FinOps Kubernetes Best Practices

Kubernetes is a cornerstone of modern cloud-native infrastructure. It’s also fairly complex. A Kubernetes cluster consists of multiple pods, nodes, and other resources like namespaces, CPU, storage, RAM, networks, secrets, users, and ingresses.

As applications scale, Kubernetes resource consumption and charges can spike, creating the risk of costs spiraling out of control. Further, Kubernetes’ complexity can make it hard to understand where cost-cutting is practical. FinOps is an essential aspect of managing and scaling Kubernetes clusters.

This article will explore FinOps Kubernetes principles in depth, including key FinOps challenges, the six core principles of FinOps, the FinOps phases, and how modern organizations can use FinOps to reduce their Kubernetes costs.

The table below summarizes three high-level Kubernetes FinOps challenges we will explore in this article.

Understanding the cost impact of each Kubernetes component

Containers in Kubernetes clusters consume cloud resources similar to any other application. However, within a cluster, multiple teams often use parts of these shared resources which can lead to several FinOps challenges. Let’s examine each of these challenges.

Kubernetes FinOps challenge 1: Resource sharing and isolation

Kubernetes optimizes infrastructure resource allocations by sharing them. This approach becomes complex when deployments and namespaces, which are somewhat isolated, share underlying resources. This issue extends to persistent volumes, load balancers, and other resources. With multi-tenancy, it’s crucial to manage and monitor costs for each tenant’s resources and ensure accurate billing.

Kubernetes FinOps challenge 2: Microservice architecture complexity

The microservice architecture adds complexity as each request traverses a series of interacting microservices. We need advanced context propagation techniques to trace service invocations to the respective business unit, product line, or tenant. For instance, the Kubernetes control plane assigns applications as pods to worker nodes, which run Kubernetes workloads.

Pods have specified resource requirements—CPU, RAM, storage—supported by cloud-provided VM instances, each with its cost. Using cloud providers like AWS means deploying Amazon EC2 instances as compute nodes, which affects billing. AWS Cost and Usage Reports (AWS CUR) focus on provider-centric metrics, often lacking the detail needed to understand application costs fully.

Kubernetes FinOps challenge 3: Dynamic scalability and cost variability

Cloud-native applications’ dynamic scalability can lead to sudden cost fluctuations as applications scale. It’s necessary to link cost increases with application demands, behaviors, and business KPIs. For example, a cost spike might result from expected increased activity during a Black Friday sale. Conversely, a spike could also stem from a flawed configuration that causes unchecked scaling of backend services.

FinOps is essential when assigning costs, forecasting, capacity planning, and cost monitoring. Additionally, organizations can use the consumption patterns of teams and product lines to make data-driven decisions to balance application cost, quality, and reliability.

Why is Kubernetes cost reporting challenging?

Analyzing cloud costs and shared resources of Kubernetes workloads poses challenges in attributing spend to specific customers, teams, and environments. Kubernetes cost reporting aims to help users grasp the factors driving container costs. This understanding empowers teams to calculate the overall cost of ownership accurately.

One approach to looking into containerization costs is to dissect costs according to:

• Billing hierarchy: Organizational structures such as organizations, folders, and projects, normalized with multi-cloud concepts like linked accounts and tags.
• Resources: Compute cores, RAM, memory, load balancers, persistent disks, and network egress.
• Namespaces: Assigning specific labels to isolate containers within Kubernetes clusters.
• Labels: Tags for teams, cost centers, app names, environments, and more. By thoroughly labeling and tagging these cost drivers, users can enhance accuracy in team invoicing, cost auditing, cost allocation, overrun cost optimization, budgeting scenario modeling, and workload cost fitting within quotas or budget caps.

“FinOps is about getting the most value out of the cloud to drive efficient growth.” - FinOps Foundation

The FinOps Foundation provides guidelines to facilitate successful adoption of FinOps practices within your organization. To understand these guidelines, let’s review the foundation’s six core principles of FinOps.

