Choosing an Autoscaling Strategy#

Kubernetes autoscaling operates at two distinct layers: pod-level scaling changes how many pods run or how large they are, while node-level scaling changes how many nodes exist in the cluster to host those pods. Getting the right combination of tools at each layer is the key to a system that responds to demand without wasting resources.

The Two Scaling Layers#

Understanding which layer a tool operates on prevents the most common misconfiguration – expecting pod-level scaling to solve node-level capacity problems, or vice versa.

Choosing Kubernetes Workload Types#

Kubernetes provides several workload controllers, each designed for a specific class of application behavior. Choosing the wrong one leads to data loss, unnecessary complexity, or workloads that fight the platform instead of leveraging it. This guide walks through the decision criteria and tradeoffs for each type.

The Workload Types at a Glance#

Decision Criteria#

The choice comes down to four questions:

Cluster Autoscaling#

Kubernetes autoscaling operates at two levels: pod-level (HPA adds or removes pod replicas) and node-level (Cluster Autoscaler adds or removes nodes). Getting them to work together requires understanding how each makes decisions.

Horizontal Pod Autoscaler (HPA)#

HPA adjusts the replica count of a Deployment, StatefulSet, or ReplicaSet based on observed metrics. The metrics-server must be running in your cluster for CPU and memory metrics.

Basic HPA on CPU#

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

This scales my-app between 2 and 10 replicas, targeting 70% average CPU utilization across all pods. The HPA checks metrics every 15 seconds (default) and computes the desired replica count as:

Kubernetes Cost Audit and Reduction#

Kubernetes clusters accumulate cost waste silently. Resource requests padded “just in case” during initial deployment never get revisited. Load balancers created for debugging stay running. PVCs from deleted applications persist. Over six months, a cluster originally running at $5,000/month can drift to $12,000 with no corresponding increase in actual workload.

This operational plan works through cost reduction systematically, starting with visibility (you cannot cut what you cannot see), moving through quick wins, then tackling the larger structural optimizations that require data collection and careful rollout.

Kubernetes FinOps: Decision Framework for Cost Optimization#

FinOps in Kubernetes is the practice of bringing financial accountability to infrastructure spending. The challenge is not a lack of cost-saving techniques – it is knowing which ones to apply first, which combinations work together, and which ones introduce risk that outweighs the savings. This article provides a structured decision framework for selecting and prioritizing Kubernetes cost optimization strategies.

The Five Optimization Levers#

Every Kubernetes cost optimization effort works across five levers. Each has a different risk profile, implementation effort, and savings ceiling.

Resource Requests and Limits#

Requests and limits control how Kubernetes schedules pods and enforces resource usage. Getting them wrong leads to pods that get evicted, throttled to a crawl, or that starve other workloads on the same node.

Requests vs Limits#

Requests are what the scheduler uses for placement. When you request 500m CPU and 256Mi memory, Kubernetes finds a node with that much allocatable capacity. The request is a guarantee – the kubelet reserves those resources for your container.

Scenario: Preparing for and Handling a Traffic Spike#

You are helping when someone says: “we have a big launch next week,” “Black Friday is coming,” or “traffic is suddenly 3x normal and climbing.” These are two distinct problems – proactive preparation for a known event and reactive response to an unexpected surge – but they share the same infrastructure mechanics.

The key principle: Kubernetes autoscaling has latency. HPA takes 15-30 seconds to detect increased load and scale pods. Cluster Autoscaler takes 3-7 minutes to provision new nodes. If your traffic spike is faster than your scaling speed, users hit errors during the gap. Proactive preparation eliminates this gap. Reactive response minimizes it.

Choosing Kubernetes Storage#

Storage decisions in Kubernetes are harder to change than almost any other architectural choice. Migrating data between storage backends in production involves downtime, risk, and careful planning. Understand the tradeoffs before provisioning your first PersistentVolumeClaim.

The decision comes down to five criteria: performance (IOPS and latency), durability (can you survive node failure), portability (can you move the workload), cost, and access mode (single pod or shared).

Storage Categories#

Block Storage (ReadWriteOnce)#

Block storage provides a raw disk attached to a single node. Only one pod on that node can mount it at a time (ReadWriteOnce). This is the most common storage type for databases, caches, and any workload that needs fast, consistent disk I/O.

Kubernetes Cost Optimization#

Most Kubernetes clusters run at 15-30% actual CPU utilization but are billed for the full provisioned capacity. The gap between what you reserve and what you use is pure waste. This article covers the practical workflow for finding and eliminating that waste.

The Cost Problem: Requests vs Actual Usage#

Kubernetes resource requests are the foundation of cost. When a pod requests 4 CPUs, the scheduler reserves 4 CPUs on a node regardless of whether the pod ever uses more than 0.1 CPU. The node is sized (and billed) based on what is reserved, not what is consumed.

Kubernetes Resource Management Deep Dive#

Resource management in Kubernetes is the mechanism that decides which pods get scheduled, which pods get killed when the node runs low, and how much CPU and memory each container is actually allowed to use. The surface-level concept of requests and limits is straightforward. The underlying mechanics – QoS classification, CFS CPU quotas, kernel OOM scoring, kubelet eviction thresholds – are where misconfigurations cause production outages.