expected
Use Prometheus Adapter for HPA custom metrics	Extend beyond CPU/memory
Use KEDA for event-driven use cases	Don’t force HPA where it doesn’t fit

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📦 Tools & Resources

Tool	Description
metrics-server	Required for HPA/VPA to collect pod metrics
KEDA	Install via Helm or kubectl
Prometheus Adapter	Use Prometheus metrics for HPA
Vertical Pod Autoscaler	Enable via admission controller
Lens / K9s	Visualize autoscaling in real time

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🏁 Final Thoughts

Kubernetes autoscaling isn’t one-size-fits-all.

• Use HPA when you care about resource usage.
• Use VPA when you want to tune performance inside pods.
• Use KEDA when your system responds to events, not load.

Mastering autoscaling helps you deliver apps that are not only resilient, but also cost-efficient and responsive to real-world usage.