1. Foster collaboration

Organizations should encourage collaboration between the business, finance, engineering, and product departments.

2. Foster ownership

Instill a culture where everyone takes ownership of their cloud usage.

3. Centralized oversight

Establish a centralized team responsible for driving FinOps initiatives.

4. Accessible and timely reporting

Ensure that reports on cloud usage are accessible and provided promptly, highlighting the importance of observability.

5. Business-driven decision making

Organizations should use internal benchmarking and trend analysis to make informed cloud usage decisions emphasizing business value.

6. Utilize variable cost model

Use the flexibility of the cloud's variable cost model, optimizing instances and comparing pricing between services and resource types.

FinOps for Kubernetes in production

On-demand prices for containerized deployments offer significant cost savings for stateless and fault-tolerant applications. These containers, designed to be ephemeral and stateless, ensure smooth startups and shutdowns. Serverless deployments become cost-effective as they incur charges only during active runtime, with no costs accruing in a dormant state. However, deploying to a serverless API requires maintaining functionality and performance.

Additionally, managing and monitoring costs for each tenant’s resources is vital in multi-tenant environments, ensuring accurate billing for services like high availability, disaster recovery, and autoscaling. Consider service provider discounts such as Reserved Instances, Savings Plans, Commitment Discounts, Subscription Discounts, etc., offered to users in exchange for a long-term commitment to spend with that cloud provider.

The FinOps lifecycle: Inform, optimize, and operate

The FinOps lifecycle integrates three key phases: inform, optimize, and operate. Organizations should continuously cycle through these phases to maintain financial accountability in the cloud.

The inform phase focuses on near real-time visibility into cloud spending to build a detailed understanding of an organization's costs. It involves mapping costs to applications and business units, which teams can analyze to create budgets, forecasts, cost dashboards, and scorecards.

The sections below detail the different aspects of the FinOps inform phase and Kubernetes cost categories.

Kubernetes resource spend analysis

A standardized tagging strategy is essential for visibility into usage and spending. Identifying and tagging untagged resources is crucial for complete cost transparency and accurate chargeback. This process equips the FinOps team with data for cost allocation and optimization opportunities.

Pod and node-level cost analysis

Implementing a labeling strategy is the best practice for tracking usage for pod and node-level granularity. For example, DevOps teams can use labels to deactivate non-essential resources during holidays, separating costs in multi-tenant environments. They can do this by distinguishing between different resources within the same namespace.

Using namespaces simplifies resource management in Kubernetes environments with multiple teams and projects. They establish clear divisions between teams and applications, facilitating better organization. In Kubernetes clusters, each resource belongs to a single namespace. Monitoring each namespace helps identify teams or services with higher cost overhead, aiding optimization efforts.

The ResourceQuota object allows the allocation of resources to each namespace, ensuring fair distribution across the Kubernetes cluster. This includes setting limits for memory and CPU usage, helping maintain resource efficiency, and preventing resource hogging.

Labeling strategy

Kubernetes labels are key-value pairs in an application's metadata. Kubernetes labels offer a simple technique for identifying Kubernetes objects and aligning them into groups. There are standard labels and custom labels. Kubernetes uses standard built-in labels to schedule pods to nodes, manage replicas of deployments, and route network services. Custom labels are very similar to the tagging concept in AWS: AWS tagging also relies on key-value pairs that identify AWS resources such as EC2, S3, and EFS.

Below are examples of recommended labels for use in Kubernetes:

{	
"app.kubernetes.io/name": "Represents the name of the application",
"app.kubernetes.io/part-of": "Indicates which application or system this component is part of",
"app.kubernetes.io/managed-by": "helm" #Specifies the entity (such as a tool or operator) responsible for managing the application
}

Kubernetes labels allow DevOps teams to optimize Kubernetes API searches, apply configurations, and manage deployment administration. Labels also enable the implementation of a cost-monitoring mechanism by identifying pod-level resource usage for different environments or applications. Below is an example custom label.

