GKE Pricing Tiers with Examples + 10 Cost Cutting Tips

Understanding GKE Pricing Models with Examples 

Let’s review GKE’s different pricing tiers, with simple examples that illustrate pricing for each tier.

Free Tier

The GKE Free Tier offers $74.40 in monthly credits per billing account, which can offset cluster management fees. For example:

• If running a single Zonal or Autopilot cluster for a month, the credit covers the cluster's flat management fee of $0.10/hour. This means such a cluster effectively costs nothing unless additional resources (e.g., compute or storage) are used.

For accounts running multiple clusters, the free tier credit is distributed across eligible clusters. For example:

• If an account runs 1,000 hours of Regional clusters and 500 hours of Zonal clusters, the total fee is $100 (1,000 hours + 500 hours × $0.10/hour), and $50 of the $74.40 credit will apply to the Zonal cluster, leaving a total charge of $50.

Credits do not apply to other GKE SKUs like compute resources or the cluster management fees for GKE Enterprise clusters.

Standard Edition Pricing

The Standard edition is billed at $0.10 per hour per cluster for management fees. Compute resources are charged separately based on Google Compute Engine pricing.

Example:

A Standard cluster runs for 30 days (720 hours) in a single zone:

• Management fee: 720 hours × $0.10 = $72.00
• Compute costs: For example, if the cluster runs on a node pool of three e2-medium machines, each with 2 vCPUs and 4 GB memory, the per node cost will be $0.03492/hour × 3 nodes × 720 hours = $75.31
• Total cost: $72.00 (management fee) + $75.31 (compute) = $147.31

Users can reduce compute costs using committed use discounts for long-term workloads.

Enterprise Edition Pricing

The Enterprise edition offers features such as multi-cluster management, improved security, service mesh, and hybrid/multi-cloud functionality. Pricing is based on vCPUs under management, at $0.00822 per vCPU per hour.

Public cloud deployment example:

A cluster with 100 vCPUs running for 30 days (720 hours). 

• Management fee: 100 vCPUs × $0.00822 × 720 hours = $591.84/month
• Additional costs: Public cloud environments (e.g., Google Cloud, AWS, Azure) may incur charges for resources like storage, networking, or load balancers, which are billed separately.

On-premises deployment example:

An on-premises setup using VMware vSphere with 50 vCPUs:

• Management fee: 50 vCPUs × $0.03288 × 720 hours = $1,183.68/month
• If hyperthreading is enabled, 1 physical CPU equals 2 vCPUs, doubling the cost.

The Enterprise edition includes the Extended support channel, so clusters on this edition do not incur the additional extended cluster management fee.

Autopilot Mode Pricing

Autopilot clusters use a flat management fee of $0.10/hour per cluster and a pod-based billing model for compute resources. Resources are billed based on the CPU, memory, and storage requested by workloads, with no charges for unscheduled or terminated pods.

Default general-purpose pods example: 

A workload requests 4 vCPUs, 8 GB memory, and 50 GB ephemeral storage, running for 720 hours in the africa-south1 region:

• vCPU: 4 × $0.0582 × 720 hours = $167.90
• Memory: 8 × $0.0064386 × 720 hours = $37.00
• Storage: 50 × $0.0001817 × 720 hours = $6.53
• Management fee: $0.10 × 720 hours = $72.00
• Total: $167.90 + $37.00 + $6.53 + $72.00 = $283.43

Specific hardware pods example: 

A workload requests a GPU with 8 vCPUs and 32 GB memory for 100 hours in the africa-south1 region:

• GPU node: Compute resources are charged for the full node, even if not fully utilized. 
• Node cost: 8 × $0.005232 × 100 hours = $4.19
• Memory cost: 32 × $0.000654 × 100 hours = $2.09
• Total node cost: $6.28

For workloads requiring specialized hardware, fully utilizing allocated resources ensures cost efficiency.

Backup Pricing for GKE

Backup for GKE is billed based on two components:

1. Management fee: $0.00171 per pod-hour
2. Storage fee: $0.0000479 per GiB-hour

Example:

Backing up an average of 20 Pods for a month (720 hours) with 500 GiB of stored data in the africa-south1 region:

• Management fee: 20 Pods × $0.00171 × 720 hours = $24.62
• Storage fee: 500 GiB × $0.0000479 × 720 hours = $17.23
• Total cost: $24.62 + $17.23 = $41.85

Additional cross-region data transfer charges apply for backups stored in a different region, ranging from $0.02 to $0.14 per GiB, depending on the source and destination regions.

Example for cross-region backup:

• Transferring 1,000 GiB from Northern America to Europe incurs $0.05 × 1,000 = $50.00 in cross-region fees.

Related content: Read our guide to GKE cost optimization

10 Cost Cutting Tips for GKE 

Here are some of the ways that organizations can ensure the most cost-effective deployments on Google Kubernetes Engine.

