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DevOps
Dec 05, 2024
Kubernetes is powerful but can become a black hole for your cloud budget. If you’re managing applications on Google Kubernetes Engine (GKE), you've likely faced the challenge of keeping costs down while ensuring your app runs smoothly. You’re not alone—Kubernetes cost management is a common headache, and it doesn’t help that many teams dive into Kubernetes without fully understanding its financial impact.
In this post, we’ll explore best practices for running cost-optimized Kubernetes applications on GKE. We’re going to break down the key strategies, from picking the right autoscaling methods to getting a grip on cost-effective machine types. We'll also explore some real-world tips, complete with data, to help you make informed decisions. By the end, you’ll know how to fine-tune your GKE clusters, monitor costs like a pro, and make your Kubernetes setup work with your budget—not against it.
Kubernetes is fantastic for managing workloads, but it has a dark side: runaway costs. Why? Because Kubernetes is inherently elastic. It’s built to respond to workload changes, scaling up nodes and Pods as necessary. This is great for keeping your services reliable, but if you’re not careful, the result can be bloated clusters, unused resources, and way more money spent than you anticipated.
Cost optimization is about balance: you want to provide enough resources for your apps to run well, but not so many that you're overpaying for idle resources. Luckily, GKE comes with tools and features designed to help you optimize your Kubernetes applications. Let’s get into it.
Autoscaling can be your best friend or your worst enemy. Used correctly, it helps save costs by adjusting the size of your infrastructure in real-time. Here’s how to get the most out of it:
HPA scales Pods based on load metrics like CPU usage or custom metrics. The key here is properly setting utilization thresholds. Set a target CPU utilization buffer of about 20-30%. Why? If you set it too low, you’re wasting resources. If it’s too high, you risk overload.
For example, let’s say your app has a target utilization of 70%. This means you have a 30% buffer—perfect for unexpected spikes in traffic, like a flash sale. Without this buffer, Kubernetes would have no room to react, leading to performance hits and unhappy customers.
Code Example for setting HPA in Kubernetes
apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: web-app-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: web-app minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70
This configuration helps you react effectively to usage surges, avoiding costly overprovisioning.
VPA is like a personal trainer for your Pods—it ensures they’re neither underfed nor overfed. It adjusts the resource requests for your Pods to find the sweet spot between performance and cost.
Start by using VPA in "recommendation mode" for at least a week to gather enough data before you let it loose on your production workloads. Here's why: VPA will recommend optimal CPU and memory levels by learning the resource requirements over time. But, don’t use VPA for sudden traffic bursts—that’s HPA’s job.
Quick Tip: VPA works best for workloads that don't need real-time scaling. Use it to keep costs in check for background or processing tasks that run steadily.
CA adds or removes nodes based on whether Pods can find a place to live in your cluster. Unlike HPA, it doesn’t use load metrics but instead focuses on Pod scheduling needs.
To maximize savings, consider using Spot VMs for non-critical workloads. These VMs can be up to 91% cheaper than regular ones! Just note they can be shut down anytime, so they’re great for batch jobs or any task that isn’t sensitive to interruptions.
When it comes to running Kubernetes on GKE, choosing the right machine type is a big deal for cloud cost optimization.
E2 instances are a great middle-ground—they’re 31% cheaper than N1 machine types. They’re ideal for most Kubernetes workloads that don’t need a huge amount of computational power. E2 instances use dynamic resource management, meaning Google Cloud optimizes them for price without you having to lift a finger.
For workloads that are not mission-critical, like batch processing jobs, Spot VMs are perfect. You can save up to 91%, but remember: these are "use-it-while-you-can" resources. Google Cloud can terminate them if needed.
Here's how you can use Spot VMs for a Kubernetes cluster in GKE:
apiVersion: container.cnrm.cloud.google.com/v1beta1 kind: ContainerNodePool metadata: name: spot-node-pool spec: clusterRef: name: my-gke-cluster nodeConfig: preemptible: true machineType: e2-standard-4
With this configuration, your batch jobs can benefit from cost-effective compute without much impact if a node goes offline.
To manage Kubernetes costs effectively, you need visibility into where your money is going. There’s a saying in the cloud world: you can’t manage what you can’t measure.
Usage Metering helps you break down costs across namespaces, labels, and workloads. This helps answer key questions like: Which team is spending the most? What’s driving that sudden spike in costs? Knowing this helps you take action quickly and adjust quotas if needed.
Example Scenario If you see a spike in costs due to increased CPU usage, it could be because of an under-optimized application update. You can then use GKE’s Recommendation Hub to understand what went wrong and how to fix it.
Metrics Server is the heart of GKE’s autoscaling pipeline. If it’s not running smoothly, your autoscalers won’t know when or how to react. Make sure to monitor Metrics Server and keep it healthy to avoid unintended scaling issues.
Every workload isn’t created equal. A batch job doesn’t have the same cost or performance needs as a real-time application. Treating them all the same is a surefire way to waste money.
For batch jobs, use dedicated node pools. This helps with more efficient scale-downs since Cluster Autoscaler won’t have to restart Pods. Also, use Node Auto-Provisioning to create node pools automatically based on the specific demands of the workload.
For serving workloads that need to handle spikes, focus on a combination of fast-starting containers and horizontal scaling. Preload container images to reduce latency and avoid disruptions during scale-up events.
Traffic between zones costs money. If you’re running services that need to communicate often, try keeping them in the same zone. For instance, using affinity and anti-affinity rules in Kubernetes helps ensure Pods are scheduled close together, reducing inter-zone egress charges.
affinity: podAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - backend topologyKey: "topology.kubernetes.io/zone"
Excessive logging is a silent cost killer. We’ve seen Kubernetes apps where logs cost 3x more than the workload itself. Limit logs to essential information—debug logs can be helpful during development but turn them off in production. Tools like Stackdriver Logging can help you analyze and reduce log volume.
Managing Kubernetes costs isn't just about technical best practices—it’s also about mindset. Teams need to treat costs like any other performance metric.
Cost should be a part of every sprint review and planning meeting. Make it visible, make it important, and make it a key performance indicator (KPI) for your DevOps teams. Cloud-native companies that focus on cost efficiency usually have fewer unpleasant surprises.
Quotas are great for creating guardrails around your costs. By assigning specific quotas to different teams or projects, you can avoid one workload swallowing up all the resources. This not only saves costs but also helps teams become more conscious of their resource footprint.
If you’re looking for tools that can help you save money, here are a few to consider:
Kubecost This tool breaks down costs by namespace, deployment, and Pod. It gives you a clear picture of how every part of your cluster is contributing to the bill.
CloudZero Cloud Zero takes things further, letting you align Kubernetes costs with business metrics. You can see which features, teams, or customer segments are driving costs.
Spot by NetApp Automatically optimizes your cluster using Spot VMs.
Qovery Offers features like automated environment shutdowns for cost control.
These tools can help make sense of complex bills and highlight where cost reductions can be made.
Optimizing your Kubernetes costs on GKE isn’t about cutting corners—it’s about getting the most bang for your buck. Start with fine-tuning autoscaling, choose cost-efficient machine types like E2 and Spot VMs, and make monitoring a habit. Remember, the most effective cost optimization strategies combine tech and culture.
By making Kubernetes cost management part of your workflow, you’re setting your teams up for success—not just in managing infrastructure, but in making smart, informed decisions about where every dollar goes. It's not just about saving money; it's about being efficient, agile, and ready for whatever comes next.
