Amazon Elastic Kubernetes Service (EKS) has rapidly become the go-to solution for organizations deploying, managing, and scaling containerized applications with Kubernetes. EKS handles the heavy lifting of cluster management, freeing teams to focus on shipping applications rather than wrestling with control plane operations.
As organizations scale their Kubernetes environments, though, managing and optimizing costs becomes a pressing concern. Clusters grow, workloads multiply, and without deliberate cost governance the bill can quietly balloon.
Understanding both the optimization strategies available and the tooling that supports them is essential for any team serious about getting value from their EKS investment. Many companies have turned to third-party tools designed specifically to identify and address cost savings within Kubernetes clusters.
Tools like Goldilocks and Kubecost are prime examples of how organizations can gain actionable insights into their Kubernetes resource usage, helping to optimize allocations and reduce overall costs. But are these tools the best option for you?
This article walks through the core strategies for EKS cost optimization, then evaluates the leading add-ons and integrations — Goldilocks, Kubecost, and CloudZero — that help teams put those strategies into practice.
How Third-Party Tools Optimize EKS Clusters
Third-party tools designed for Kubernetes environments, such as Amazon EKS, play a vital role in assisting organizations to optimize costs. These tools provide granular insights into resource usage, helping organizations identify and eliminate inefficiencies, as well as streamline their deployments to align more closely with actual resource needs.
This level of optimization is crucial within scaling Kubernetes environments, as the potential for unnecessary costs can escalate quickly.
By leveraging third-party tools, organizations can achieve a balance between performance and cost, ensuring that their EKS clusters are not only efficient but also cost-effective. Goldilocks and Kubecost are two prominent examples of third-party tools that have been designed with cost optimizations in mind.
Goldilocks helps organizations to right-size their Kubernetes deployments, ensuring that resources are allocated optimally, thus preventing over-provisioning and reducing wastage. Similarly, Kubecost provides detailed insights into spend within a cluster, enabling organizations to pinpoint where their money is going and how it can be better utilized.
Before diving into specific tools, it helps to understand the optimization levers they act on. Most EKS cost waste falls into a few predictable categories: overprovisioned pods, underutilized nodes, missed discount opportunities, and invisible data transfer charges. The tools below address these from different angles.
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Core EKS Cost Optimization Strategies
Effective EKS cost optimization starts well before installing any tool. Three strategies consistently deliver the largest impact across production clusters.
Rightsizing Pods And Nodes
Overprovisioning is the single largest source of Kubernetes cost waste. A pod requesting 500m CPU but consistently running at 120m wastes roughly 75% of its scheduled capacity. Multiply that across dozens of deployments, and the unused resources add up to entire nodes sitting near-idle. Rightsizing means setting CPU and memory requests to match actual P95 usage, then packing more workloads onto fewer, appropriately sized instances. Tools like the Vertical Pod Autoscaler (VPA) in recommendation mode surface these gaps automatically.
Autoscaling With Karpenter
For node-level scaling, Karpenter has emerged as the preferred provisioner for EKS clusters. Unlike the older Cluster Autoscaler, which operates within predefined node groups, Karpenter provisions exactly the right instance type for pending workloads in real time — often launching a node in under a minute compared to three or four minutes with node-group-based approaches. This precision reduces both the time workloads wait for capacity and the overhead of maintaining multiple node groups with different instance configurations.
Leveraging Spot Instances
Spot instances can reduce compute costs by up to 90% compared to on-demand pricing, making them one of the most powerful levers available. Safe adoption requires stateless workloads, multi-AZ and multi-instance-type spread, and PodDisruptionBudgets to handle preemption gracefully. Spot works well for batch processing, queue workers, and stateless microservices. It should be avoided for control plane components, single-instance databases, and workloads that cannot tolerate interruption.
Deep Dive: Goldilocks
Goldilocks deployed within an EKS Cluster
Goldilocks, maintained by Fairwinds, is uniquely positioned to tackle the rightsizing challenge within Kubernetes environments. It operates by deploying the Vertical Pod Autoscaler (VPA) in recommendation mode across namespaces you opt in via labels, then surfaces those recommendations through a clean web dashboard.
