Kubernetes makes it easy to deploy and scale containerized applications quickly and efficiently, and when built in alignment with best practices, it can increase the reliability, cost-efficiency, and security of deployments. As deployment to production environments grows, many organizations are finding their Kubernetes spending increase, but may not understand how to analyze and manage spend in K8s environments.
To truly understand Kubernetes spending and Kubernetes cost allocation, you need to think about the key cost areas. Cloud spend is one that everyone is familiar with, as K8s is typically deployed on cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Regardless of which cloud platform or platforms you use, cloud spend is likely to be a significant line item in your budget. Other potential expenses include managed Kubernetes services and consulting services to help you deploy and manage Kubernetes more effectively. There are a number of tools available for Kubernetes, both open source and commercial, to help manage resources, automate policy enforcement, improve monitoring and alerting, and much more. These tools help you manage and troubleshoot your Kubernetes environments, but they can also be a component of Kubernetes spending.
There are also indirect costs associated with Kubernetes adoption, including managing security in these ephemeral environments, training and supporting staff, hiring new team members with Kubernetes expertise, and integrations with other systems, such as CI/CD pipelines, monitoring and logging systems, which can also involve development and testing costs. The size and complexity of the Kubernetes environment, level of support required, and cost of cloud resources all contribute to the overall cost of Kubernetes adoption, but the increased agility, efficiency, and reliability typically outweighs those costs.
Cloud spending can be hard to estimate. Cloud spend is the total amount of money that your organization spends on cloud computing services, including the cost of cloud resources, such as compute, storage, and networking, as well as the cost of cloud services, such as managed databases, machine learning services, and application hosting. A key aspect of cloud spend is getting an understanding of your cloud cost allocation and what it needs to look like.
Several factors influence cloud spend, including:
Organizations can reduce their cloud spend by carefully planning their cloud adoption strategy, choosing the right cloud services for their needs, and monitoring their cloud usage.
Managed services, such as Amazon's Elastic Kubernetes Service (EKS) or generic compute instances like Amazon Elastic Compute Cloud (EC2), offer scalability, flexibility, and simpler management, which can streamline operations and potentially reduce infrastructure overheads. EKS abstracts the complexities of Kubernetes management, allowing teams to focus on deployment without needing to understand the complexities of cluster management. However, costs can escalate rapidly without diligent monitoring and management. EC2 provides granular control, but, if not correctly sized or optimized, may lead to over-provisioning or underutilization.
Kubernetes spending considerations aren’t only related to cloud spend. Unlike cloud-based deployments (when costs are variable and tied directly to usage), on-prem deployments require upfront investments in data center compute resources. This includes purchasing and maintaining servers, storage solutions, and networking equipment. There is also facility overhead to consider, which includes cooling, power redundancy, maintaining hardware, and physical security. On-prem, Kubernetes still optimizes container orchestration and can maximize server utilization, but the static nature of on-premises hardware means businesses may over-provision to account for future growth or risk under-provisioning, which could result in performance bottlenecks. Accurately forecasting resource needs and planning capacity is important when building on-prem environments for Kubernetes deployments.
Storage and networking introduce multiple cost dynamics. Storage often requires you to invest in persistent volumes and volume claims, especially when deploying stateful applications. And as your applications scale, storage demands may rise exponentially, which may result in use of more durable and performant storage solutions. It’s also important to consider backup, disaster recovery, and data redundancy.
Kubernetes also requires a robust networking solution to handle service discovery, load balancing, and inter-pod communications. You’ll need to implement and maintain a network overlay, ingress controllers, and network policies, which are part of your overall Kubernetes costs. Finally, you need to consider the bandwidth costs within clusters and for external communications.
Containers are lightweight, encapsulated environments that run applications in isolation. However, as applications scale in a Kubernetes cluster, demand for CPU, memory, and GPU resources across all containers can grow significantly. Misconfigurations or lack of optimization can lead to over-allocation. Over-provisioned container resources can result in underutilized infrastructure and inflated costs, while under-provisioned containers may result in poor application performance.
Kubernetes spending may also include software licenses, vendor support, and managed services. You may use commercial distributions or platforms that come with licensing fees or vendor support contracts. However, software, support, and services can also help you ensure stability and deliver rapid assistance and access to expert knowledge when you need it.
You also need to consider your internal platform engineering and development teams. Deploying, maintaining, and scaling Kubernetes clusters requires specialized knowledge and expertise, particularly at the beginning as you learn more about configuration, monitoring, and troubleshooting. A well-managed Kubernetes environment can also result in long-term efficiencies, faster deployment cycles, and enhanced scalability, which may offset your initial time investments.
