DEV Community: CloudPilot AI

Kubernetes Capacity Planning Kubernetes Capacity Planning Playbook: How to Balance Performance, Stability, and Cost

CloudPilot AI — Fri, 17 Oct 2025 07:43:23 +0000

If you’ve ever opened your cloud bill and wondered why your Kubernetes cluster costs keep climbing despite "auto-scaling", you’re not alone. Many teams face the same problem: over-provisioned clusters that waste resources or under-provisioned clusters that cause latency, pod evictions, or service degradation.

Kubernetes was built to orchestrate containers efficiently, but it doesn’t automatically ensure your workloads are right-sized. Without structured capacity planning, organizations either overspend for peace of mind or risk performance issues to save money. Striking the right balance between cost and reliability is where Kubernetes capacity planning comes in.

What Is Kubernetes Capacity Planning?

Kubernetes capacity planning is the discipline of understanding, forecasting, and optimizing how your cluster consumes infrastructure resources such as CPU, memory, storage, and network bandwidth. It ensures that your workloads always have enough resources to run reliably while minimizing waste and controlling cloud costs.

At its core, capacity planning bridges two competing goals: performance and efficiency. On the one hand, you need to ensure there are enough resources available to handle peak workloads without failures or latency.

On the other, over-allocating resources can result in idle capacity and unnecessary cloud spend. The goal is to find the “sweet spot” where your Kubernetes environment runs smoothly, scales predictably, and remains financially sustainable.

A typical capacity planning process in Kubernetes involves three layers of consideration:

1. Workload-Level Planning

Every application running in a Kubernetes cluster requests a certain amount of CPU and memory. These requests and limits influence how the Kubernetes scheduler places pods across nodes. If requests are too high, the scheduler may leave nodes underutilized. If they’re too low, workloads risk contention and instability.

Effective capacity planning starts by analyzing workload characteristics, such as CPU spikes, memory consumption trends, and traffic variability, to define accurate requests and limits. This ensures pods receive the resources they need without starving others or wasting compute.

2. Cluster-Level Planning

Once workloads are right-sized, attention shifts to the cluster’s node composition. You must decide how many nodes are needed, what instance types to use, and how to distribute them across availability zones. Cluster-level planning also involves determining whether to use on-demand, reserved, or spot instances, balancing cost with resilience.

For example, steady workloads might run on reserved instances for predictable cost, while fault-tolerant batch jobs can leverage cheaper spot capacity.

3. Strategic Forecasting and Scalability Planning

Beyond day-to-day resource allocation, capacity planning also looks ahead. As traffic grows, new services launch, or regions expand, teams must predict future demand. Forecasting involves analyzing historical usage patterns and growth rates to project when additional capacity will be needed.

This prevents last-minute scaling issues, such as running out of schedulable nodes during peak events, and allows teams to plan budgets and scaling policies proactively.

Capacity planning in Kubernetes is both a technical and strategic process. It requires collaboration between engineering and finance teams, blending performance data with business insights.

Technically, it leverages monitoring tools, autoscalers, and cloud analytics to quantify usage patterns. Strategically, it guides long-term infrastructure investment and helps organizations adopt modern pricing models, such as spot or savings plans, without compromising reliability.

Why Capacity Planning Matters

1. Cost Optimization

Most Kubernetes environments operate at less than 50% average resource utilization. This means you could be paying twice as much for infrastructure as you actually need. Proper capacity planning identifies inefficiencies, enabling teams to safely reduce over-provisioning and control costs.

2. Reliable Performance

Right-sized clusters prevent resource contention and ensure that critical workloads always have the compute and memory they need. This translates to consistent performance, fewer OOM errors, and reduced service disruptions.

3. Predictable Scalability

By forecasting future resource needs, teams can scale smoothly as application demand grows. Capacity planning removes guesswork from cluster expansion and helps avoid emergency node provisioning during peak hours.