Rather than blindly applying autoscaling changes, Goldilocks shows teams the gap between what their pods request and what they actually consume, organized by namespace and deployment. This makes it a low-risk starting point for organizations beginning their cost optimization journey — you review the recommendations, decide what to act on, and adjust requests and limits at your own pace.
In practice, Goldilocks works by deploying a set of controllers within your Kubernetes cluster. These controllers are responsible for collecting data on resource usage and comparing it to the limits set by cluster admins.
If the limit is too high or too low compared to actual usage, Goldilocks provides concrete recommendations on what the ideal settings should be. Recent updates have expanded support beyond Deployments to include DaemonSets and StatefulSets, giving broader coverage across production workloads. Organizations typically report 30–50% reductions on overprovisioned resources after implementing Goldilocks recommendations.
With these insights, teams can fine-tune their deployments, optimize resource usage, and unlock cost savings that contribute to a better bottom line.
Deep Dive: Kubecost
Kubecost focuses on cost visibility: understanding exactly where money goes inside a Kubernetes cluster. It breaks down costs by namespace, deployment, service, and label, giving organizations a detailed map of their spending patterns.
Under the hood, Kubecost is built on OpenCost, a CNCF incubating project that provides the open-source cost allocation engine. Kubecost layers enterprise features on top — budget alerts, RBAC-based access controls, savings recommendations, and automated cost policy workflows. A free tier is available for single-cluster visibility, while business plans start at $449 per month for teams needing multi-cluster support and advanced governance. IBM acquired Kubecost in 2024, and the platform is now being integrated into IBM’s broader FinOps suite.
In addition to its cost analysis capabilities, Kubecost offers budgeting and forecasting tools, allowing organizations to set budgets for their Kubernetes environments and receive alerts when spending is at risk of exceeding targets. The forecasting feature predicts future costs based on historical data, helping teams plan and allocate resources more effectively.
How Can CloudZero Help?
Adding to the list of essential solutions for optimizing costs within EKS environments, CloudZero stands out as a pivotal platform for teams looking to enhance their financial observability within cloud environments, including Kubernetes clusters.
The Cloudzero cloud cost intelligence platform is designed to provide real-time insights into cloud costs, empowering businesses to understand where every dollar is spent. This level of visibility is crucial, especially when dealing with the complexities of EKS clusters, as it enables organizations to precisely pinpoint areas where costs can be trimmed.
CloudZero capabilities are tailored to provide a granular view of cloud spending, breaking down costs by service, team, and environment.
This is particularly valuable for administrators managing EKS clusters, as they can easily identify which specific deployments or services are driving costs and make informed decisions to optimize resource usage.
Where Goldilocks focuses on pod-level rightsizing and Kubecost on cluster-level cost allocation, CloudZero operates at the business layer. Its Dimensions feature maps Kubernetes spending to teams, products, features, and individual customers — enabling unit economics analysis that tools focused purely on infrastructure cannot provide. For organizations running multi-tenant EKS clusters, this means understanding not just that a cluster costs a certain amount, but which customer or product line that cost serves.
Additionally, CloudZero offers anomaly detection, which automatically alerts administrators of unexpected spikes in spending, thereby preventing budget overruns.
By integrating CloudZero with EKS, organizations are equipped with the solution they need to not only lower costs and identify inefficiencies but also align their cloud spending with business outcomes, ensuring they are getting the most value out of their investment into Kubernetes.
Conclusion
Optimizing EKS costs is equal parts strategy and tooling. The foundational practices — rightsizing pods, scaling efficiently with Karpenter, and leveraging Spot instances for fault-tolerant workloads — address the most common sources of waste. Add-ons like Goldilocks and Kubecost turn those strategies into data-driven workflows, while CloudZero connects infrastructure spending to business-level questions about unit economics and customer profitability.
As the adoption of Kubernetes continues to grow, the importance of cost optimization within these environments cannot be overstated. Organizations must be proactive in managing their EKS-related expenses to maximize the value of their investments.
No single tool solves every cost challenge. The most effective approach combines pod-level recommendations (Goldilocks), cluster-level cost visibility (Kubecost), and business-level cost intelligence (CloudZero) into a layered optimization strategy that scales with the organization.
Software and pricing information last verified May 2026. Features, pricing, and availability may have changed. Please verify current details with vendors before making decisions.