{
"metadata":{
  "labels":{
    "tenant":"account134",  
    "environment":"dev",
    "tier":"backend",
    "version":"2.0.2",
  }
}

Multi-tenant and shared resources visibility

In a multi-tenant environment, using Kubernetes search APIs with custom labels allows DevOps teams to monitor the status of pods based on their environment. By utilizing key-value pairs that identify the environment, such as "environment":"dev","environment":"test","environment":"prod",

Teams can easily filter and list the status of pods. For example, the command below lists the status of all production pods:

kubectl get pods -l 'environment=prod' 

Furthermore, in a multi-tenant setup, understanding the services used by a particular tenant is essential. By filtering based on the "tenant" label, such as "tenant": "account134", and further refining with tier labels like "tier": "backend" and "tier": "frontend" teams can collate data specific to that tenant's usage.

While labeling simplifies cost insights, accurately allocating costs in shared, autoscaled environments presents challenges.

Cost allocation often involves assigning a tenant's proportional usage of resources, including CPU, GPU, memory, disk, and network. In this context, a concept like the Unit of Economics, which is the smallest measure of resource consumption that can be tracked and analyzed to understand costs and optimize spending, provides a framework for FinOps teams to concentrate on the costs associated with each tenant or project, enabling targeted efforts toward optimizing expenses.

Mapping Kubernetes cost to business value

Mapping Kubernetes costs to understand business value involves considering various factors beyond container expenses. When allocating costs to consumers, it's crucial to account for compute node costs, operational expenses, storage costs, licensing fees, observability tools, and security services.

Management/cluster operational costs

Cloud service providers charge for managing the cluster, including self-managed container orchestrator nodes. Additional expenses may arise from edge services like web application firewalls and load balancers.

Storage costs

Containers consume storage, and while container storage may be temporary, the nodes’ host operating system and backup storage used to operate a production cluster should be allocated to workloads.

Licensing

Consider licensing costs for the host operating system and any software packages running on the host OS. Licensing fees may also apply to software used within containers.

Observability

Metrics and logs sent from the cluster to services like Splunk Cloud or Datadog incur costs. These costs should be allocated to teams utilizing the observability tools.

Security

Cloud service providers offer security-related services, which may come with additional costs. These expenses should be allocated to teams benefiting from enhanced security features.

Addressing the costs associated with containerization involves understanding both static and runtime expenses. Here’s a breakdown of these costs:

Static costs

• Solution creation: When creating a solution within a container, it’s essential to ensure its quality and assess how it impacts CPU, network, and storage upon deployment.
• Stateless and stateful containers: Static costs vary depending on whether the container is stateless or not.

Runtime costs

• Bandwidth: Often underestimated, bandwidth can significantly affect cloud computing charges.
• Forgotten deployments: A containerized application left deployed can lead to unexpected bills. It's crucial to remove applications or data from the cloud when they’re no longer needed.
• Polling data: Polling in the cloud is expensive and incurs transaction fees, which can add up based on frequency.
• Unintended traffic: DoS attacks or web crawlers can unexpectedly increase traffic. Implementing security audits and controls, like CAPTCHAs, can mitigate these costs.
• Monitoring: Regularly monitor application health and billing, review cloud necessities, and adjust deployments to match the load.

Allocating shared expenses

• Networking and storage: Assign these costs to specific projects, teams, namespaces, and applications.
• Autoscaled multi-tenant services: For architectures with autoscaled pods supporting multi-tenant services, map Kubernetes units like pods, deployments, or namespaces to an economic unit for cost calculation.

By establishing methods to allocate costs, FinOps teams can focus on the costs associated with single tenants, teams, or applications alongside other static and runtime costs, thus aligning Kubernetes expenses with actual business value. This approach ensures a comprehensive understanding of where resources are consumed and provides a basis for optimizing cloud expenditures.

