July 7, 2025
Kubernetes Performance Mastery: Expert Guide to Cluster Rightsizing
6 min read
Did you know that over 78% of Kubernetes usage is wasted due to over-provisioning? Our recent study of more than $2.5 million annual EKS spend revealed this startling inefficiency in Kubernetes performance management.
According to Gartner, by 2027, more than 70% of enterprises will use industry cloud platforms, compared to less than 15% in 2023. This rapid adoption makes Kubernetes performance optimization more critical than ever. Through effective Kubernetes performance monitoring and applying cluster size best practices, organizations can achieve remarkable results. For instance, one major Kubernetes user saved $500,000 in cloud costs within 60 days, representing a 25% cost reduction. However, rightsizing isn't a one-time effort; workloads evolve, and traffic patterns shift, requiring ongoing attention.
In this blog, we'll explore Kubernetes performance tuning through cluster rightsizing. We'll cover resource allocation fundamentals, workload profiling techniques, implementation of autoscaling, and automation strategies to help you maximize efficiency while maintaining application stability.
Understanding Kubernetes Resource Allocation
Resource allocation forms the foundation of effective Kubernetes performance optimization. Recent studies show that CPU utilization in Kubernetes clusters averages only 13%, with memory usage at approximately 20%, indicating significant resource misallocation.
CPU and Memory Requests vs Limits
Kubernetes employs two primary mechanisms to control resource allocation: requests and limits. Requests represent the minimum guaranteed resources a container needs to function, essentially serving as a reservation. Limits define the maximum resources a container can consume, preventing resource monopolization.
The difference between CPU and memory behavior is notable. CPU is considered a "compressible" resource; when containers exceed their CPU limits, Kubernetes throttles them rather than terminating them. Memory, conversely, is "non-compressible"; when containers exceed memory limits, they face termination through Out of Memory (OOM) kills.
For optimal Kubernetes performance, it's crucial to understand Quality of Service (QoS) classes:
Guaranteed: Requests equal limits, providing the highest stability
Burstable: Requests lower than limits, allowing flexible resource usage
Best Effort: No requests or limits specified (not recommended)
Impact of Over-Provisioning on Cluster Efficiency
Over-provisioning, setting requests significantly higher than actual usage, leads to substantial Kubernetes performance inefficiencies. Research indicates this problem is worsening, with the gap between CPU requests and actual provisioning widening from 37% to 43% within a year.
Furthermore, over-provisioning creates "hidden costs" through:
Wasted infrastructure expenses
Reduced pod density per node
Higher energy consumption and carbon footprint
Under-Provisioning and Application Stability Risks
Under-provisioning presents equally serious challenges to Kubernetes performance monitoring and stability. When resource requests are set too low, pods may receive insufficient resources, consequently triggering several critical reliability risks:
Memory-related risks include under-provisioned memory requests (6% of reliability issues) and under-provisioned memory limits (2%), potentially leading to pod evictions and OOM events.
CPU-related risks primarily manifest as CPU throttling (15% of reliability issues) and under-provisioned CPU requests (13%), resulting in degraded application performance and increased latency.
The consequences are particularly severe for stateful workloads where data persistence is critical. Under-provisioning in these scenarios can lead to system crashes, data corruption, and extended service disruptions.
Effective Kubernetes performance tuning requires careful balancing, neither under- nor over-provisioning while following Kubernetes cluster size best practices to achieve optimal resource utilization.
Profiling Workloads for Rightsizing Decisions
Effective rightsizing decisions depend entirely on accurate workload data. Without proper metrics, any attempts at Kubernetes performance optimization become mere guesswork. Fortunately, several powerful tools exist to gather the essential metrics needed for informed decisions.
Using Kubernetes Metrics Server for On-Demand Resource Metrics
The Metrics Server acts as the foundation for Kubernetes performance monitoring, collecting resource metrics from kubelets and exposing them through the Kubernetes API. It fetches point-in-time CPU and memory usage data every 15 seconds, making it ideal for monitoring and autoscaling decisions. To deploy the Metrics Server:
After installation, verify deployment success with
And then, test functionality using kubectl top nodes. Despite its usefulness, remember that Metrics Server data isn't suitable for historical analysis.
Prometheus + Grafana for Historical Usage Trends
For comprehensive Kubernetes performance tuning, historical data analysis becomes essential. Prometheus collects and stores metrics over time, whereas Grafana transforms this data into actionable visualizations. Together, they enable teams to:
Identify resource usage patterns across different periods
Establish benchmarks for future optimization
Generate forecasts for resource utilization efficiency
This historical perspective helps teams make better choices regarding resource allocation and identify stranded resources that drive up costs unnecessarily.
Goldilocks for VPA-Based Recommendations
Goldilocks simplifies Kubernetes cluster size best practices by leveraging the Vertical Pod Autoscaler in "Off" mode to generate resource recommendations without actually changing your configurations. It creates VPA objects for deployments in namespaces labeled with goldilocks.fairwinds.com/enabled=true.
