Rightsizing Kubernetes Workloads - A Practical Guide
February 13, 2025
Tags:
Rightsizing
Cost Optimization
Best Practices
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Kubernetes gives teams the flexibility to scale applications efficiently, but managing resource allocation is a constant challenge. Over-provisioning wastes resources and drives up cloud costs, while under-provisioning leads to performance bottlenecks, slowdowns, and even crashes.
Striking the right balance is critical, yet difficult—especially in dynamic environments where workload demands fluctuate over time. Set it once and forget it? That doesn’t work in Kubernetes.
This is where rightsizing comes in.
Rightsizing in Kubernetes ensures that workloads get the right amount of CPU and memory—neither too much nor too little. By continuously analyzing resource usage patterns, teams can optimize allocations, lower infrastructure costs, and maintain peak application performance.
But rightsizing isn’t just about cost savings—it’s also about stability. Poorly set resource limits can cause cascading failures, making proper rightsizing essential for both financial and operational efficiency.
In this guide, we will explore the fundamentals of Kubernetes rightsizing, the challenges teams face when implementing it, and practical strategies to ensure optimal resource allocation. We will also discuss how data-driven insights can make the process more effective and efficient.
Additionally, we’ll also see how Randoli App Insights simplifies rightsizing by providing actionable recommendations, making rightsizing more effective and cost-efficient at scale.
Rightsizing in Kubernetes refers to the process of adjusting the CPU and memory resources allocated to workloads to match their actual usage patterns. The goal is to ensure that applications have just enough resources to function efficiently without wasting capacity or risking performance degradation.
Kubernetes provides a flexible framework for resource allocation, but it does not automatically optimize resource settings. Many teams either over-provision to avoid instability or under-provision due to incorrect estimations. Rightsizing strikes the right balance by continuously monitoring usage and adjusting requests and limits accordingly.
At its core, rightsizing is about precision—allocating only what is needed while leaving room for workload variability. This not only improves cost efficiency but also enhances cluster stability, ensuring workloads do not experience bottlenecks due to resource constraints.
Inefficient resource allocation impacts both financial costs and system performance. Two common missteps lead to these issues:
Assigning more CPU and memory than a workload requires results in wasted resources and higher infrastructure costs. Since cloud providers charge for allocated capacity rather than actual usage, over-provisioned workloads contribute directly to unnecessary expenses.
Insufficient resource allocation can lead to application slowdowns, increased latency, and even system failures. If a workload does not have enough CPU or memory, it can be throttled or terminated, affecting availability and user experience.
For example, a FinOps team might see a spike in cloud spending due to workloads consistently requesting 2 CPU cores when only 0.5 cores are actively used. Meanwhile, a DevOps engineer might experience frequent OOMKilled (Out of Memory) errors because memory requests were underestimated, causing pods to crash under load.
The key takeaway is that both over-provisioning and under-provisioning lead to inefficiencies. Rightsizing helps mitigate these risks by ensuring that workloads operate within optimal resource boundaries.
Before implementing rightsizing, it is crucial to understand the fundamental resource allocation concepts in Kubernetes.
Every container in Kubernetes is assigned CPU and memory requests, which define the minimum guaranteed resources it will receive, and limits, which set the maximum it can use. Kubernetes schedules workloads based on requests, while limits prevent excessive resource consumption.
For example, if a container requests 500m CPU (0.5 cores) but consistently uses only 100m CPU (0.1 cores), it is over-provisioned. On the other hand, if a container requests 512Mi memory but frequently exceeds this, it risks OOMKilled errors and requires adjustment.
Rightsizing involves continuously analyzing these metrics and making necessary modifications to prevent inefficiencies.
One of the most common misconceptions about rightsizing is that it is a one-time optimization task. In reality, workload demands fluctuate over time due to factors such as:
A static resource allocation strategy does not work in Kubernetes. Instead, rightsizing should be an ongoing practice where teams continuously monitor, adjust, and refine resource settings based on historical data and real-time insights.
By implementing a feedback loop that regularly compares requests against actual usage, organizations can:
This iterative approach is especially valuable for FinOps teams managing cloud costs and DevOps engineers responsible for workload performance.
Rightsizing in Kubernetes is not a single action applied uniformly across all workloads. Instead, it operates at multiple levels, each influencing the overall efficiency of the cluster. By understanding the different dimensions of rightsizing, teams can ensure that resources are allocated optimally across individual workloads, namespaces, and the entire cluster.
