Managing application performance while keeping costs low is a challenge in cloud environments. Kubernetes autoscaling helps solve this by automatically adjusting your app’s resources based on real-time needs. Whether you're scaling pods during traffic spikes or shrinking your infrastructure during low usage, autoscaling helps maintain performance without wasting money.
In this blog, we'll break down how Kubernetes autoscaling works, the different types (HPA, VPA, and Cluster Autoscaler), and how to choose the right one for your workload. You'll also learn cost-saving strategies with real examples so you can scale smartly and stay within budget.
What is Autoscaling in Kubernetes?
Autoscaling in Kubernetes refers to the automatic adjustment of resources, pods or nodes based on the current needs of an application. Instead of manually provisioning infrastructure or adjusting replicas, Kubernetes can scale applications up during traffic spikes or down during idle periods.
This improves performance and can reduce costs if done correctly.
Autoscaling is dynamic and metrics-driven, often relying on CPU utilization, memory usage, or custom business metrics like queue depth or request count.
Types of Autoscaling in Kubernetes
There are three primary types of autoscaling available in Kubernetes:
1. Horizontal Pod Autoscaler (HPA)
- What it does: Adjusts the number of pod replicas in a deployment, replica set, or stateful set.
- Metrics used: CPU, memory, or custom metrics via the Kubernetes Metrics Server or Prometheus.
- Use case: Ideal for stateless applications like web services or APIs that handle variable traffic.
- Example: Scale from 2 to 10 pods based on 70% CPU utilization threshold.
2. Vertical Pod Autoscaler (VPA)
- What it does: Adjusts the CPU and memory requests/limits of containers within a pod.
- Modes:
- Off: Only provides recommendations.
- Auto: Applies recommendations automatically.
- Initial: Applies recommendations only at pod startup.
- Use case: Suitable for workloads with unpredictable memory/CPU needs or long-running batch jobs.
- Example: A machine learning pod that periodically spikes in memory can get updated limits based on observed usage.
3. Cluster Autoscaler (CA)
- What it does: Automatically adds/removes worker nodes in your Kubernetes cluster.
- Trigger:
- Adds nodes if pods are unschedulable due to insufficient resources.
- Removes underutilized nodes (usually idle for >10 minutes).
- Use case: Works alongside HPA to ensure infrastructure availability.
- Example: If HPA scales pods to 15 but current nodes support only 10, CA will provision more nodes to accommodate the extra pods.
How to Choose the Right Kubernetes Autoscaler for Your Workloads ?
Choosing the right autoscaler in Kubernetes depends heavily on your application's traffic patterns, resource consumption behavior, and scalability needs.
Below is a breakdown of typical scenarios and the most suitable auto scaling method for each:
Best Practice: For stateless microservices, the ideal setup is to combine HPA with Cluster Autoscaler. HPA handles application-level scaling, while CA ensures infrastructure keeps up. For resource tuning, start with VPA in recommend mode, analyze the suggestions, and gradually apply them to improve cost efficiency without risking application stability.
How Autoscaling Impacts Kubernetes Costs
Autoscaling in Kubernetes helps your apps handle more traffic by adding more pods or nodes when needed. It also removes extra resources when things quiet down. While this makes your apps more reliable, it also affects your cloud bill.
Here’s how autoscaling can increase or decrease your costs:
1. Compute Costs Increase with Scaling Up
Autoscaling typically results in additional pods being scheduled or new nodes being added to your cluster. Horizontal Pod Autoscaler (HPA) increases the number of pods when CPU, memory, or custom metrics exceed the defined threshold. Cluster Autoscaler (CA) responds when pods are pending due to insufficient node resources by provisioning new nodes.
- Example: If an application’s CPU usage spikes, HPA may increase the number of pods from 5 to 10. If your existing nodes can’t accommodate the new pods, CA might launch additional EC2 instances (on AWS), which are then billed per second or per minute depending on your pricing model.
- Impact: You’re billed for the additional vCPUs, RAM, and potentially storage on these new nodes. If the scale-up is frequent or sustained, costs can increase.
2. Idle or Over-Provisioned Resources
One of the most common cost pitfalls with autoscaling is resource wastage. Over-requesting resources (CPU/memory) for pods ,especially in static configurations or poorly tuned VPA results in higher costs without actual usage. Underutilized nodes often remain active even when the workload drops, especially if scale-down thresholds aren’t aggressive.
- Example: If VPA sets high default resource limits based on a temporary spike, your pods may reserve more CPU/memory than they need for most of the time leading to inefficient node utilization.
- Impact: Nodes that are only 30-40% utilized are still fully billed. Without right-sizing and cleanup automation, unused capacity becomes silent budget drain.