The dashboard visualizes two types of recommendations based on QoS classes:
Guaranteed: Uses VPA "target" field for identical requests and limits
Burstable: Uses "lower bound" for requests and "upper bound" for limits
kube-capacity for Node-Level Resource Visibility
Kube-capacity provides detailed node-level resource information, displaying total CPU and memory requests, limits, and utilization in a single view. Basic usage outputs a node list with aggregated resource metrics:
For deeper analysis, adding the -p flag includes pod-specific details, while -c shows container-level metrics. Additionally, you can filter results by labels using --pod-labels or --namespace-labels flags, making this tool invaluable for Kubernetes performance best practices.
Implementing Rightsizing with Autoscaling
Autoscaling represents the dynamic implementation of rightsizing principles in Kubernetes environments. Through the strategic configuration of autoscale, we can achieve optimal Kubernetes performance without constant manual intervention.
Horizontal Pod Autoscaler (HPA) for Pod Count Scaling
HPA automatically adjusts the number of pod replicas based on observed metrics. Operating as a control loop that runs every 15 seconds by default, HPA monitors resource utilization against target thresholds. When CPU utilization exceeds your specified target (typically 50%), HPA increases the replica count to distribute the workload. Conversely, when utilization drops, it scales down to minimize resource waste.
HPA implementation requires the Kubernetes Metrics Server for basic CPU/memory scaling or custom metrics for more advanced scenarios. By specifying minimum and maximum pod counts alongside target utilization percentages, we ensure both reliability during traffic spikes and cost efficiency during quiet periods.
Vertical Pod Autoscaler (VPA) for Resource Adjustments
Unlike HPA, VPA focuses on right-sizing individual pods by automatically adjusting CPU and memory requests and limits. This optimization improves Kubernetes performance tuning at the container level. VPA operates in four modes:
Auto: Currently equivalent to Recreate mode, adjusts resources by evicting pods
Recreate: Updates existing pods by evicting them when resource recommendations differ significantly
Initial: Sets resources only during pod creation, never changes running pods
Off: Calculates recommendations without applying changes
VPA's components include the Recommender (analyzes usage patterns), Updater (removes pods with incorrect resource settings), and Admission Controller (sets proper resources on new pods).
Combining HPA and VPA for Dynamic Optimization
Though powerful individually, combining HPA and VPA requires careful consideration. The conventional wisdom suggests avoiding their simultaneous use on the same workload when both are configured to use CPU or memory metrics. This caution exists because:
VPA adjusts resource requests that HPA then uses for scaling decisions
HPA tends to increase pod counts while VPA aims to optimize resource allocation
Their competing actions can create scaling conflicts and instability
Nevertheless, they can work complementarily when HPA uses custom metrics (like request rate) while VPA handles CPU/memory optimization.
Best Practices for Autoscaler Configuration
For optimal Kubernetes cluster size, best practices with autoscale:
Configure appropriate stabilization windows to prevent scaling flapping
For HPA, ensure all pods have resource requests properly configured
For VPA, start in "Off" mode during development to gather recommendations before applying
Pair both auto-scalers with Cluster Autoscaler to ensure sufficient node capacity
When using HPA, prefer custom metrics over external metrics when possible
Automating Kubernetes Performance Tuning
Manual optimization of Kubernetes clusters becomes increasingly challenging as environments grow. Automation tools offer a solution by continuously enforcing policies and adjusting resources based on demand.
Policy Enforcement with Polaris
Polaris serves as an open-source policy engine that runs numerous checks to ensure Kubernetes resources follow best practices for security, reliability, and efficiency. With over 30 built-in configuration policies, Polaris validates critical aspects including security configurations, resource efficiency, and network settings.
The tool operates in three key modes: as a dashboard visualizing workload issues, as an admission controller automatically rejecting non-compliant workloads, and as a command-line utility for testing local YAML files. Notably, Polaris can function as a mutating webhook that automatically fixes issues rather than simply blocking deployments.
Optimization Through Dynamic Resource Management
This approach to Kubernetes performance optimization uses automated, continuous resource management. The system analyzes container usage patterns and dynamically adjusts CPU and memory allocations, effectively eliminating both over-provisioning and under-provisioning. Organizations adopting this method have reported cloud cost reductions of up to 80%.
Key capabilities include:
Automatic pod rightsizing based on evolving application demands
Smart pod placement and node consolidation to improve cluster efficiency
Detailed cost monitoring across computing resources
Unlike static configurations, this solution operates continuously in production environments where resource demands shift rapidly and workloads constantly evolve.
Integrating Rightsizing into CI/CD Pipelines
Incorporating Kubernetes performance best practices into CI/CD pipelines ensures configuration issues are caught before deployment. Polaris can be installed within continuous integration systems like GitLab CI or Jenkins to validate infrastructure-as-code files.
