Guide to Kubernetes Cost Management - Part 1
February 12, 2025
Tags:
Cost Management
Cost Optimization
Right Sizing
Kubernetes
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It is undeniable that Kubernetes has revolutionized how organizations deploy and manage applications at scale. However, as its adoption grows, so do the operational costs of running Kubernetes in production.
According to the 2023 Sysdig Cloud-Native Security and Usage Report, 69% of requested CPU resources in Kubernetes environments are left unused, leading to substantial cost inefficiencies. Similarly, a survey by Pepperdata highlights that uncontrolled Kubernetes environments can lead to costs spiraling out of control, making it a growing concern for businesses worldwide.
These rising costs are not just numbers—they’re a wake-up call. Without proper visibility and control, Kubernetes environments can become a significant financial burden.
This is why proactive Kubernetes cost management is no longer optional—it’s a necessity. Taking proactive measures to manage and optimize costs isn’t just about saving money; it’s about building efficient, scalable, and sustainable systems.
In this first part of a two-part blog series, we will explore the fundamentals of Kubernetes cost management, breaking down the factors that contribute to Kubernetes cost and the challenges organizations face in controlling them. Understanding these challenges is the first step toward building a cost-efficient and scalable Kubernetes environment.
At its core, Kubernetes cost management is all about ensuring that your Kubernetes environment is both efficient and cost-effective. It involves identifying areas where resources—like compute, storage, and network are either overused or underused and making adjustments to match resource consumption with actual workload needs.
To give a few examples:
Underutilized Workloads: If a workload is allocated 1GiB of memory and 1 CPU but only uses 500MiB and 0.5 CPU, the excess allocation leads to wasted resources and higher costs. Adjusting the allocation to match actual usage can save money.
Underutilized Clusters: Assume a cluster with 6 nodes, each with 4 CPUs and 16GB RAM. You have 10 microservices with 40% CPU allocation, and 60% memory allocation. You could reduce at least one node to save on costs. Given the workloads are more memory bound, you could also investigate the possibility of using memory optimized nodes for further cost reduction.
Cost management focuses on gaining visibility into how and where costs are incurred and implementing appropriate cost optimization strategies. These strategies include rightsizing workloads & clusters, automating scaling, cleaning up unused resources & promoting accountability. The following section explains these strategies in detail.
For engineers and FinOps teams, the goal is simple: maximize the value of every dollar spent on Kubernetes. By implementing cost management and optimization strategies, organizations can maintain high performance and scalability without letting costs spiral out of control.
Before thinking about managing and optimizing cost, it’s important to understand the fundamental cost models and different cost factors associated with running Kubernetes in production.
Kubernetes cluster costs can be divided into 3 segments:
Tip:
Resource allocation costs are incurred when you reserve them. Therefore over allocation of resources at this level has a direct impact on cost. Ex Adding too many nodes in a cluster due to poor capacity planning or auto scaling up due to over allocated workloads (containers) will drive up the cost.
Now let's look at how these costs are allocated to workloads. A container is the smallest unit of allocation. Gaining visibility and optimization needs to start at this level as over-allocated containers can increase the resource allocation costs needlessly.
An often hidden source of cost at this level is network bandwidth utilized by a container which can drive up costs significantly as explained in the Network Cost section below.
Compute costs refer to the expenses associated with the actual processing power required to run your containerized applications. There can be various factors that contribute to the compute costs:
For example, a team might allocate 8 CPUs and 16 GB of memory to a set of Microservices that only needs 4 CPUs and 8 GB to perform optimally. In the managed Kubernetes setup, this means paying more than you’re actually using and on-prem it may result in you having to procure more hardware to make room for new applications.
Storage costs arise from the data your applications store - including both types: persistent data (e.g., databases) and ephemeral data (e.g., temporary files).
In Kubernetes, these costs may depend on:
For example, a development team may create temporary environments that generate large logs stored in persistent volumes. After the environments are deleted, the volumes may remain active, unnecessarily increasing storage costs.
In Kubernetes, network costs are driven by egress traffic, cross-zone communication, and cross-region data transfers. These costs are heavily influenced by high transaction volumes and can be further amplified by misconfigured or inefficiently designed applications.
As an example, in a multi-availability-zone (multi-AZ) Kubernetes deployment, running a Kafka cluster can inadvertently lead to significant cross-zone data transfer costs. This occurs because Kafka brokers and their corresponding partitions may be distributed across different availability zones (AZs). When producers and consumers interact with these brokers, data often traverses AZ boundaries.
For instance, if a producer sends data to a broker in a different AZ, or a consumer fetches data from a broker located elsewhere, this inter-AZ communication incurs additional charges. Moreover, Kafka's internal mechanisms, such as replication between brokers for fault tolerance, can further amplify cross-zone traffic.
Each replica must synchronize data across AZs, leading to increased data transfer volumes and, consequently, higher costs. Therefore, without careful planning and configuration, deploying a Kafka cluster in a multi-AZ Kubernetes environment can result in unforeseen and substantial cross-zone data transfer expenses.
