Scale to Zero on GKE with KEDA: A Game-Changer for Kubernetes Cost Optimization

Introduction: Why Scaling to Zero Matters for Modern Workloads

For startup CTOs and DevOps engineers, managing Kubernetes costs efficiently is critical. Google Kubernetes Engine (GKE) offers powerful auto-scaling features, but scaling workloads to zero—completely shutting them down when idle—is not natively supported.

This is where Kubernetes Event-driven Autoscaler (KEDA) comes in. By integrating KEDA with GKE, teams can:

Reduce infrastructure costs by shutting down idle workloads
Automatically restart services when needed, without manual intervention
Optimize resource utilization for event-driven applications

In this guide, we’ll explore why scaling to zero matters, how KEDA works, and real-world use cases to help you implement this strategy effectively.


1. What is KEDA? How It Enables Scale-to-Zero on GKE

💡 Why It Matters: GKE’s default autoscaler adjusts node pools dynamically, but it doesn’t support scaling workloads to zero. KEDA fills this gap.

What is KEDA?

KEDA (Kubernetes Event-driven Autoscaler) is an open-source project that enhances Kubernetes’ auto-scaling capabilities. Unlike the Horizontal Pod Autoscaler (HPA)—which only scales workloads based on CPU and memory metrics—KEDA triggers scaling based on external events, such as:

Pub/Sub messages
Incoming HTTP requests
Database queue depth
Kafka or RabbitMQ event triggers

How KEDA Enables Scale-to-Zero

1️⃣ Listens for external events (e.g., incoming HTTP requests or Pub/Sub messages)
2️⃣ Triggers pod deployment when an event occurs
3️⃣ Scales pods back to zero when no events are detected

🚀 Quick Win: Install KEDA on your GKE cluster and configure event-driven autoscaling in minutes. Learn more here.


2. Key Use Cases for Scaling to Zero with KEDA

💡 Why It Matters: Scaling to zero isn’t just about saving money—it’s about optimizing performance and automation for specific workloads.

Use Case 1: Pub/Sub-Based Workloads

📌 Scenario: A Kubernetes service processes Google Pub/Sub messages. When no messages are available, running idle pods wastes resources.
Solution: Use KEDA’s Pub/Sub Scaler to automatically scale the deployment down to zero when the queue is empty and spin up pods only when messages arrive.

Use Case 2: GPU-Intensive AI/ML Inference

📌 Scenario: AI/ML workloads require expensive GPU resources, even when idle.
Solution: KEDA’s HTTP scaler can shut down inference workloads when no requests exist and spin them back up only when needed.

Use Case 3: Staging & Development Environments

📌 Scenario: Developers create temporary Kubernetes environments for testing, but these remain active longer than necessary.
Solution: Implement auto-scaling to zero with KEDA to ensure these environments shut down automatically when idle.

🚀 Quick Win: Configure KEDA ScaledObjects to automatically scale down services during off-hours and reactivate them when needed.


3. How to Implement Scale-to-Zero with KEDA on GKE

💡 Why It Matters: Implementing KEDA is straightforward, but ensuring it integrates well with existing Kubernetes scaling policies is key.

Step 1: Install KEDA on Your GKE Cluster

bash
 
kubectl apply -f https://github.com/kedacore/keda/releases/latest/download/keda.yaml

This deploys KEDA’s controller and admission webhook to your cluster.

Step 2: Create a ScaledObject for Your Workload

A ScaledObject defines when and how KEDA scales a Kubernetes deployment. Here’s an example for Google Pub/Sub scaling:

yaml
apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: pubsub-worker-scaler spec: scaleTargetRef: name: my-pubsub-worker minReplicaCount: 0 maxReplicaCount: 10 triggers: - type: gcp-pubsub metadata: subscriptionName: my-subscription credentialsFromEnv: GOOGLE_APPLICATION_CREDENTIALS

This configuration scales pods from 0 to 10 based on Pub/Sub message volume.

Step 3: Validate & Monitor Scaling Behavior

Use kubectl to track pod scaling:

bash
 
kubectl get hpa,scaledobject

Ensure KEDA is correctly scaling services based on incoming events.

🚀 Quick Win: Implement scale-to-zero for one microservice and track cost savings.


4. Pros & Cons of Scaling to Zero with KEDA

💡 Why It Matters: While scaling to zero offers cost savings, it introduces challenges like cold start latency and state management.

✅ Pros

Major cost savings (No idle compute costs)
Event-driven, automated scaling
Works with multiple event sources (HTTP, Pub/Sub, Kafka, RabbitMQ, etc.)
Seamless integration with GKE

❌ Cons

Cold starts → Expect a delay when pods scale from zero.
State management challenges → In-memory state is lost when services shut down.
Not ideal for always-on applications that require low latency.

🚀 Quick Win: Optimize KEDA workloads by caching warm startup containers to reduce cold start latency.


5. Alternatives to Scaling to Zero in GCP

While KEDA is a great fit for event-driven applications, GCP offers other scaling options:

1️⃣ Cloud Run (Best for Stateless Apps)

Auto-scales down to zero
✅ Ideal for serverless HTTP-based workloads
✅ Fully managed with built-in logging & monitoring

2️⃣ GKE Autopilot (Fully Managed Kubernetes Scaling)

Automatically provisions & optimizes resources
No need to manage nodes or scale policies
✅ Good for standard workloads that don’t need scale-to-zero

3️⃣ Compute Engine Preemptible & Spot VMs (For Batch Jobs)

Auto-terminates workloads when not needed
Lower costs than standard VMs
✅ Best for batch processing or non-critical workloads

🚀 Quick Win: Compare KEDA vs. Cloud Run for your event-driven workloads.


Conclusion: Unlock Cost Efficiency with KEDA on GKE

Scaling to zero is a game-changer for cost optimization in Kubernetes. By integrating KEDA with GKE, teams can:

Eliminate idle infrastructure costs
Scale services dynamically based on demand
Leverage event-driven autoscaling for efficiency

At Buoyant Cloud, we help startups implement smart autoscaling strategies on GCP.

💡 Want to optimize Kubernetes costs with KEDA?
📩 Book a free consultation with our GCP experts today!