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
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:
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:
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!