In this guide, we will closely look at top Kubernetes (K8s) cost optimization best practices to follow for reduced cost without compromising on performance. 

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As Kubernetes continues to gain widespread adoption across enterprises and among platform-as-a-service (PaaS) and software-as-a-service (SaaS) providers, the use of multi-tenant Kubernetes clusters is the way to go. 

In this type of setup, a single Kubernetes cluster can have applications from different teams, departments, customers, or environments. 

Using Kubernetes' multi-tenancy capabilities allows these organizations to minimize their infrastructure by managing a few large clusters instead of maintaining many smaller ones. 


This approach offers several advantages, including optimized resource utilization, simplified management controls, and reduced operational fragmentation.

But, as these companies with rapidly expanding Kubernetes clusters evolve, they often encounter a significant increase in operational costs, often in millions. 

This cost surge is primarily due to the fact that traditional companies embracing cloud-native solutions like Kubernetes, may lack the cloud expertise in their developers and operators. 

This deficiency in cloud “readiness” can lead to issues such as application instability during auto-scaling events, including routine traffic fluctuations throughout the day or sudden spikes in usage, such as those triggered by TV commercials or peak-scale events like football World Cups or Olympics. 

To address these challenges, these organizations tend to resort to over-provisioning their Kubernetes clusters. 

Consequently, this over-provisioning results in significantly higher CPU and memory allocations than what the applications actually require for the majority of the day.

So, for cost-effectiveness and the stability of your applications, it's important to configure and fine-tune certain settings. These settings include features like autoscaling, machine types, and region selection. 

Additionally, your specific workload type also plays a role because different configurations are required based on your application's needs to help you reduce costs. 

Lastly, it's essential to keep a close eye on your expenditure and establish guidelines to enforce best practices early in your development process. 

Better be safe than sorry! 

1. Gaining Complete Kubernetes Cost Visibility

To manage costs effectively, you first need to understand where your money is going. This means getting a clear picture of how much your Kubernetes cluster is costing you. 

You can use tools like Cost Management Platforms or Kubernetes-specific cost tracking tools. 

Imagine you run an eCommerce website using Kubernetes. 

By tracking costs, you discover that your product recommendation service consumes more resources than necessary, causing higher expenses. 

With this visibility, you can investigate and optimize the service's resource allocation.

Also Read: AWS Cost Optimization Tools & Best Practices

2. Detecting Kubernetes Cost Anomalies

Once you have visibility into your costs, it's crucial to identify anomalies or unexpected spikes that can drive up expenses to an extent that might seem not data-driven. 

Anomalies might result from inefficient resource usage, scaling issues, or other factors.

Suppose your Kubernetes cost suddenly doubles. 

Upon investigation, you find that a misconfigured auto-scaling policy caused your application to scale up too aggressively during a traffic spike. 

Also Read: Horizontal Scaling vs Vertical Scaling

3. Optimizing Pod Resource Requests

Optimizing how you allocate resources to your Kubernetes pods can significantly impact costs. Set resource requests and limits in a way its neither too little nor too overprovisioned. 

Resource requests define the minimum resources a pod needs, while limits cap the maximum.

In a media streaming service, if you set the resource requests too high for each video encoding pod, you'll waste resources and increase costs during idle times. 

On the other hand, if you set them too low, the pods may struggle during peak usage, leading to a poor user experience. Find the right balance.

Also Read: Azure Cost Optimization Best Practices

4. Node Configuration

Kubernetes nodes are the underlying virtual or physical machines that run your pods. 

Optimizing these node configurations means ensuring they are sized correctly, leveraging spot instances (if they’re available), and using the right instance types in cloud environments.

Consider a data processing application running on Kubernetes. If you provision nodes with excessive memory and CPU, you're paying for resources you don't need. 

Conversely, if you don't allocate enough, your jobs might fail or run too slowly. By matching node configurations to your actual workload, you can save on infrastructure costs. 

Also Read: Top Cloud Cost Optimization Best Practices

5. Processor Selection

Choosing the right processors or CPUs for your nodes can have a significant impact on cost and performance. Different cloud providers offer various CPU types and families, each with its price and performance characteristics.  