Rightsizing your clusters to your workloads can help you with Kubernetes cost optimization by ensuring that you are not paying for unused capacity in your cloud bills or data center expenses. Kubernetes offers a few types of multiple scaling techniques
Cloud providers offer spot instances at discounted rates (up to 90% compared to on-demand prices) to enable you to take advantage of excess compute capacity. Spot instances are best for fault-tolerant and instance type flexible applications. However, they don’t work well when running stateful workloads, because if the provider terminates the spot instance, the data disappears. If you are using spot instances for stateful workloads, make sure you are using a Container Storage Interface (CSI) that replicates your data to ensure real-time availability.
Goldilocks, an open source utility maintained by Fairwinds, uses the VPA to help you identify a starting point for resource requests and limits. Goldilocks creates VPAs for every workload deployed within a namespace, then presents that information in a dashboard to help you decide how to set your resource requests and limits based on your knowledge of your application. Goldilocks is also built into Fairwinds Insights to make it easier for you to rightsize your clusters at scale.
In addition to setting resource requests and limits appropriately, you can make sure that you use the right instance types. In addition to spot instances, every cloud provider offers their own sets of virtual machine or instance offerings, such as:
Select your instance types based on the specific needs of your application or task to ensure cost-effectiveness and performance optimization.
You can also use different geographic locations because the pricing for cloud services can vary based on operational expenses, supply and demand in the area, local regulations, network connectivity, and redundancy and back up. Make sure that you choose the region based on the requirements of the workload. Also, consider data transfer costs and whether the region meets your performance and business requirements.
There are also a lot of different storage options — object storage, block storage, file storage, archival storage, hybrid storage, databases, and data warehouses. Consider the performance of these storage options, how durable and available they are, the data location and compliance requirements. While cost is certainly a consideration, choosing storage solutions depends on the specific requirements for the application, data, and business use case. All the infrastructure components are part of your overall Kubernetes cost management plans.
In terms of cloud computing and Kubernetes spending, ingress and egress costs relate to the expenses associated with data entering and exiting from a cloud provider's data centers. If you have a multi-cluster Kubernetes setup where microservices communicate across different regions and availability zones, you need to consider ingress and egress costs.
Ingress costs relate to data being transferred into the cloud provider's network, but most cloud providers don’t charge for ingress data. When you transfer out of the cloud provider's network to another network or the public internet, those are egress costs. These are based on the volume of data (usually in gigabytes or terabytes) that exits their network. A few areas to look for egress and ingress costs:
Log growth and retention can have a significant impact on spending. Kubernetes generates logs at several levels—pods, nodes, services, and more—capturing events from container statuses to API server requests. As clusters scale and applications multiply, the volume of these logs can escalate dramatically. Every pod's log output, every controller's decision, and every service's traffic pattern contribute to this growing data set. This growth influences storage costs, especially if logs are retained for extended periods. While retaining logs helps with troubleshooting and meeting compliance requirements, it’s important to find a balance by setting appropriate log verbosity levels, leveraging log rotation, and periodically archiving or deleting outdated logs.
In an ephemeral environment like Kubernetes, with workloads orchestrated across various nodes and services, achieving cost visibility becomes both critical and difficult. Kubernetes abstraction can make it difficult to understand the direct costs associated with running specific applications or services. Kubernetes cost monitoring bridges this gap by using namespaces, deployments, pods, and even individual containers to break down costs. This granularity helps teams understand which components drive expenses so they can make informed optimization decisions. Gaining visibility into cluster resource utilization versus allocation can also highlight inefficiencies, such as over-provisioned resources.
Savings plans are one way to manage cloud costs in relation to Kubernetes deployments. A savings plan offers predictable pricing for cloud service usage, usually in exchange for a commitment to a consistent amount of usage (measured in dollars per hour) over a one or three year term. This is particularly relevant in Kubernetes as clusters scale and resource demands fluctuate. Savings plans offer flexibility by allowing users to change instance families, sizes, and switch between compute options or regions, provided it's within the cloud provider's offer. If you have a clear understanding of your Kubernetes workloads and anticipate consistent usage, using a savings plan could result in substantial cost optimizations in your Kubernetes spending.
Fairwinds Insights enables you to better understand and manage your overall Kubernetes spending by ensuring that resources, such as CPU, memory, and storage, are allocated efficiently and effectively across the platform. It also helps you to define and enforce cost control policies, such as label enforcement and mandating settings for resource requests and limits. Try the free tier, available for environments of up to twenty nodes, two clusters, and a single repo. It leverages multiple open-source tools in a fully integrated platform, enabling you to manage Kubernetes at scale and take control of your Kubernetes spending.