4. Business Continuity

A well-planned cluster prevents outages caused by capacity shortages. It supports high availability strategies, ensuring that even during spikes or failures, user-facing services continue running seamlessly.

How Capacity Planning Works

Kubernetes capacity planning combines data analysis, forecasting, and automation. It starts by measuring how your workloads consume resources and ends with decisions about how your cluster should scale and what instance types it should use.

1. Collect Usage Data

Begin by gathering real usage data from your monitoring tools such as Prometheus, CloudWatch, or Datadog. Focus on CPU and memory requests, actual utilization, and the frequency of pod rescheduling or throttling. This establishes a baseline for current performance and efficiency.

2. Analyze Workload Behavior

Different workloads have different demand patterns. Some are steady and predictable, while others spike based on traffic or job schedules. By classifying workloads according to these patterns, you can design scaling strategies that meet each workload’s needs without wasting resources.

3. Model Future Growth

Forecasting helps you anticipate when demand will exceed current capacity. By analyzing historical metrics and business growth projections, teams can plan node expansions or instance upgrades ahead of time rather than reacting to incidents.

4. Implement Scaling Policies

Once demand patterns are clear, you can apply scaling tools such as the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), or Karpenter to dynamically adjust capacity. These policies ensure that clusters expand during traffic peaks and shrink when workloads are idle.

5. Refine Continuously

Capacity planning is never finished. Continuous monitoring and adjustment are essential, as workloads evolve and usage patterns shift over time.

Capacity Planning Playbook

Phase 1: Establish Visibility

1. Enable Resource Metrics

Install and configure:
- metrics-server
- Prometheus and Grafana
Ensure the following metrics are available:
- Pod CPU and memory usage (container_cpu_usage_seconds_total, container_memory_working_set_bytes)
- Node utilization
- Pending pods count
- Throttling and OOMKill events

2. Collect Baseline Data

Run for at least 7–14 days to capture weekday and weekend patterns.
Export data as:
kubectl top pods --all-namespaces > resource-usage.txt, kubectl top nodes > node-usage.txt

3. Visualize Utilization

Create Grafana dashboards showing:
- Cluster CPU/memory usage vs. capacity
- Requests vs. actual usage
- Node utilization heatmaps
- Namespace-level resource consumption

Key Metrics to Track

Metric	Ideal Range	Why It Matters
CPU utilization	60–80%	Below this → waste; above → risk of throttling
Memory utilization	60–75%	Memory spikes cause OOM errors
Pending pods	0–2% of total	Indicates scheduling or quota issues
Cost per namespace	Decreasing trend	Tracks efficiency over time

Phase 2: Analyze and Identify Inefficiencies

1. Compare Requested vs. Actual Usage

kubectl get pods -A -o=custom-columns=NAME:.metadata.name,REQ_CPU:.spec.containers[*].resources.requests.cpu,REQ_MEM:.spec.containers[*].resources.requests.memory

Cross-check against Prometheus usage data.

2. Detect Over-Provisioned Pods

If actual usage < 50% of requested CPU/memory → candidate for rightsizing.

3. Detect Under-Provisioned Pods

If actual usage > 90% of requested → risk of throttling or OOMKill.

4. Use Automated Tools

Goldilocks: recommends requests/limits based on historical metrics.
CloudPilot AI Workload Autoscaler: continuously adjusts resource requests based on real-time utilization and trends.

Phase 3: Optimize Resource Requests and Limits

1. Set New Requests/Limits

Start with the 80th percentile of observed usage as request value.
Only set limits if necessary (e.g., memory-heavy or bursty workloads).

2. Gradually Apply Changes

Update one namespace or deployment group at a time.
Use a rolling deployment to minimize disruption:

  kubectl rollout restart deployment <name>

3. Monitor After Changes

Watch Grafana dashboards for:
- New OOMKills or throttling
- Utilization improvements
- Scheduling delays

💡 Tip:
Avoid making requests = limits. Allow some burst capacity to improve bin packing and scheduling efficiency.