CPUs/Processor Types for AWS

  • General purpose instances: T2, T3, T4g, M4, M5, M6g, M7g

  • Compute-optimized instances: C4, C5, C6g, C7g

  • Memory-optimized instances: R4, R5, R6g

  • Storage optimized instances: D2, I3, I3en

  • Accelerated computing instances: P3, P4, G4, F1, Inf1

  • Burstable Instances: T2, T3, T4g:

  • Storage Instances: I3, I3en, D2, H1, G2, G

  • Micro Instances: T2 Nano and Micro

  • High I/O Instances: I3, I3en

  • Network-Optimized Instances: X1e, X1, Z1d

  • Dense Storage Instances: H1

  • ARM-Based Instances: A1, Graviton2-Based Instances

  • Specialized Instances:

  • F1: FPGA-based instances for hardware acceleration.

  • Z1d: High-frequency CPU instances with fast clock speeds.

  • X1, X1e: Memory-optimized instances with large memory capacities.

CPUs/Process Types for Azure:

1. General-Purpose VMs (e.g., B-series, D-series):

  • Standard_B2s

  • Standard_D4s_v3

2. Compute Optimized VMs (e.g., F-series):

  • Standard_F2s

  • Standard_F16s_v2

3. Memory Optimized VMs (e.g., E-series):

  • Standard_E2s_v3

  • Standard_E64s_v3

4. Storage Optimized VMs (e.g., L-series):

  • Standard_L4s

  • Standard_L80s_v2

5. Windows VMs:

  • WindowsServer2019Datacenter

  • WindowsServer2016Datacenter

6. Linux VMs (various distributions):

  • UbuntuServer16.04LTS

  • CentOS7.4

7. Specialized VMs (e.g., GPU VMs):

  • Standard_NC6

  • Standard_NV12

8. Performance Tiers (e.g., Premium VMs):

  • Standard_DS2_v2

  • Standard_DS14_v2

9. Virtual Machine Scale Sets (VMSS):

  • VMSSName_instance1

  • VMSSName_instance2

10. High-Performance Computing (HPC) VMs:

  • HPCVmType1

  • HPCVmType2

Let's say you're running a machine learning workload on Kubernetes. If you choose high-performance CPUs for every node, you might end up overspending. 

However, if you understand your application's CPU requirements and select processors that balance cost and performance, you can achieve the same results while saving money.

6. Purchasing Options

When it comes to purchasing infrastructure for your Kubernetes cluster, there are different options available.

If you are using a cloud provider like AWS, you can choose between on-demand, reserved, or spot instances. 

For predictable workloads, reserved instances might be cost-effective as they provide discounts for longer commitments. 

However, for workloads that can tolerate interruptions, using spot instances can lead to substantial cost savings.

Also Read: Kubernetes Pods vs Nodes vs Clusters

7. Autoscaling Rules

Suppose you run a web application that experiences traffic spikes during sales events. 

By setting up autoscaling rules, your cluster can automatically add more nodes during these events to handle the increased load. 

Once the event is over, the cluster scales down, saving you money by not running unnecessary resources during quieter periods.

Also Read: How to Setup Multiple Apps Using Just One Load Balancer in Kubernetes?

8. Kubernetes Scheduler (Kube-Scheduler) Configuration

The Kubernetes scheduler determines where and when to run your pods on the cluster.  

If you have pods that require specific hardware capabilities, like GPUs, you can configure the scheduler to place these pods on nodes with the required hardware. 

This ensures that you are not wasting GPU resources on nodes that don't need them, optimizing both performance and cost.

Also Read: How to Setup & Use Kube-State-Metrics?

9. Managing Unattached Persistent Storage

Persistent storage in Kubernetes can become a hidden cost if not managed properly. Identifying and reclaiming storage resources that are no longer in use is essential.

Over time, pods and their associated persistent volumes may be deleted, but the storage volumes might remain, incurring costs.

Implementing policies and scripts to identify and clean up unattached storage can help you avoid unnecessary storage expenses.

Also Read: Everything You Need to Know About Persistent Volume Claims (PVCs)

10. Optimizing Network Usage to Minimize Data Transfer Charges

Data transfer charges can be a significant cost in cloud-based Kubernetes environments

Suppose you have multiple microservices within your Kubernetes cluster communicating with each other. 

By configuring your services and pods to use efficient communication patterns and reducing unnecessary data transfer, you can lower network-related costs. 