3. Scale-Up and Scale-Down Latency
Autoscaling is not instantaneous. There is a time lag involved in both scale-up and scale-down operations.
Scale-up delay: While workloads wait for new pods or nodes to be ready, performance can degrade.
Scale-down delay: Nodes and pods might continue running even after the workload reduces, leading to unnecessary billing.
- Example: Cluster Autoscaler typically waits several minutes (e.g., 10 minutes by default) before terminating an unused node. During this idle time, you're still billed for the compute resources even though your application doesn’t need them.
- Impact: Additionally, if your thresholds are set too tightly, autoscalers may scale up and down frequently (known as flapping), leading to Unstable environments, Increased load on API servers, More frequent pod restarts (causing potential downtime) as well as Higher cost due to constant provisioning and deprovisioning.
Strategies to Reduce Autoscaling Costs in Kubernetes
Now that we know how autoscaling can incur charges, let’s explore practical strategies to optimize cost:
1. Set Resource Requests and Limits Correctly
To optimize autoscaling behavior and reduce costs, it's crucial to define precise CPU and memory requests and limits for each container. Requests ensure Kubernetes schedules sufficient resources, while limits prevent excessive usage
Overestimating requests leads to unnecessary node provisioning, while underestimating risks throttling or crashes. Monitoring tools like kubectl top or Prometheus help determine realistic values.
Example Scenario
CloudNative Labs runs a high-availability app on Amazon EKS with 10 pods, each requesting 1 vCPU, using m5.large nodes (2 vCPUs, $0.096/hour). Real usage data shows each pod only consumes ~0.3 vCPU.
Current Setup (Without Optimization):
- Pods: 10
- CPU Request per Pod: 1 vCPU
- Total CPU Requested: 10 vCPUs
- Nodes Required: 10 ÷ 2 = 5
- Monthly Cost: 5 × $0.096 × 24 × 30 = $345.60
After Right-Sizing to 0.5 vCPU per Pod:
- Total CPU Requested: 10 × 0.5 = 5 vCPUs
- Nodes Required: 5 ÷ 2 = 3
- Monthly Cost: 3 × $0.096 × 24 × 30 = $207.36
Savings:
- Monthly: $345.60 − $207.36 = $138.24
- Annual (1 cluster): $138.24 × 12 = $1,658.88
- Annual (5 clusters): $1,658.88 × 5 = $8,294.40
Right-sizing resource requests based on real-time metrics enables Kubernetes to pack workloads more efficiently, reducing node count and compute costs without compromising stability or performance.
2. Use Horizontal Pod Autoscaler (HPA) with Custom Metrics
Using Horizontal Pod Autoscaler (HPA) with custom metrics allows Kubernetes to scale workloads based on application-specific indicators rather than default metrics like CPU or memory usage. This leads to smarter and more cost-effective scaling decisions. Custom metrics such as queue length, request rate, or number of active sessions help ensure that scaling occurs only when the workload truly demands it. This prevents overprovisioning, improves resource utilization, and directly reduces unnecessary compute costs.
Additionally, using custom metrics with HPA lowers the chances of triggering the Cluster Autoscaler, which adds new nodes and increases infrastructure costs. By scaling pods more precisely based on meaningful metrics, you maintain application performance while minimizing resource waste. This ensures that infrastructure usage closely aligns with actual demand, resulting in optimized reliability and cost efficiency.
Example Scenario: Food Delivery App Autoscaling
The app uses Horizontal Pod Autoscaler (HPA) with queue length as a custom metric. Each pod costs $0.10 per hour.
Current Setup (CPU-based):
- Pods run at 15 all day.
- Monthly Cost: 15 × $0.10 × 24 × 30 = $1,080
Optimized Setup (Queue length-based):
- Pods scale to 15 during peak hours and down to 5 during off-peak.
- Savings during 10 off-peak hours daily: (15 − 5) × $0.10 × 10 = $10/day
- Monthly Savings: $10 × 30 = $300
Scaling based on demand saves about $300 per month by reducing unnecessary pods during low traffic.
3. Implement Vertical Pod Autoscaler (VPA) in Recommendation Mode
VPA automatically adjusts pod resource requests (CPU and memory) based on actual usage. Many pods are often over-provisioned with resource requests that exceed their needs, which forces Kubernetes to allocate larger nodes or more nodes than necessary. By using VPA in Recommendation Mode, you get suggestions on optimal resource requests without immediately changing running pods, allowing you to manually adjust and right-size your deployments. This avoids wasted resources and reduces the cost of running oversized nodes or unnecessary pods.
Instead of paying for large CPU/memory allocations that sit idle, VPA helps tune resource requests closer to actual consumption, improving packing efficiency on nodes and potentially reducing node count or size.