Phase 4: Plan Node and Cluster Capacity

1. Determine Baseline Node Count

Calculate average node utilization.
Use formula:

  Required Nodes = (Total Pod CPU Requests / Node CPU Capacity) × Safety Buffer

Example: 500 vCPU requested / 32 vCPU per node × 1.2 buffer = ~19 nodes.

2. Right-Size Node Types

Compare actual workload profiles:

Workload Type	Recommended Node Type
Compute-heavy	c6i / c7g
Memory-heavy	r6i / r7g
Bursty / batch	spot instances
ML / GPU jobs	g5 / a10g

3. Use Karpenter or Cluster Autoscaler

Configure Karpenter to dynamically launch optimized nodes:

requirements:
  - key: "node.kubernetes.io/instance-type"
    operator: In
    values: ["m6i.large", "m6i.xlarge"]
limits:
  resources:
    cpu: 1000

Set different node pools for on-demand and spot capacity.

4. Add Safety Buffers

Reserve at least 15–25% extra capacity for critical workloads or sudden spikes.

Phase 5: Forecast and Budget

1. Analyze Historical Growth

Use Prometheus or cloud cost tools to chart 3–6 month growth trends.
Track CPU hours, memory GB hours, and node count over time.

2. Estimate Future Demand

Apply trend-based forecasting:

Future Capacity = Current Usage × (1 + Growth Rate) × Safety Margin

Example: 400 cores × (1 + 0.25) × 1.2 = 600 cores.

3. Simulate Scenarios

“What if traffic doubles?”
“What if we migrate 30% of jobs to spot?”
Adjust budgets and scaling strategies accordingly.

Phase 6: Continuous Review and Automation

1. Monthly Review

Compare forecasted vs. actual usage.
Identify new over-provisioned namespaces.
Review cost by workload or environment.

2. Quarterly Optimization

Update node instance types for new pricing options.
Review reserved instance and savings plan utilization.

3. Automate Scaling

Integrate with:
- Horizontal Pod Autoscaler (for application-level scaling)
- Vertical Pod Autoscaler (for automatic right-sizing)
- Karpenter (for predictive node provisioning)

4. Alerting

Configure alerts for:
- 90% node CPU/memory
- High pod pending rates
- Excessive cost anomalies

Kubernetes Capacity Planning Checklist

[ ] Metrics collection is complete and accurate
[ ] Resource requests match observed 80th percentile usage
[ ] Growth forecast reviewed and budget approved
[ ] Autoscaling policies tuned and tested
[ ] Alerting for capacity saturation in place
[ ] Regular review cadence established

How CloudPilot AI Helps with Capacity Planning

Manual capacity planning in Kubernetes is complex and time-consuming. Resource patterns change by the hour, workloads evolve, and spot prices fluctuate constantly. CloudPilot AI eliminates guesswork by introducing autonomous optimization at both the workload and node levels.

Here’s how CloudPilot AI transforms capacity planning into a continuous, intelligent process:

Workload-Level Optimization: Automatically right-sizes workloads based on real-time CPU and memory usage, preventing over-allocation and improving cluster density.
Node-Level Optimization: Dynamically selects the best instance types (including spot, on-demand) using price, performance, and availability data.
Intelligent Scheduling: Ensures workloads are placed efficiently across nodes for maximum utilization and stability.
Autonomous Scaling: Integrates seamlessly with Karpenter and autoscaling tools to maintain optimal capacity while reducing costs by up to 80%.

With CloudPilot AI, capacity planning becomes proactive and automated. Instead of reacting to resource issues, your clusters stay optimized — continuously, intelligently, and cost-effectively.

Right-Sizing Kubernetes Requests and Limits: How to Avoid OOMKills and Waste

CloudPilot AI — Sat, 11 Oct 2025 01:33:25 +0000

Introduction: The Hidden Cost of Wrong Requests & Limits

Picture this: Your team just launched a major promotion campaign. Traffic surges exactly as marketing hoped but minutes later, your flagship service crashes.