Additionally, consider using a Content Delivery Network (CDN) or edge caching to offload traffic and reduce data transfer charges.

Also Read: AWS EFS vs EBS vs S3

11. Minimizing Cluster Counts

Running multiple Kubernetes clusters can quickly increase infrastructure costs.  

If your organization operates several environments (e.g., development, staging, production), consider using a single cluster with appropriate isolation mechanisms, like Kubernetes namespaces or network policies, rather than maintaining separate clusters for each environment. 

This consolidation can reduce infrastructure overhead and management complexity, ultimately lowering costs.

Also Read: How to Manage K8s Clusters with Kubeadm?

12. Cost Monitoring

Implement a cost monitoring tool that provides real-time insights into your Kubernetes spending. Set up alerts to notify you of cost spikes or unusual expenditure patterns. 

For instance, if you notice that your storage costs have increased significantly, you can investigate and optimize storage usage to reduce expenses.

Also Read: Understanding ReplicsSets in Kubernetes

13. Resource Limits

Setting resource limits at the pod level helps prevent resource-hungry pods from consuming more resources than necessary, leading to cost savings by avoiding resource over-provisioning.

Consider a database pod in your Kubernetes cluster. 

By setting appropriate CPU and memory resource limits, you ensure the database doesn't consume excessive resources, preventing performance bottlenecks and reducing costs. 

Additionally, pods with well-defined limits are less likely to cause node resource contention issues.

14. Discounted Computing Resources (Reserved Instances)

Many cloud providers offer discounted or reserved instances for longer-term commitments. Leveraging these discounted computing resources can lead to substantial cost savings.

Suppose you have a Kubernetes workload that runs 24/7. 

By purchasing reserved instances from your cloud provider, you commit to using those resources for a specified period (e.g., one or three years) at a lower cost compared to on-demand instances. 

Also Read: Kubernetes Taints & Tolerations

15. Sleep Mode

For non-production or development environments, consider implementing a "sleep mode" for your Kubernetes clusters during periods of inactivity. 

In sleep mode, you can shut down or scale down non-essential resources to minimize costs.

In a development or testing environment, you can schedule scripts or use Kubernetes tools to scale down replica counts during nights and weekends when developers are not actively working. 

This approach makes sure that resources are only utilized when needed, reducing costs during idle periods.

16. Cleanup

Regularly cleaning up unused or obsolete resources within your Kubernetes cluster is essential for cost optimization.

Over time, you may have pods, services, or other Kubernetes objects that are no longer necessary. 

Implement automated cleanup scripts or policies to identify and remove these resources, freeing up cluster capacity and reducing associated costs.

Also Read: How to Cleanup Docker Resources?

17. Cluster Sharing

If your organization operates multiple Kubernetes clusters, consider sharing clusters for different workloads or teams, rather than creating separate clusters for each. 

This can lead to resource consolidation and cost savings.

Instead of maintaining separate clusters for each department, you can create a shared cluster with appropriate access controls and namespaces for each team. 

This approach reduces the overhead of managing multiple clusters and optimizes resource usage.

Advantages of Fewer Clusters

1. Cost Savings: Running fewer clusters typically results in lower infrastructure costs. This is because each cluster consumes resources such as virtual machines, storage, and networking. 

2. Simplified Management: Managing multiple clusters can be complex and time-consuming. Fewer clusters mean fewer administrative tasks, making it easier to monitor, maintain, and troubleshoot your Kubernetes infrastructure.

3. Easier Governance: Implementing security, access control, and policies becomes more straightforward when dealing with fewer clusters. 

4. Improved Collaboration: When different teams or projects share a single cluster, it improves collaboration and resource sharing. Teams can benefit from insights and solutions developed by others.

Disadvantages of Fewer Clusters

1. Resource Contention: If multiple teams or workloads share a single cluster, resource contention can become an issue during peak usage periods. This can lead to performance bottlenecks and instability if not managed properly.

2. Isolation Challenges: Fewer clusters may require more robust isolation mechanisms, such as Kubernetes namespaces and network policies, to prevent one workload from impacting another. This adds complexity to the cluster configuration.

3. Risk of Single Point of Failure: Putting up workloads into fewer clusters increases the risk that a cluster failure can disrupt multiple services or teams simultaneously. Implementing high availability and disaster recovery measures becomes crucial here. 