1. Right-Size Clusters and Nodes

Right-sizing involves aligning cluster and node resources with workload demands, eliminating unnecessary resource allocation. This strategy requires continuous monitoring and adjustment of resource types and quantities based on performance metrics and utilization patterns, ensuring efficient resource usage without over-provisioning.

2. Use Spot VMs

Spot VMs offer a cost-effective alternative for running short-lived, fault-tolerant workloads. These instances are available at reduced prices compared to standard VMs, making them suitable for workloads such as batch processing or CI/CD tasks, where temporary interruptions are acceptable. Deploying applications on preemptible VMs can yield substantial savings.

However, the instability of spot VMs requires proper planning. Users should architect applications to handle unexpected terminations, leveraging checkpointing and data retention strategies to minimize data loss.

3. Implement Autoscaling Effectively

Autoscaling dynamically adjusts resources based on real-time demands, ensuring optimal performance without over-provisioning. This functionality aids in aligning costs with usage, automatically scaling clusters to accommodate load changes. Proper configuration and policy settings ensure efficient autoscaling operations, preventing unnecessary spending while maintaining system responsiveness.

4. Leverage Committed Use Discounts

GKE offers committed-use contracts where organizations can purchase capacity at a discounted rate by committing to a specified volume of resources over an extended period. This strategy suits organizations with predictable workload patterns aiming to achieve substantial long-term savings on their cloud expenses. Careful analysis of workload requirements is important for leveraging such discounts.

5. Monitor Resource Usage and Spending

Active monitoring of resource usage and expenses forms the backbone of effective cost management. Utilizing Google Cloud's monitoring and budget management tools provides visibility into spending patterns and alerts for anomalous resource consumption. Setting up automated alerts and tracking utilization trends ensures timely intervention and cost control.

6. Use Cost Allocation Labels

Cost allocation labels involve tagging cloud resources with labels that associate costs with chosen departments, projects, or clients. This practice simplifies tracking, reporting, and managing expenses, enabling more granular insights into cloud spending. Effective labeling allows organizations to allocate costs accurately, enabling budgeting and cost accountability.

However, while labeling is critical, it is also difficult to achieve and maintain. The more complex the environment, especially in multi-tenant scenarios, it requires a major effort to apply cost allocation labels to resources, and these quickly get out of date. Automated cloud cost management tools like Finout can help automatically apply these labels and maintain them over time to ensure cost accountability across complex environments.

7. Regularly Review and Optimize Resource Utilization

Consistent review of resource utilization promotes efficient cloud operations, identifying underutilized assets and optimization opportunities. Routine audits, combined with intelligent scaling strategies, prevent resource waste and associated costs, aligning cloud usage with application requirements.

8. Optimize Workload Deployments

Optimizing workload deployments requires strategic configuration of applications, focusing on maximum resource efficiency and cost savings. This involves using best practices in application architecture, such as containerization, and selecting appropriate deployment environments for each workload. Understanding workload characteristics enables precise tuning of infrastructure components.

9. Implement Resource Quotas and Limits

Implementing resource quotas and limits controls cloud usage, preventing over-provisioning and budget overruns. This strategy involves setting boundaries for resource allocation per application or department, encouraging simplified usage while ensuring adequate capacity. Quotas enforce resource discipline, ensuring usage aligns with budgetary constraints.

10. Use Cost Management Tools and Dashboards

Cost management tools and dashboards provide actionable insights, assisting organizations in controlling cloud expenses. These tools consolidate cost data, offering visibility into spending trends and enabling efficient tracking of budget adherence. Dashboards enable real-time monitoring, aiding in quick identification of consumption anomalies and potential savings.

Open-source tools like OpenCost and KubeCost are often used to provide visibility into Kubernetes costs. While useful, these tools are not true FinOps solutions and are limited in their ability to forecast and optimize cost for large-scale Kubernetes environments. Full-featured cloud cost management solutions like Finout provide better visibility, more granular cost allocation and management capabilities, including showback and chargeback.

Why you should consider Finout when managing Kubernetes costs

Finout's FinOps solution excels in managing complex Kubernetes environments by enabling dynamic shared cost reallocation across the entire infrastructure. This capability is crucial for businesses operating in multi-cloud or hybrid environments, where cost attribution can become complicated due to the intermingling of resources across different platforms and services.

The ability to perform on-the-fly cost reallocation allows Finout to provide a nuanced view of financial data, aligning costs with actual usage. This is especially beneficial in Kubernetes settings where resources are dynamically scaled and vary significantly between teams or projects. By reallocating costs based on current usage, Finout ensures that each department or project is accurately charged for the resources they consume, enhancing accountability and promoting efficient resource use.

Moreover, Finout’s robust allocation features support complex financial management tasks such as showback and chargeback, making it easier for organizations to understand their spending and make informed budgeting decisions. This level of financial granularity and control is essential for companies looking to optimize their cloud expenditure and maximize their return on investment in cloud technologies.