Pods are in a CrashLoopBackOff state, restarts are piling up, and engineers are scrambling. The culprit? A single container hits its memory limit, triggering an OOMKill.

This isn't an uncommon story. Every Kubernetes engineer knows resource configuration matters, but few realize just how impossible it is to get right manually.

Overprovision, and you're burning money. Underprovision, and you risk outages. The stakes are high, yet the tooling and processes most teams rely on make it nearly impossible to hit the sweet spot.

What Are Requests and Limits?

Kubernetes schedules workloads based on two critical values you define in Pod specs:

resources:
  requests:
    cpu: "500m"
    memory: "256Mi"
  limits:
    cpu: "1"
    memory: "512Mi"

Request: The guaranteed amount of CPU or memory for a container. The scheduler uses these numbers to decide where to place the Pod.
Limit: The hard cap on what the container can consume at runtime. Exceeding a memory limit triggers an OOMKill; exceeding a CPU limit results in throttling.

Key behavior difference:

Requests affect scheduling.
Limits affect runtime enforcement.

When requests are too high, nodes look "full", leading to poor bin-packing efficiency and unnecessary node scaling. When limits are too low, workloads crash.

The Common Pitfalls in Resource Configuration

Even experienced teams often fall into these traps:

1. Guesswork

Developers set arbitrary numbers, or worse, leave defaults in place. These numbers stick around for months, silently driving waste or risk.

2. Equal Request and Limit

Setting request == limit seems safe but leaves no burst capacity. Memory spikes instantly result in OOMKills.

3. No Limits

Containers without limits can consume unlimited memory, turning one bad deployment into a node-wide outage—a noisy neighbor problem.

4. Overly Conservative Estimates

SREs, burned by outages, often over-allocate. A service needing 300Mi may get a 1Gi request, bloating costs by 3x.

5. Static Configs in Dynamic Environments

Resource profiles change with every release. Static settings quickly become outdated.

Why Manual Right-Sizing Fails

On paper, right-sizing sounds easy:

"Just gather metrics, analyze them, and adjust numbers."

But anyone running Kubernetes at scale knows this is a fantasy. Let's break down why.

Metrics Are Misleading

Metrics dashboards often show averages or 95th percentile values:

kubectl top pod

or via Prometheus queries like:

quantile_over_time(0.95, sum by(pod)(container_memory_usage_bytes)[5m])

But:

Short-lived memory spikes often don't appear in sampled data.
The spike you miss is the one that triggers OOMKill.
To avoid this, teams over-allocate “just in case,” inflating costs.

Workloads Don't Stay Still

Modern microservices are dynamic by design:

Traffic fluctuates daily, weekly, seasonally.
Feature releases change memory profiles overnight.
Yesterday's "perfect" numbers are tomorrow's liability.

Too Many Services to Tune

In a cluster with 100+ services, even spending 30 minutes per service means days of tuning work. Repeat that every sprint, and your SRE team is just firefighting.

Dashboards Don't Tell You What to Do

Grafana or Datadog dashboards look impressive but don't answer the core question:

“What should I set my requests and limits to?”

Most engineers guess, run a deploy, and hope for the best.

VPA Isn't a Silver Bullet

The Vertical Pod Autoscaler (VPA) was designed to solve this, but:

It restarts Pods to apply new values, unacceptable for many production systems.
Its recommendations lag behind real-world traffic changes.
Bursty or unpredictable workloads often get inaccurate values.

Bottom line: Manual right-sizing is like playing darts blindfolded—you might hit the target occasionally, but you’ll waste enormous time and money doing it.

Where to Go From Here

If this resonates, you're not alone. Industry data shows Kubernetes clusters often use only 10–25% of CPU and 18–35% of memory.

Manual right-sizing is unsustainable at scale. The future lies in continuous, automated resource optimization , tools like VPA paved the way, but we now need solutions that:

Continuously adapt to changing workloads.
Eliminate Pod restarts when applying changes.
Optimize for both cost and reliability.