In this section, we will break down the key levels of rightsizing and explain how they contribute to both cost optimization and performance stability.
At the most granular level, container rightsizing focuses on ensuring that each container within a pod has the correct amount of CPU and memory allocated. This is the foundation of effective workload optimization.
Kubernetes schedules workloads based on the resource requests set at the container level. If these values are too high, the workload consumes more reserved capacity than necessary, leading to waste. If set too low, the workload may experience throttling or crashes, affecting application reliability.
For example, consider a microservice-based application where a database container has been allocated 2 CPU cores, but its actual consumption rarely exceeds 0.5 cores. This over-provisioning leads to inflated infrastructure costs. By analyzing usage patterns and adjusting requests accordingly, teams can significantly reduce wasted compute resources.
Beyond container rightsizing, pod-level rightsizing ensures that workloads have the appropriate number of replicas running in the cluster.
Pods, which may have one or more running containers, are often scaled to handle varying traffic loads. However, running too many replicas leads to unnecessary resource consumption, while running too few risks application instability.
A common challenge is static replica counts, where applications are set to run with a fixed number of pods regardless of demand fluctuations. This can lead to inefficiencies in both cost and performance.
For example, a web application may be configured to run 10 replicas at all times, even though 5 replicas are sufficient during off-peak hours.
Implementing Horizontal Pod Autoscaler (HPA) or KEDA can dynamically adjust pod counts based on real-time CPU or memory usage, ensuring the right number of replicas are running at any given time.
In multi-tenant Kubernetes environments, namespaces provide logical boundaries between different teams, applications, or projects. Namespace-level rightsizing ensures that resource consumption is distributed fairly across these boundaries, preventing a single team or workload from consuming excessive cluster resources.
Kubernetes provides ResourceQuotas and LimitRanges to control resource allocation at the namespace level:
For example, in an organization where multiple teams share a Kubernetes cluster, FinOps teams may set a ResourceQuota limiting a development namespace to 8 CPU cores and 16GB of memory. This prevents excessive consumption that could impact production workloads.
Note:
Namespace-level resource controls like ResourceQuotas and LimitRanges act as enforcement mechanisms rather than a direct form of rightsizing.
Instead of dynamically adjusting resources, they establish predefined limits to ensure fair resource allocation across teams and prevent excessive consumption. While they don’t optimize workloads in real-time, they play a crucial role in cost control and stability, making them an essential tool for FinOps and platform teams.
Even if individual workloads are optimized, inefficiencies at the cluster level can still drive unnecessary cloud costs. Cluster rightsizing ensures that the right number and type of nodes are provisioned to match actual workload demand, preventing waste while maintaining performance.
Kubernetes clusters operate on a pool of nodes that provide CPU and memory for workloads. If nodes are over-provisioned, the cluster runs idle capacity, leading to inflated infrastructure costs. If nodes are under-provisioned, workloads may compete for limited resources, causing scheduling delays or performance bottlenecks.
For example, consider a Kubernetes cluster running 10 nodes, where workload rightsizing has reduced resource requests by 30%. If this excess capacity is not reclaimed, the cluster still incurs costs for idle nodes. Cluster Autoscaler or Karpenter can automatically downscale underutilized nodes, aligning infrastructure costs with real workload needs.
Best practice:
Before adjusting cluster size, ensure workload rightsizing is complete. Then, use data-driven observability tools such as App Insights to analyze node utilization trends and remove unnecessary nodes, reducing overall cloud spend.
Note: The Relationship Between Workload and Cluster Rightsizing
Workload rightsizing directly impacts cluster efficiency. If workloads are over-provisioned, Kubernetes may scale up nodes unnecessarily, increasing costs.
For example, reducing workload requests by 30% could free up enough capacity to remove 2 out of 10 nodes, leading to infrastructure savings.
Before scaling clusters, first ensure workloads are rightsized to prevent avoidable over-scaling.
Rightsizing seems like a simple concept—adjusting CPU and memory allocations to optimize performance and cost. However, in real-world Kubernetes environments, achieving the perfect balance is far from easy.
Workloads in Kubernetes are dynamic, and resource needs fluctuate, making it difficult to determine the right allocations. Over-provisioning leads to wasted spending, while under-provisioning risks performance degradation.
To ensure stability and efficiency, teams must navigate these key challenges.
One of the biggest challenges in rightsizing is ensuring workloads have enough resources to perform optimally without over-provisioning. Allocating too many resources inflates Kubernetes costs, but scaling down too aggressively can lead to CPU throttling, slow response times, or even pod crashes (OOMKilled errors).