4. Scaling Challenges: Scaling a single, consolidated cluster may be more challenging compared to scaling individual clusters independently. You need to carefully plan and manage the scaling process.

5. Limited Customization: Different teams or projects may have unique requirements or configurations. In a shared cluster, you may need to compromise on certain configurations or policies to accommodate diverse needs.

How to Safely Reduce the Number of Required Clusters?

1. Gradual Transition: Instead of abruptly reducing the number of clusters, plan for a gradual transition. Migrate one workload or team at a time to ensure that resource requirements and performance are not negatively impacted.

2. Testing and Monitoring: Thoroughly test and monitor the impact of consolidation on performance, resource utilization, and stability. Use metrics and alerts to catch any issues early and adjust your strategy accordingly.

3. Resource Isolation: Implement strong resource isolation mechanisms, such as Kubernetes namespaces and network policies, to maintain security and stability when sharing clusters. 

4. High Availability: Ensure that your consolidated clusters are designed for high availability. Implement redundancy, failover mechanisms, rollbacks, and disaster recovery plans to mitigate the risk of cluster-level failures.

Also Read: 5 Ways to Use Kubectl Rollout Restart

18. Use Virtual Clusters

Virtual clusters, sometimes referred to as namespace-based clusters, can provide a compromise between having separate clusters and consolidating everything into one. 

They allow you to create logical isolation within a single cluster, providing teams or workloads with their dedicated namespaces while sharing underlying resources.

You must utilize Kubernetes resource quotas within virtual clusters to enforce resource limits and prevent resource contention. What this does is it helps maintain stability and isolation while optimizing resource utilization.

How Virtual Kubernetes Clusters Decrease Costs?

1. Resource Efficiency: Virtual clusters enable you to share the underlying physical cluster resources (nodes) among multiple tenants or workloads. 

2. Reduced Infrastructure Overhead: Maintaining multiple physical clusters for each tenant or team can lead to significant infrastructure overhead. Virtual clusters consolidate workloads onto a single cluster, reducing the number of nodes required.

3. Simplified Management: Managing a single physical cluster with virtual clusters is more straightforward than managing multiple independent clusters. It reduces administrative complexity and streamlines cluster operations.

4. Cost Sharing: Tenants or teams sharing a virtual cluster can collectively cover the costs of the underlying infrastructure. 

5. Scalability: Virtual clusters can scale more flexibly than provisioning additional physical clusters. You can allocate additional resources to specific virtual clusters as needed.

6. Isolation with Efficiency: Virtual clusters provide a balance between isolation and resource efficiency. While tenants share the same physical cluster, they are logically isolated, preventing resource contention.

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19. Implementing Effective Multi-Tenancy

Effective multi-tenancy in a Kubernetes environment involves hosting multiple tenants or user groups on a shared cluster while maintaining security, isolation, and performance. 

Here's how to achieve it.

1. Namespace Isolation: Use Kubernetes namespaces to logically separate tenants. Each tenant's resources and workloads are contained within their respective namespaces. 

This provides a basic level of isolation.

kubectl config set-context --current --namespace=<insert-namespace-name-here>

2. Resource Quotas and Limits: Implement Kubernetes resource quotas and limits within namespaces to control resource consumption. This prevents one tenant from consuming all available resources and impacting others.

3. Network Policies: Use network policies to define communication rules between namespaces. This ensures that tenants can only communicate with approved services, enhancing network security and isolation. Example: 

kind: NetworkPolicy
  name: test-network-policy
  namespace: default
      role: db
    - Ingress
    - Egress
    - from:
        - ipBlock:
        - namespaceSelector:
              project: myproject
        - podSelector:
              role: frontend
        - protocol: TCP
          port: 6379
    - to:
        - ipBlock:
        - protocol: TCP
          port: 5978

4. Role-Based Access Control (RBAC): Configure RBAC to define fine-grained access controls for each tenant. Ensure that each tenant has access only to the resources and actions they need, limiting the risk of unauthorized access.

5. Monitoring and Auditing: This helps identify anomalies, security breaches, or performance issues and ensures accountability.

6. Tenant Education: Educate tenants about best practices, resource optimization, and security guidelines. 

7. Automation: Use automation tools and scripts to enforce policies, resource quotas, and security controls. Automation reduces the manual effort required to manage multi-tenancy effectively.