4. Use Cluster Autoscaler with Node Pool Optimization
The Cluster Autoscaler dynamically adjusts your Kubernetes node count by monitoring pod scheduling needs. If pods remain unscheduled due to insufficient resources, the autoscaler adds nodes. When nodes become underutilized, it safely removes them. This ensures your cluster scales only as needed, preventing overprovisioning and idle compute waste.
Combining this with Node Pool Optimization allows you to group nodes by workload type.
For example:
Incorporating Spot or Preemptible Instances in these pools further cuts costs. You can assign non-critical or batch workloads to spot instances (cheaper but interruptible), while critical workloads run on stable on-demand nodes. This hybrid strategy balances reliability with affordability.
5. Use Spot or Preemptible Instances for Non-Critical Workloads
Spot and preemptible instances are discounted compute instances available at a fraction of on-demand prices, often up to 70-90% cheaper. The tradeoff is that these instances can be interrupted by the cloud provider with short notice.
Using them for non-critical workloads such as batch processing, development, testing, or jobs that can be restarted or rescheduled can drastically reduce your compute bill. Critical workloads should stay on reliable on-demand or reserved instances.
6. Limit Autoscaling Boundaries with Sensible Thresholds
Autoscalers without limits can scale pods or nodes beyond what is necessary, especially when sudden spikes or misconfigurations occur, resulting in unexpected cost surges.
By setting minimum and maximum boundaries on replicas or node counts, you ensure scaling stays within reasonable limits, balancing availability with cost control. For example, setting a maximum number of pods prevents the cluster from spinning up hundreds of pods in response to transient load spikes, which could waste resources and increase bills.
Example: Limiting Horizontal Pod Autoscaler (HPA) replicas
Suppose you have a web app with HPA configured to scale based on CPU usage:
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: webapp-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: webapp
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- Without maxReplicas: The HPA might scale up to 50 pods during a sudden traffic spike, causing a huge cost increase.
- With maxReplicas -10: The HPA caps scale at 10 pods, avoiding runaway costs while still improving capacity during demand peaks.
This way, you prevent unnecessary resource consumption and keep costs predictable.
7. Use Schedule-Based Scaling for Predictable Workloads
Many workloads have predictable usage patterns, business hours, weekends, or specific batch processing windows. Schedule-based scaling leverages these patterns by scaling resources up or down on a time schedule, regardless of current metrics.
For example, if traffic drops sharply after 7 PM daily, you can schedule your deployment to scale down pods or reduce node counts during those hours, avoiding paying for unused capacity.
This approach is highly effective for workloads with low variability and predictable idle times.
8. Use KEDA for Event-Driven and Cost-Efficient Scaling
KEDA (Kubernetes-based Event-Driven Autoscaler) is an open-source component that enables Kubernetes to scale workloads based on external event sources such as message queues (like AWS SQS, Kafka), database row counts, or custom application metrics. Unlike the default autoscalers that rely mainly on CPU or memory usage, KEDA allows pods to scale out when real demand exists and scale down to zero when idle.
This event-driven model helps reduce costs significantly by eliminating idle resources. For example, background jobs, webhook handlers, or queue consumers often stay active waiting for input leading to wasted compute. With KEDA, these services run only when needed, avoiding 24/7 pod uptime and reducing infrastructure bills.
Example Scenario
A notification service processes messages from an Amazon SQS queue. Without KEDA, you might keep 3 pods running 24/7 to poll the queue—even when there are no messages to process.
Without KEDA (Always-on pods)
- Pods: 3
- Cost per pod: $0.10/hour
- Monthly Cost: 3 pods × $0.10 × 24 hours × 30 days = $216
With KEDA (Event-driven scaling)
- Pods scale from 0 to 10 based on queue depth
- Assume queue traffic occurs only 6 hours per day
- Monthly Cost: 3 pods × $0.10 × 6 hours × 30 days = $54
Monthly Savings: $216 − $54 = $162
Annual Savings: $162 × 12 = $1,944
Conclusion
By applying these autoscaling strategies in Kubernetes, you can significantly reduce your cloud costs without compromising performance. From right-sizing your pods and nodes to leveraging Spot Instances and custom metrics, each tactic helps ensure your workloads scale efficiently based on actual demand. Start with one or two changes, measure the impact, and gradually optimize your entire cluster for cost-effective scalability.
Smart scaling = smart saving!
References
1. Horizontal Pod Autoscaling | Kubernetes
2. Kubernetes Vertical Pod Autoscaler:
4. Cluster Autoscaler - Amazon EKS
5. About GKE cluster autoscaling | Google Kubernetes Engine (GKE)
6. CNCF Blog: "Scaling Kubernetes Efficiently"
7. KEDA | CNCF
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