For example, a database workload may appear underutilized based on historical CPU and memory usage, but during peak transaction times, it needs sudden bursts of compute power. Over-optimizing in this case could lead to slow queries and degraded application performance when demand spikes.
Why this matters: Historical usage alone isn’t always a perfect indicator of future needs. Rightsizing isn’t a one-time adjustment—it requires continuous monitoring to adapt to changing workload patterns.
Rightsizing is more than just adjusting numbers—CPU and memory behave differently in Kubernetes, making their allocation complex.
For example, a machine learning pipeline may have high memory needs but low CPU usage, while a real-time analytics service might require bursty CPU loads with moderate memory consumption. Misconfiguring these allocations can lead to either wasted resources or application instability.
Why this matters: Teams must analyze workload behavior before setting requests and limits—blindly adjusting values can create more problems than it solves.
Not all workloads have predictable resource consumption. Some applications follow steady usage patterns, while others fluctuate based on traffic spikes, external events, or batch-processing schedules.
For example, a static internal API may have consistent CPU and memory requirements, making it easier to rightsize. However, a web service, machine learning model, or e-commerce platform requires frequent rightsizing adjustments to adapt to changing demand.
Consider an e-commerce website—on a normal day, its traffic might be stable, but during a flash sale, CPU and memory usage can spike 10x. If the system was rightsized based on average usage, it wouldn’t handle sudden traffic bursts, leading to performance degradation or downtime.
Why this matters: Rightsizing must be adaptive—workloads with variable demand require autoscaling mechanisms alongside rightsizing to remain efficient.
Many teams struggle with rightsizing due to unclear guidelines on how much to allocate and when to adjust resources.
Some teams over-provision to “play it safe”, fearing performance issues, which leads to unnecessary cloud costs. Others rely on static t-shirt-sized configurations that offer predefined CPU/memory values. While these approaches provide some structure, they rarely reflect real-time workload demand. Without a feedback loop, teams miss opportunities to refine allocations, leading to either wasted resources or performance bottlenecks.
Additionally, in large-scale environments, platform teams depend on engineers to manually determine the right sizing. However, without detailed monitoring and usage insights, answering critical questions like "Which workloads are consuming excessive resources?" or "How much can we scale down without impacting performance?" becomes difficult.
Why this matters: Rightsizing is an ongoing process, not a one-time task. Teams need historical data, real-time monitoring, and iterative feedback to optimize resources effectively.
A common misconception is that rightsizing automatically reduces Kubernetes costs. While it optimizes resource allocation, it doesn’t necessarily lower infrastructure costs unless idle nodes are also removed.
For example, if a cluster runs on three nodes, rightsizing workloads alone won’t reduce costs if all three nodes are still running. Kubernetes schedules workloads efficiently, but unless the workloads can be consolidated onto fewer nodes, the total compute cost remains unchanged.
To achieve real cost savings, rightsizing should be paired with node-level autoscaling tools like Cluster Autoscaler or Karpenter to adjust cluster size based on available resources.
Why this matters: Optimizing workloads without optimizing the cluster leaves cost savings on the table. Workload rightsizing should be combined with cluster rightsizing for maximum efficiency.
These challenges highlight a fundamental truth – rightsizing in Kubernetes is not a one-time task but a continuous, data-driven process. Achieving the right balance between cost efficiency, workload performance, and infrastructure optimization requires more than just adjusting resource requests and limits.
By adopting some proactive rightsizing strategies, organizations can ensure that Kubernetes workloads are not just cost-efficient but also high-performing and resilient in dynamic environments.
Rightsizing requires more than just adjusting CPU and memory allocations—it needs a structured, data-driven approach to ensure workloads remain efficient, stable, and cost-effective.
Below are four key strategies that help teams rightsize Kubernetes workloads effectively.
Setting up observability tools is just the first step—the real challenge is acting on the data effectively. Many teams monitor CPU and memory usage but fail to continuously refine their resource requests and limits. Without an iterative feedback loop, workloads remain either over-provisioned or throttled, leading to inefficient resource consumption.
To make rightsizing truly effective, teams should implement an efficiency scoring model that highlights workloads with misaligned requests vs. actual usage. Additionally, analyzing historical usage trends prevent rightsizing decisions based on temporary spikes, ensuring resource adjustments are based on long-term patterns rather than short-term anomalies.