Optimizing your Kubernetes environment involves leveraging your cloud vendor's cost governance and optimization tools to review costs, identify trends, and eliminate resource wastage. These tools help identify underutilized resources and offer options for purchasing reserved instances or creating savings plans to lower long-term costs. Additionally, comparing expensive resources against similar third-party or cloud vendor services can provide cost-saving opportunities.

In these sections, we’ll review how organizations can optimize their Kubernetes environments effectively.

Pod resizing and right-sizing

Review and update pod resource requirements regularly to prevent budget overruns. Identify pods with surplus resource allocation and optimize them to free up resources. Define appropriate values for resource requests and limit parameters to ensure stable pod operation and efficient resource utilization.

Right zone

Distribute workloads to cost-effective regions, zones, and nodes. Consider the pricing variations across different regions and zones cloud providers like AWS offer. Moving applications to cheaper parts of the cloud can help optimize costs.

Right time

Implement an optimization strategy for scheduling workloads across different nodes. Kubernetes can assign workloads to specific servers by grouping applications and node groups, reducing resource allocation costs.

Cost anomaly alerts

Set alerts to identify unusual usage or cost spikes. These alerts help the FinOps team analyze resource usage effectively and take corrective actions.

Autoscaling optimization

Configure Kubernetes autoscaling to dynamically adjust the number and size of pods based on demand. This ensures optimal performance while minimizing resource wastage and costs. However, it's essential to fine-tune autoscaling setups for efficiency.

Flexibility

Consider leveraging spot instances for specific workloads, which offer extra capacity at lower costs. Additionally, explore commitment discounts such as Reserved Instances or Savings Plans for long-term cost savings.

By adopting these strategies, you can enhance the performance of your Kubernetes environment while keeping costs in check. Remember, the key is to be proactive and continuously monitor and adjust your configurations to align with your organization’s needs and budget.

During the operation phase, you put your cost optimization plan, which you formulated during the optimization phase, into action. While the FinOps team doesn't directly implement changes, they offer guidance and transparency into cloud usage. Ensuring engineering teams are trained in effective cost optimization strategies and empowered to implement recommendations is crucial. Ultimately, the engineering teams make the necessary infrastructure changes to optimize cloud spending.

Kubernetes cost monitoring strategy

A robust strategy is essential for Kubernetes cost monitoring. Cost monitoring tools streamline cost analysis, reporting, and optimization. Since native cloud tools may have limited features for expense tracking, many companies adopt third-party cost management tools.

These tools enable budget forecasting, real-time visibility of pod-level resource utilization, and insightful expense reports. Automation features within these tools are invaluable, as they help detect, analyze, and report any abnormalities in resource usage, preventing budget overruns.

With coherent labeling strategies established, you move into the inform phase of your FinOps journey. Now, you can identify over-provisioned resources and map labels for all business units incurring costs against consumption logs for shared resources. This labeling strategy proves beneficial for:

• Identifying over-provisioned resources.
• Allocating costs to individual teams, applications, or services.
• Adjusting costs by individual team, application, or service.

Leveraging cost-monitoring tools enables informed decision-making in the operating phase. Such tools simplify understanding and calculating the costs of running your applications in the cloud.

Simplifying FinOps Kubernetes practices with cost monitoring tools

Practical Kubernetes cost monitoring tools should:

• Be user-friendly: Easy installation and configuration are key.
• Offer detailed visibility: They should provide granular visibility into costs.
• Integrate with billing systems: Ability to connect to external billing platforms like AWS.
• Be open source: An open-source license ensures broader accessibility and community support.
  

By integrating the right cost monitoring tools, Kubernetes environments can achieve a streamlined FinOps process and enhance organizational understanding and management of cloud costs. Popular cost monitoring tools such as Kubecost, Prometheus, Kubernetes Dashboard, and the ELK Stack facilitate understanding and calculating running application costs in the cloud.

Kubernetes cost monitoring tools

Teams should consider different use cases, benefits, and tradeoffs when considering Kubernetes cost monitoring tools. The sections below cover four popular Kubernetes cost-monitoring platforms.