Teams configure Polaris to fail builds when detecting critical issues or when the overall configuration score falls below predetermined thresholds. This approach shifts Kubernetes performance tuning left in the development lifecycle, preventing problematic configurations from reaching production clusters.
For optimal Kubernetes cluster size best practices, combining these automation approaches creates a comprehensive performance management strategy, policy enforcement catches misconfigurations early, optimization handles dynamic workloads, plus CI/CD integration ensures consistent application of standards throughout the development process.
Conclusion
Effective Kubernetes performance management requires a balanced approach to resource allocation. Throughout this guide, we explored how proper rightsizing significantly impacts both cost efficiency and application stability. Undoubtedly, the fundamentals of CPU and memory management form the backbone of optimization efforts, with careful consideration of requests, limits, and Quality of Service classes.
Workload profiling stands as an essential prerequisite for making informed rightsizing decisions. Tools like Metrics Server provide visibility, while Prometheus with Grafana offers the historical context necessary for identifying usage patterns. Additionally, specialized tools such as Goldilocks and kube-capacity give targeted insights for resource optimization.
Autoscaling represents the next evolution in performance tuning. HPA adjusts pod counts based on demand, whereas VPA focuses on right-sizing individual containers. Though powerful separately, their combination requires careful orchestration to prevent conflicts while maximizing efficiency.
Automation completes the performance optimization picture. Policy enforcement through Polaris, adjustments, and CI/CD integration creates a comprehensive approach that reduces manual effort while maintaining consistent standards.
The statistics speak for themselves: organizations waste over 78% of Kubernetes resources due to over-provisioning, yet proper optimization can yield 25% cost reductions within months. These numbers highlight why mastering rightsizing techniques matters for every organization running Kubernetes workloads.
We recommend starting with comprehensive workload profiling before implementing any changes. Subsequently, apply autoscaling with conservative thresholds, then gradually refine your approach as confidence grows. Finally, automate the process to maintain optimization over time.
Kubernetes performance mastery remains an ongoing journey rather than a destination. Workloads evolve, traffic patterns shift, and cloud costs fluctuate, all demanding continuous attention to rightsizing. The tools and techniques covered here equip you to meet these challenges, transforming Kubernetes from a resource-hungry platform into a model of cloud efficiency.
Key Takeaways
Master these essential Kubernetes rightsizing strategies to eliminate waste and optimize performance while maintaining application stability.
78% of Kubernetes resources are wasted through over-provisioning - proper workload profiling with tools like Metrics Server and Prometheus is essential for data-driven optimization decisions.
Combine HPA and VPA strategically - use HPA for pod scaling with custom metrics while VPA handles CPU/memory optimization to avoid conflicts and maximize efficiency.
Implement Quality of Service classes correctly - set Guaranteed QoS for critical workloads and Burstable for flexible applications to balance stability with resource utilization.
Automate performance tuning with policy enforcement - integrate tools like Polaris into CI/CD pipelines and use optimization platforms to maintain consistent standards.
Start conservative, then optimize gradually - begin with comprehensive workload analysis, apply autoscaling with safe thresholds, and continuously refine based on actual usage patterns.
Organizations implementing these rightsizing practices report cost reductions of 25-80% within months. Remember that Kubernetes performance optimization is an ongoing journey requiring continuous monitoring as workloads evolve and traffic patterns shift over time.
FAQs
Q1. What is Kubernetes cluster rightsizing and why is it important?
Kubernetes cluster rightsizing is the process of optimizing resource allocation to maximize efficiency while maintaining application stability. It's important because over 78% of Kubernetes usage is typically wasted due to over-provisioning, leading to unnecessary costs and reduced performance.
Q2. How can I profile my Kubernetes workloads for rightsizing decisions?
You can profile Kubernetes workloads using tools like Metrics Server for data, Prometheus with Grafana for historical trends, Goldilocks for VPA-based recommendations, and kube-capacity for node-level resource visibility. These tools provide essential insights for making informed rightsizing decisions.
Q3. What are the differences between Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA)?
HPA automatically adjusts the number of pod replicas based on observed metrics, while VPA focuses on right-sizing individual pods by adjusting CPU and memory requests and limits. HPA is ideal for scaling based on demand, whereas VPA optimizes resource allocation at the container level.
Q4. How can I automate Kubernetes performance tuning?
You can automate Kubernetes performance tuning by using policy enforcement tools like Polaris, implementing optimization platforms, and integrating rightsizing checks into your CI/CD pipelines. These approaches help maintain consistent standards and reduce manual effort in performance optimization.
Q5. What are some best practices for Kubernetes resource allocation?
Some best practices for Kubernetes resource allocation include: understanding the difference between CPU and memory requests vs limits, implementing appropriate Quality of Service (QoS) classes, avoiding over-provisioning to prevent wasted resources, and carefully balancing resource allocation to prevent under-provisioning risks. Regular monitoring and adjustment based on actual usage patterns are also crucial.
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