💡 Exciting news: This month, we're releasing an intelligent Workload Autoscaler that automatically right-sizes your Pods without restarts, helping your cluster run efficiently and reliably.

We've already opened an early access beta, and if you'd like to try it, feel free to contact us— your SRE team will thank YOU!

K8s VPA: Limitations, Best Practices, and the Future of Pod Rightsizing

CloudPilot AI — Fri, 26 Sep 2025 02:25:16 +0000

As Kubernetes adoption continues to grow across industries and regions, optimizing workloads for cost efficiency and reliability has become a universal challenge. Over-provisioning pods wastes cloud budgets, while under-provisioning risks outages and poor customer experience.

The Vertical Pod Autoscaler (VPA) was designed to simplify this process by automatically adjusting pod CPU and memory settings. While helpful, VPA has clear trade-offs—especially for teams running multi-region clusters, multi-cloud workloads, or latency-sensitive applications.

In this article, we’ll explore how VPA works, its most significant limitations, and best practices for scaling Kubernetes workloads effectively while looking ahead at the next evolution of pod optimization.

What is Kubernetes VPA ?

The VPA is a Kubernetes component that analyzes pod resource usage and adjusts CPU and memory requests to match workload needs.

Unlike the Horizontal Pod Autoscaler (HPA), which adds or removes pod replicas to handle scaling, VPA focuses on optimizing the resource allocation of individual pods.

VPA is often used for:

Backend services with stable workloads
Applications with fluctuating CPU or memory needs
Environments where resource planning is complex or manual tuning is error-prone

For teams operating across regions or clouds, VPA offers baseline resource management automation. However, it has major limitations that can create operational friction at scale.

Key Limitations of VPA

1. Pod Restarts Cause Disruption

VPA adjusts CPU and memory requests and limits for pods by restarting them, which can cause disruptions, especially for critical or stateful applications, because pods must be evicted and recreated to apply changes.

2. Conflicts with HPA

When both HPA and VPA scale on the same metrics (CPU or memory), they can interfere with each other and even cause over-scaling.

3. Limited Scope of Metrics

VPA focuses only on CPU and memory, ignoring network, I/O, and other critical signals that matter for performance.

4. Short Historical Window

It typically analyzes only a few hours to eight days of data, making it blind to seasonal trends or longer-term workload patterns.

5. No Awareness of Cluster Architecture

VPA may recommend values exceeding node capacities, leaving pods stuck in a Pending state.

6. Poor StatefulSet Support

Stateful workloads require careful orchestration, which VPA’s restart model doesn’t handle gracefully.

7. Not Suitable for Real-Time Scaling

Since every change requires a restart, VPA reacts slowly to sudden traffic spikes.

8. Complexity and Tuning Overhead

Configuring VPA for production environments requires deep Kubernetes expertise, testing, and ongoing monitoring.

VPA’s challenges aren’t just theoretical but they represent real engineering trade-offs. Pod restarts can lead to customer-facing downtime, missed SLAs, and engineering frustration. The lack of awareness of historical patterns or node topology can lead to inefficiency and wasted resources.

In a world where Kubernetes clusters power critical workloads, these inefficiencies add up—both in cloud costs and operational complexity.

Best Practices for Running VPA Effectively

Run VPA in Recommend Mode

Let VPA provide recommendations instead of automatically applying changes. Combine it with HPA for scaling replicas, avoiding metric conflicts.

Separate Metrics Between VPA and HPA

Use VPA to tune CPU/memory requests, while HPA scales pods based on traffic or custom business metrics.

Use with Care for Critical or Stateful Workloads

Plan maintenance windows and design disruption budgets to minimize impact.

Set Reasonable Initial Requests and Monitor Closely

Provide sensible defaults and track VPA performance with Prometheus and Grafana.

Protect Service Availability with Pod Disruption Budgets

Prevent cascading restarts that could take down services.

