In the era of cloud-native infrastructure, the scheduler is king. However, the efficiency of that scheduler depends entirely on the accuracy of the data you feed it. For expert Platform Engineers and SREs, Kubernetes request right sizing is not merely a housekeeping task—it is a critical financial and operational lever. Over-provisioning leads to “slack” (billed but unused capacity), while under-provisioning invites CPU throttling and OOMKilled events.

This guide moves beyond the basics of resources.yaml. We will explore the mechanics of resource contention, the algorithmic approach Kubecost takes to optimization, and how to implement a data-driven right-sizing strategy that balances cost reduction with production stability.

The Technical Economics of Resource Allocation

To master Kubernetes request right sizing, one must first understand how the Kubernetes scheduler and the underlying Linux kernel interpret these values.

The Scheduler vs. The Kernel

Requests are primarily for the Kubernetes Scheduler. They ensure a node has enough allocatable capacity to host a Pod. Limits, conversely, are enforced by the Linux kernel via cgroups.

• CPU Requests: Determine the cpu.shares in cgroups. This is a relative weight, ensuring that under contention, the container gets its guaranteed slice of time.
• CPU Limits: Determine cpu.cfs_quota_us. Hard throttling occurs immediately if this quota is exceeded within a period (typically 100ms), regardless of node idleness.
• Memory Requests: Primarily used for scheduling.
• Memory Limits: Enforce the OOM Killer threshold.

Pro-Tip (Expert): Be cautious with CPU limits. While they prevent a runaway process from starving neighbors, they can introduce tail latency due to CFS throttling bugs or micro-bursts. Many high-performance shops (e.g., at the scale of Twitter or Zalando) choose to set CPU Requests but omit CPU Limits for Burstable workloads, relying on cpu.shares for fairness.

Why “Guesstimation” Fails at Scale

Manual right-sizing is impossible in dynamic environments. Developers often default to “safe” (bloated) numbers, or copy-paste manifests from StackOverflow. This results in the “Kubernetes Resource Gap”: the delta between Allocated resources (what you pay for) and Utilized resources (what you actually use).

Without tooling like Kubecost, you are likely relying on static Prometheus queries that look like this to find usage peaks:

max_over_time(container_memory_working_set_bytes{namespace="production"}[24h])

While useful, raw PromQL queries lack context regarding billing models, spot instance savings, and historical seasonality. This is where Kubernetes request right sizing via Kubecost becomes essential.

Implementing Kubecost for Granular Visibility

Kubecost models your cluster’s costs by correlating real-time resource usage with your cloud provider’s billing API (AWS Cost Explorer, GCP Billing, Azure Cost Management).

1. Installation & Prometheus Integration

For production clusters, installing via Helm is standard. Ensure you are scraping metrics at a resolution high enough to catch micro-bursts, but low enough to manage TSDB cardinality.

helm repo add kubecost https://kubecost.github.io/cost-analyzer/
helm upgrade --install kubecost kubecost/cost-analyzer \
    --namespace kubecost --create-namespace \
    --set kubecostToken="YOUR_TOKEN_HERE" \
    --set prometheus.server.persistentVolume.enabled=true \
    --set prometheus.server.retention=15d

2. The Right-Sizing Algorithm

Kubecost’s recommendation engine doesn’t just look at “now.” It analyzes a configurable window (e.g., 2 days, 7 days, 30 days) to recommend Kubernetes request right sizing targets.

The core logic typically follows a usage profile:

• Peak Aware: It identifies max(usage) over the window to prevent OOMs.
• Headroom Buffer: It adds a configurable overhead (e.g., 15-20%) to the recommendation to account for future growth or sudden spikes.

Executing the Optimization Loop

Once Kubecost is ingesting data, navigate to the Savings > Request Right Sizing dashboard. Here is the workflow for an SRE applying these changes.

Step 1: Filter by Namespace and Owner

Do not try to resize the entire cluster at once. Filter by namespace: backend or label: team=data-science.

Step 2: Analyze the “Efficiency” Score

Kubecost assigns an efficiency score based on the ratio of idle to used resources.

Target: A healthy range is typically 60-80% utilization. Approaching 100% is dangerous; staying below 30% is wasteful.

Step 3: Apply the Recommendation (GitOps)

As an expert, you should never manually patch a deployment via `kubectl edit`. Take the recommended YAML values from Kubecost and update your Helm Charts or Kustomize bases.

# Before Optimization
resources:
  requests:
    memory: "4Gi" # 90% idle based on Kubecost data
    cpu: "2000m"

# After Optimization (Kubecost Recommendation)
resources:
  requests:
    memory: "600Mi" # calculated max usage + 20% buffer
    cpu: "350m"

Advanced Strategy: Automating with VPA

Static right-sizing has a shelf life. As traffic patterns change, your static values become obsolete. The ultimate maturity level in Kubernetes request right sizing is coupling Kubecost’s insights with the Vertical Pod Autoscaler (VPA).

Kubecost can integrate with VPA to automatically apply recommendations. However, in production, “Auto” mode is risky because it restarts Pods to change resource specifications.

Warning: For critical stateful workloads (like Databases or Kafka), use VPA in Off or Initial mode. This allows VPA to calculate the recommendation object, which you can then monitor via metrics or export to your GitOps repo, without forcing restarts.

VPA Configuration for Recommendations Only

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: backend-service-vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: backend-service
  updatePolicy:
    updateMode: "Off" # Kubecost reads the recommendation; VPA does not restart pods.

Frequently Asked Questions (FAQ)

1. How does right-sizing affect Quality of Service (QoS) classes?

Right-sizing directly dictates QoS.

Guaranteed: Requests == Limits. Safest, but most expensive.

Burstable: Requests < Limits. Ideal for most HTTP web services.

BestEffort: No requests/limits. High risk of eviction.

When you lower requests to save money, ensure you don’t accidentally drop a critical service from Guaranteed to Burstable if strict isolation is required.

2. Can I use Kubecost to resize specific sidecars (like Istio/Envoy)?

Yes. Sidecars often suffer from massive over-provisioning because they are injected with generic defaults. Kubecost breaks down usage by container, allowing you to tune the istio-proxy container independently of the main application container.

3. What if my workload has very “spiky” traffic?

Standard averaging algorithms fail with spiky workloads. In Kubecost, adjust the profiling window to a shorter duration (e.g., 2 days) to capture recent spikes, or ensure your “Target Utilization” threshold is set lower (e.g., 50% instead of 80%) to leave a larger safety buffer for bursts.

Conclusion

Kubernetes request right sizing is not a one-time project; it is a continuous loop of observability and adjustment. By leveraging Kubecost, you move from intuition-based guessing to data-driven precision.

The goal is not just to lower the cloud bill. The goal is to maximize the utility of every CPU cycle you pay for while guaranteeing the stability your users expect. Start by identifying your top 10 most wasteful deployments, apply the “Requests + Buffer” logic, and integrate these checks into your CI/CD pipelines to prevent resource drift before it hits production. Thank you for reading the DevopsRoles page!