Kubernetes Dashboard

Kubernetes Dashboard provides a general-purpose web UI for Kubernetes clusters. Its straightforward configuration and installation make it accessible. However, it lacks cost visibility features, such as price per pod or deployment. Additionally, it cannot connect to external billing systems for data collection or enrichment.

Prometheus + Grafana

Prometheus is a leading open-source monitoring framework offering robust Kubernetes monitoring capabilities. While it may be challenging to install and configure, it can collect various metrics from pods and nodes, including CPU, memory, and storage. However, like the Kubernetes Dashboard, Prometheus lacks cost visibility features and integration with external billing systems. Integrating it with Grafana can enhance the visualization of collected metrics.

The ELK stack

Comprising Elasticsearch, Logstash, and Kibana, the ELK Stack is valuable for collecting, analyzing, and viewing Kubernetes logs. It's beneficial for diagnosing and troubleshooting issues in distributed applications. However, it requires complex installation and configuration and does not offer integration with external billing systems or cost visibility for Kubernetes resources.

Kubecost

Kubecost is a specialized cost monitoring and management tool that provides cost visibility and control for Kubernetes clusters. It offers insights into the actual price of Kubernetes resources, such as pods and deployments. Kubecost allocates costs based on native Kubernetes concepts like namespaces or labels, according to resource usage.

It also provides alerts for unexpected spending changes or efficiency improvements. The dashboard allows grouping costs based on dimensions, such as namespace, pod, or service. Additionally, Kubecost enables scheduling cost reports, setting alerts for budget thresholds or spending deviations, and receiving alerts via email, Slack, or Webhook for easy integration with third-party tools.

Kubecost saves reports, offering to customize and save Cost Allocation (and Asset) reports for periodic access.

Kubecost can also integrate with external billing systems like AWS billing.

Five best practices and policies for Kubernetes FinOps

When implementing Kubernetes, adherence to FinOps best practices and policies is crucial for effective cost governance. In this section, we will review five powerful FinOps Kubernetes best practices.

Implement FinOps throughout all Kubernetes lifecycle phases

Following the FinOps lifecycle ensures a structured approach to cost optimization. The five best practices below can help teams improve their FinOps effectiveness throughout the inform, optimize, and operate phases.

Create a labeling strategy

A comprehensive labeling strategy is essential for cost allocation and tracking. Automating this process within continuous integration and continuous deployment (CI/CD) pipelines ensures consistency and accuracy in labeling Kubernetes resources.

Leverage ready-to-use Pod templates

Utilizing pre-defined pod templates streamlines resource provisioning and ensures consistency across deployments. These templates should be optimized for resource efficiency to avoid over-provisioning and unnecessary costs.

Proper sizing and autoscaling

Properly sizing pods and leveraging autoscaling capabilities are crucial to optimizing resource utilization. Rightsizing ensures that pods have adequate resources without over-provisioning, while autoscaling dynamically adjusts resource allocation based on workload demands, reducing wastage and optimizing costs.

Implement Kubernetes cost monitoring tools for effective FinOps

Implementing cost monitoring tools simplifies FinOps by providing real-time visibility into Kubernetes spending. These tools offer insights into resource consumption, identify cost-saving opportunities, and enable proactive cost-management strategies.

Automation and continuous improvement are key to effective Kubernetes FinOps. Automation ensures consistent application of cost-saving measures, while FinOps principles provide a framework for comprehensive cost visibility and optimization. Tagging and labeling are essential for tracking resource utilization, and the use of cloud financial management platforms allows for the integration of cost data into monitoring workflows. Open-source tools like Helm, Kubecost, Argo, and Linkerd offer real-time insights, enhancing efficiency. Finally, adopting a FinOps team and specialized platforms ensures precise cost allocation and optimization, tailoring Kubernetes spending to the needs of complex infrastructures. Organizations that understand and implement these principles and practices can improve operational efficiency while reducing overall costs.