Kubernetes Cost Optimization for Small Teams on GKE

Managing Kubernetes clusters on Google Kubernetes Engine (GKE) can be both powerful and costly, especially for small teams with limited budgets and engineering resources. While GKE provides a managed and scalable Kubernetes experience with deep integration into Google Cloud’s ecosystem, it can quickly accumulate expenses if not properly optimized. Fortunately, there are practical strategies to help small teams maintain high efficiency while keeping infrastructure costs in check.

Understanding Your GKE Cost Drivers

Before diving into optimization strategies, it’s essential to understand what contributes to your GKE costs. The key factors are:

• Node pools and VM types: The virtual machines (VMs) used in your clusters significantly influence your billing rates.
• Cluster management fees: GKE charges a flat fee for each zonal or regional cluster you maintain.
• Persistent storage and data egress: Volumes and network traffic outside the region also add to the costs.
• Idle resources: Unused CPU and memory reservations can quietly drain your budget.

Once you can deconstruct your GKE billing, you can make informed decisions on how to optimize it. Let’s explore how.

1. Right-Sizing Your Node Pools

One of the simplest and most effective ways for small teams to cut costs is by right-sizing node pools. Often, Kubernetes clusters are over-provisioned during initial deployment. This might be acceptable at first, but it becomes costly over time.

• Use smaller machine types like e2-medium or n2-standard-2 for low workloads.
• Leverage custom machine types to define your own CPU and memory configurations for precise resource allocation.
• Evaluate the use of spot instances (preemptible VMs) for stateless workloads to reduce costs by up to 80%.

Google Cloud provides preemptible VM options that work well for batch jobs or other non-critical processes.

2. Use Autopilot Mode with Caution

GKE offers an Autopilot mode, which abstracts away infrastructure management and only charges you for the actual resource requests. On the surface, this model seems perfect for small teams. However, there are trade-offs you should be aware of:

• You’re charged based on requested CPU and memory, not actual usage.
• There’s less control over node-level optimizations and some advanced configurations are not supported.

If your workloads are predictable and you’re disciplined about setting correct resource requests and limits, Autopilot can be cost-effective. Otherwise, manually managed (Standard mode) clusters might provide more flexibility in tuning resource efficiency.

3. Configure Resource Requests and Limits Properly

A common mistake teams make is setting high default resource requests for CPU and memory. Kubernetes schedules pods based on these requests, not actual usage. If your requests exceed what a pod truly needs, you’ll end up with underutilized nodes.

Tips for small teams:

1. Use Kubernetes monitoring tools like kubectl top, or Google Cloud Monitoring (formerly Stackdriver) to track real resource usage.
2. Set lower default requests and iterate upward only when workloads get throttled.
3. Automate this process with tools like Vertical Pod Autoscaler, which suggests optimized request values.

4. Employ Horizontal and Cluster Autoscaling Strategically

GKE supports both Cluster Autoscaler and Horizontal Pod Autoscaler (HPA), which are instrumental in aligning resource allocation with actual demand.

• Use HPA to automatically scale the number of pod replicas based on CPU or custom metrics.
• Enable Cluster Autoscaler so that additional nodes are only spun up when they’re really needed.

For budget-minded teams, this strategy ensures you’re only paying for what’s actively being used, while also accommodating workload spikes without manual intervention.

5. Turn Off Unused Clusters and Environments

In active development, small teams often maintain multiple clusters or namespaces—for staging, testing, or experimentation. But unused clusters continue to incur cost.

Suggestions:

• Schedule automatic shutdowns of dev/test clusters during off-hours using scripts or third-party tools.
• Consolidate applications into shared namespaces within a single cluster where appropriate.
• Use Infrastructure-as-Code (IaC) tools like Terraform to recreate environments when needed, instead of keeping them always running.

6. Optimize Persistent Storage Usage

Persistent volumes are not auto-scaled or deleted even when workloads are gone. That means abandoned volumes continue to incur charges. To mitigate this:

• Enable volume lifecycle policies that delete unused Persistent Volume Claims (PVCs) when their associated Pods are deleted.
• Use smaller disk sizes and consider SSDs only when truly needed.
• Periodically audit your storage usage and remove orphaned volumes manually if necessary.

7. Monitor and Visualize Costs

You can’t optimize what you don’t measure. Fortunately, Google Cloud offers a robust suite of monitoring and billing tools that small teams can use:

• Cost Allocation Tags: Track expenses by project, cluster, or environment.
• Billing Reports: Analyze cost trends and usage patterns over time.
• Cloud Monitoring Dashboards: Visualize CPU, memory, and disk utilization across pods and nodes.

A combination of these tools allows your team to make data-driven decisions and spot wasteful spending quickly.

8. Use Budget Alerts and Quotas

GCP allows you to define budgets and quota limits on services, including GKE. Configuring these is invaluable for small teams:

• Set budget alerts to notify you as you approach predefined thresholds.
• Use IAM and organization policies to restrict who can create costly resources, like large VM types.
• Apply quotas to limit the maximum number of nodes or cluster resources that each project can consume.

This approach prevents surprises and forces discipline in infrastructure management.

9. Use Free Tier, Committed Use Discounts, and Sustained Use Discounts

Google Cloud offers various opportunities to save:

• Take advantage of the free tier (1 zonal cluster per billing account).
• Evaluate committed use discounts if you have predictable workloads (saves up to 57% on VM usage).
• GKE nodes automatically qualify for sustained use discounts when used consistently in a billing month.

Assess your deployment and see if short-term commitment pays off, particularly for production workloads that don’t vary much.

Conclusion

For small engineering teams, managing Kubernetes costs on GKE doesn’t have to be overwhelming. By taking a proactive and structured approach—right-sizing nodes, setting diligent resource limits, implementing autoscaling, and staying on top of unused resources—you can keep your Kubernetes operations lean and cost-effective.

Invest time early in understanding and leveraging GCP’s billing and optimization tools. This enables you to build a culture of cost awareness and ensures that your team scales responsibly within its means. Remember, every dollar saved on infrastructure is a dollar you can reinvest into your product and team.