Application rightsizing is the process of aligning compute resources with actual workload demand. The goal is simple: deliver consistent performance without wasting money on unused capacity.

The problem is that many tools and vendors only tackle a single piece of this puzzle, such as Horizontal Pod Autoscaler (HPA) tuning. In practice, that only solves a small fraction of the challenge.

As DevOps professionals, we know the stakes. Inefficient rightsizing leads to unnecessary cloud spend, unpredictable latency, and brittle systems that collapse under real-world load. Getting this right requires a more holistic approach.

In this guide, we’ll break down the three pillars of rightsizing, expose common hidden bottlenecks, and walk through a practical step-by-step methodology you can apply in your own environments.

The three pillars of application rightsizing

To fully rightsize an application, you must address three key dimensions: min replicas, scaling metrics, and pod resources. These form the foundation for stability and efficiency.

Pillar 1: MinReplica

What it is: The minimum number of pods that Kubernetes should keep running for a given deployment.
Why it matters: Without a baseline, applications may cold start during sudden traffic spikes. This hurts latency and user experience.

Best practice:


  apiVersion: autoscaling/v2

kind: HorizontalPodAutoscaler

spec:

  minReplicas: 3

Start with setting it to 2 or 3, to be resilient to problems in an AZ.
If the resource usage metrics indicate that below a certain point the scaling of demand is too quick for autoscaling to catch up, and unavailability is unacceptable, then set minReplica to be above that point.

Pillar 2: Scaling Metrics

What it is: The criteria HPA uses to scale pods up or down. CPU utilization is the default, but real-world workloads often need more.

Examples:

  • targetCPUUtilizationPercentage: 75 (basic CPU scaling)
  • Latency-based scaling using Prometheus metrics.
  • QPS (queries per second) from service-level telemetry.

Many tools often stop here, but CPU alone doesn’t reflect user experience. Adding latency or QPS ensures scaling matches actual demand.

Pillar 3: Pod Resources (Requests and Limits)

What it is: Pod-level definitions of guaranteed minimum resources (requests) and maximum allowed usage (limits).

Example configuration:


  resources:

  requests:

    cpu: "500m"

    memory: "512Mi"

  limits:

    cpu: "1"

    memory: "1Gi"

Key practices:

  • Set requests based on observed steady-state usage.
  • Apply limits cautiously to prevent throttling.
  • Remember: Kubernetes schedules based on requests, not limits. Incorrect requests can lead to inefficient bin-packing and wasted nodes.
  • Be aware that some applications cannot use all the resources you assign. For example, Python apps are limited by the GIL to a single CPU, and code with heavy lock contention may not benefit from more parallelism. In these cases, setting high requests is just wasted cost.

With these three pillars in place, you have the building blocks for scaling. But CPU and memory are not always the real constraints. That brings us to the hidden challenges.

Hidden Bottlenecks That Break Scaling

If rightsizing only considered CPU and memory, life would be easy. In reality, bottlenecks often emerge in other layers. Here are some of the most common:

  • Disk I/O: Slow storage throttles throughput even when CPU is idle.
  • Network bandwidth or latency: Pods may scale, but if traffic saturates network interfaces or egress costs spike, performance still suffers.
  • Service dependencies: Scaling one service doesn’t help if a downstream service it depends on can only handle a fixed number of requests per second.
  • Programming language or runtime limits: Some runtimes (such as Python with the GIL) cannot efficiently use more than one CPU, no matter how many you allocate.
  • Code-level constraints: Heavy lock contention or sequential algorithms limit parallelism, so additional resources provide little or no gain.

This is why requests and limits must be set with a clear understanding of what the application can actually use. The next step is learning how to systematically uncover where the true bottlenecks are.

Step-by-Step How-To for Practical Rightsizing

Rightsizing is not a one-size-fits-all recipe. The first step is figuring out whether CPU or memory is truly your bottleneck. If it is, continue with the standard optimization flow. If not, address the real constraint directly.

Step 1: Identify the Bottleneck

Prerequisites:

  • Access to monitoring tools like Prometheus, Datadog, or CloudWatch.
  • Load testing capability (e.g., Locust, k6, JMeter).

Action: Run load tests while watching CPU, memory, disk, and network usage.

  • If CPU or memory saturates consistently before throughput plateaus, you’re bottlenecked there.
  • If not, note the first resource or dependency to max out.

Checkpoint: By the end of this step, you should know whether your problem is CPU/memory or something else.

Step 2A: If CPU/Memory Are the Bottleneck

  1. Set Minimum Replicas
     Ensure a safe baseline using minReplicas.
    Verify that requests per pod at baseline traffic never exceed 70–80% of CPU utilization.

Configure Scaling Metrics
 Begin with CPU/memory thresholds. Add latency or QPS once stable.


  metrics:

- type: Resource

  resource:

    name: cpu

    target:

      type: Utilization

      averageUtilization: 75
  1. Tune Pod Requests and Limits
    Use real metrics, not guesses.

    Example: If the pod consistently uses 300m CPU, set requests at 400m for buffer.

Common pitfall: Setting limits too close to requests can cause throttling spikes.

Step 2B: If CPU/Memory Are Not the Bottleneck

  1. Investigate the True Constraint
     Examples:
    • Database queries running slower than expected.
    • Disk throughput at 95% during load tests.
    • Network egress hitting bandwidth caps.
  2. Fix Where Possible
    • Optimize database queries or add indexes.
    • Move to faster storage classes.
    • Fix the services that are the bottleneck in the call chain.
  3. Reduce CPU/Memory Allocations
     If fixes are impossible in the short term, scale down CPU/memory to the maximum your application can realistically use. This avoids burning budget on resources that won’t help.

Checkpoint: After this step, your pods should be tuned to match actual useful capacity, not theoretical maximums.

Step 3: Continuous Validation

Rightsizing is not a one-time exercise.

Action:

  • Schedule load testing quarterly or after major releases.
  • Monitor saturation points regularly.
  • Document the reasons for setting your parameters the way you chose, so that the issues will be learned and not repeated in the future.

Pitfall: Assuming today’s bottlenecks are permanent. Application code and traffic patterns evolve.

Next: let’s see why stopping at HPA tuning is not enough.

Why Full Rightsizing Beats Narrow Approaches

Many solutions on the market focus exclusively on CPU/memory HPA tuning. That’s a tiny slice of the problem.

True rightsizing:

  • Defines safe replica baselines (minReplica).
  • Uses scaling policies that reflect business outcomes, not just CPU load.
  • Tunes pod resources to match steady-state usage.
  • Accounts for hidden bottlenecks like storage, networks, and dependencies.

Tools that only automate CPU thresholds leave organizations exposed. Their customers still suffer from disk saturation, database slowdowns, and runaway costs. A holistic approach is the only way to build resilient, efficient systems.

Moving Beyond Partial Solutions

Rightsizing is about aligning infrastructure with reality. Doing it well reduces waste, prevents outages, and creates predictable performance.

We covered the three pillars, exposed hidden bottlenecks, and walked through a practical methodology for tackling both CPU-based and non-CPU bottlenecks.

Modern workloads demand more than narrow CPU-based HPA automation. By adopting full-spectrum rightsizing practices, you’ll protect your applications from fragility and your business from unnecessary cloud bills.

Next step: Run a controlled load test against one of your critical services today. Identify whether CPU/memory are the bottleneck, and apply the branching methodology outlined above. That single exercise will put you ahead of teams relying on partial solutions.