Kubernetes offers unparalleled flexibility for deploying and managing containerized applications, but its dynamic and distributed nature presents significant challenges in cost management.
Let us discuss some key challenges faced when trying to control Kubernetes costs.
One of the biggest challenges in Kubernetes cost management is the lack of clear visibility into where costs are coming from. This is due to the difficulty of attributing compute, storage, and network expenses from the cloud-vendor billing to specific workloads. This opacity complicates identifying cost drivers, optimizing resource usage, and controlling spending effectively in Kubernetes environments.
Platform teams often look to answer two critical questions:
Kubernetes environments are highly dynamic by nature, with workloads and nodes scaling up and down regularly. This makes it hard to pinpoint which application or resource is driving up costs. One day, you might see a sudden spike in your cloud bill but have no clear way to pinpoint exactly which workload or resource is responsible.
Similarly, workloads or environments that are no longer needed often remain active, consuming resources unnecessarily and adding to the bill as dormant (idle or abandoned) workloads. Lack of visibility into dormant workloads is another challenge faced by platform teams in controlling costs.
Effective visibility not only helps identify cost drivers but also lays the foundation for implementing other strategies like rightsizing, dormant workload detection and accountability frameworks. (discussed in the next section)
Rightsizing is one of the most effective strategies for Kubernetes cost management, which involves allocating just the right amount of resources to workloads based on actual usage patterns. (discussed in the next section)
Rightsizing is typically performed at two levels:
Without effective workload-level rightsizing, platform teams struggle to properly rightsize their clusters and control cluster costs. In environments with numerous workloads and clusters, teams rely on engineering to determine rightsizing or select from a set of predefined t-shirt-sized options.
However, for engineering teams, it's often challenging due to the lack of clear guidelines on what and how to rightsize. For instance, many teams allocate more resources than necessary to "play it safe" and avoid performance issues (over-provisioning).
While t-shirt-sized right-sizing attempts to provide guidance, it’s often sub-optimal as engineering teams try to “play it safe” or choose an option but rarely revisits to adjust afterwards. Static rightsizing guidance could help to a certain extent but without a feedback loop to assess the effectiveness it remains ineffective and sometimes contributes to more costs.
Questions like "Which workloads are consuming excessive resources?" or "How much should we scale down without affecting performance?" often go unanswered without detailed usage insights (feedback loop).
Without detailed monitoring and insights, rightsizing efforts can miss the mark, leading to wasted resources or performance issues. Addressing this challenge requires robust tools and clear guidelines to balance cost efficiency and workload performance effectively.
Establishing accountability for costs within an organization is frequently mentioned as a key challenge by decision makers and platform teams. Many enterprise customers we speak with share a similar sentiment:
Currently, the platform team covers the majority of the costs, but we want to establish a transparent and fair chargeback model. When teams are accountable for their share, they'll take cost optimization more seriously.
A chargeback mode provides a methodology where costs are allocated to specific teams or projects based on their allocation of resources or the actual usage or a combination of both. This helps teams understand the financial impact of their decisions and promotes cost-conscious behavior. Therefore it is an effective tool in driving accountability and awareness to reduce wastage and controlling costs.
However, in large organizations with distributed environments, building and maintaining such accountability frameworks can be complex. It can be broken down into two key challenges
Identifying the ownership can be challenging when there is no clear ownership structure behind the applications. Additional complexities arise in how to attribute the cost of common infrastructure services & models, licensing & subscription costs of 3rd party products as well as the overhead of maintaining the platform which includes cloud costs, platform team wages & salaries and support contracts.
In discussing the visibility challenge, we highlighted the difficulty of attributing compute, storage, and network expenses from the cloud-vendor billing to specific workloads. This in turn remains a key challenge in attributing cost to teams, projects and products when building a chargeback model. Identifying an ownership structure, along with implementing cost monitoring and visibility tools can help lay the foundation.
Organizations can utilize that foundation to foster a culture where cost accountability is shared across teams, supported by detailed usage reports and actionable insights.
Many organizations use multiple cloud providers or hybrid setups, combining on-premises infrastructure with cloud platforms. While this is a modern approach and provides flexibility, it introduces cost challenges due to different cloud pricing models, billing structures, and data transfer fees.
The more complex the environment gets (which is inevitable), the harder it is to manage costs effectively. Without a unified approach to cost management, teams may end up spending more time and effort trying to make sense of multiple, distributed bills across platforms.
In such an environment the previous challenges are amplified, highlighting the need for laying a proper foundation to implement visibility, rightsizing guidelines and chargeback models to effectively manage the cost complexity of Multi-Cloud & Hybrid Cloud setups.
Kubernetes enables powerful automation and scalability, but uncontrolled costs can quickly become a financial burden if not managed properly. Without clear visibility, structured cost allocation, and effective policies, organizations risk wasting resources, over-provisioning workloads, and encountering unexpected expenses.
In this first part, we explored the key cost factors and challenges that make Kubernetes cost management difficult. In Part 2, we’ll dive into practical strategies for cost optimization, covering observability, rightsizing, scaling techniques, and best practices that help teams gain control over Kubernetes expenses while maintaining high performance and reliability.
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