Integrating these insights into a regular review cycle ensures workloads are constantly optimized rather than becoming outdated.
Vertical Pod Autoscaler (VPA) is a powerful tool that automates CPU and memory recommendations based on past usage, but blindly applying VPA’s suggestions can lead to performance risks. VPA lacks business context—it doesn’t differentiate between steady-state applications and bursty workloads, meaning its recommendations may not always align with real-world traffic patterns.
For example, an e-commerce application may experience traffic surges during sales events, but if VPA rightsizes based on normal traffic, the system could struggle under peak loads.
To avoid this, teams should validate VPA’s recommendations by comparing them with real-time observability data. This ensures that automated rightsizing decisions enhance efficiency without compromising performance stability.
Many organizations assume that rightsizing workloads automatically leads to cost savings—but if underutilized cluster nodes remain active, infrastructure costs don’t decrease. Kubernetes schedules workloads efficiently, but unless excess nodes are removed, teams still pay for unused compute power.
To unlock real cost savings, workload rightsizing must be paired with node-level autoscaling. Cluster Autoscaler can dynamically scale down nodes when demand decreases, while Karpenter helps optimize node selection for better cost efficiency. Without this integration, rightsizing only improves resource allocation without delivering financial benefits.
By ensuring that cluster infrastructure scales in alignment with optimized workloads, teams can achieve true cost reductions, not just theoretical efficiency gains.
One of the biggest mistakes teams make is setting CPU and memory requests once and forgetting about them. Workloads evolve—new features, changing traffic patterns, and shifting business needs all impact resource consumption. Static resource allocations become outdated over time, leading to inefficient usage and increasing costs.
To prevent this, rightsizing should be treated as a continuous process rather than a one-time fix. Teams should establish a regular review cadence for adjusting requests and limits based on updated observability data. This ensures that workloads remain optimized as business requirements and infrastructure change.
Additionally, integrating historical efficiency trends into rightsizing decisions helps teams avoid reactive adjustments and instead implement proactive, data-driven optimizations.
Rightsizing in Kubernetes isn’t just about adjusting resource requests—it’s about making sure those adjustments actually improve performance and reduce costs without introducing instability. The challenge is that manual rightsizing doesn’t scale. Tracking usage, analyzing trends, and making workload adjustments across multiple applications is time-consuming and prone to human error. This is where data-driven automation comes in.
Randoli App Insights simplifies the workload rightsizing process by continuously analyzing resource efficiency and providing precise, actionable VPA-based recommendations. It monitors real-time and historical trends, identifying workloads that are consistently over- or under-provisioned, preventing teams from reacting to short-term fluctuations instead of sustained patterns. Unlike static, one-size-fits-all adjustments, App Insight’s 8-day historical model ensures recommendations are based on actual long-term usage, reducing unnecessary over-provisioning.
One of the biggest gaps in rightsizing is translating workload optimizations into real cost savings. If workloads are rightsized but excess cluster nodes remain active, cloud costs don’t decrease. App Insights bridges this gap by providing visibility into how rightsizing decisions impact overall node utilization (idle cost, CPU/memory utilization etc.), helping teams align workload optimizations with cluster scaling for actual financial benefits.
Data-driven automation also helps engineers and FinOps teams align their efforts. Engineers often have access to raw observability data but lack clear, prioritized actions, while FinOps teams need insight into how resource adjustments impact spending.
App Insights translates this complex data into clear, structured recommendations that show:
With these insights, teams can eliminate inefficiencies, continuously refine workload configurations, and ensure Kubernetes remains cost-effective over time.
Rightsizing in Kubernetes isn’t just about cutting costs—it’s about making sure your workloads run efficiently, reliably, and without waste. If you’ve ever struggled with over-provisioned workloads draining your budget or under-provisioned ones slowing down applications, you know how tricky it can be to get resource allocations right. It’s not a one-time fix—it’s something you need to continuously refine as workloads change.
That’s why relying on manual adjustments and static thresholds won’t cut it. By bringing in observability, automation, and data-driven insights, you can shift from guesswork to informed, proactive rightsizing.
With Randoli App Insights, you don’t have to spend hours analyzing metrics—it continuously evaluates workload efficiency, helps you optimize requests, and ensures that infrastructure scales only when truly needed.
At the end of the day, rightsizing is about striking the right balance—giving your workloads exactly what they need, no more, no less. When done right, it’s not just about saving money—it’s about running a more resilient, scalable, and high-performing Kubernetes environment.
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