Thorough Test Before Production Rollouts

Validate scaling thresholds and restart policies in staging environments first.

Implement Namespace-Level Resource Policies

Use LimitRanges and ResourceQuotas to cap excessive VPA recommendations.

The Future of Pod Rightsizing

Kubernetes VPA was an important milestone in automated resource tuning, but it’s no longer enough for today’s fast-moving, large-scale environments. The next generation of pod optimization should:

Deliver real-time, zero-disruption adjustments without requiring pod restarts
Use long-term data and predictive analytics to anticipate demand patterns
Enable policy-driven, environment-aware scaling that aligns with business goals
Simplify configuration for developers and platform engineers

VPA remains a valuable tool, but it’s far from a complete solution. By understanding its limitations and applying best practices, teams can unlock better efficiency and stability. With smarter, AI-driven solutions emerging, hassle-free, intelligent pod rightsizing is closer than ever.

We’re actively building a next-generation solution to make Kubernetes resource optimization smarter, more reliable, and more cost-efficient. Stay tuned and more details are coming soon!

Join our Slack community or Discord for early access updates and insights.

From Theory to Practice: A Complete Guide to Kubernetes In-Place Pod Resizing

CloudPilot AI — Wed, 10 Sep 2025 03:04:56 +0000

Kubernetes 1.27 brought about In-Place Pod Resizing (also known as In-Place Pod Vertical Scaling). But what exactly is it? And what does it mean for you?

In-Place Pod Resizing, introduced as an alpha feature in Kubernetes v1.27, allows you to dynamically adjust the CPU and memory resources of running containers without the traditional requirement of restarting the entire Pod.

While this feature has been available since v1.27, it remained behind a feature gate, meaning it was disabled by default and required manual activation. Feature gates in Kubernetes serve as toggles for experimental or development functionality, enabling cluster administrators to opt into new capabilities while they're still being refined and tested.

At the time of writing, In-Place Pod Resizing has graduated to beta status in Kubernetes v1.33 and will be enabled by default. This progression from alpha to beta signals that the feature has matured considerably and thoroughly, and the API has stabilized enough for broader adoption.

In this article, we'll dive deep into how In-Place Pod Resizing works, walk through a hands-on demo so you can experience Kubernetes' shiniest new feature firsthand, and explore the practical implications for your workloads and infrastructure costs.

A brief history of Kubernetes scaling methods

Before looking ahead, it is worth looking at how workload scaling has traditionally operated on Kubernetes. In the early days, resource allocation was largely a manual affair; you defined your resource requests and limits at deployment time, and those values remained fixed throughout the Pod's lifecycle.

If you needed more resources, you’d update your deployment configuration and wait for Kubernetes to terminate the old Pods and create new ones with the updated resource specifications.

This approach worked well enough for simple, stateless applications, but as Kubernetes adoption grew, so did the complexity of workloads running on it. The need for more dynamic resource management became apparent. This led to the introduction of the Horizontal Pod Autoscaler (HPA) in November 2015 with Kubernetes 1.1. The HPA was designed to help users scale out their workloads more dynamically based on CPU and memory usage.

Fast forward to Kubernetes 1.8, and the Vertical Pod Autoscaler (VPA) was introduced as a way to dynamically resize the CPU and memory allocated to existing pods. While HPA scaled horizontally by adding more instances, VPA scaled vertically by adjusting the resource allocation of individual Pods.

While all this was happening, a joint effort between Microsoft and Red Hat in 2019 led to the creation of Kubernetes Event-driven Autoscaling, or KEDA for short.

Initially geared toward better supporting Azure functions on OpenShift, KEDA's open-source nature meant the community quickly expanded its use case far beyond its original scope.

KEDA enables scaling based on external metrics and events, bridging the gap between traditional resource-based scaling and the complex, event-driven nature of modern applications.

So, if all these scaling methods exist, HPA for horizontal scaling, VPA for vertical scaling, and KEDA for event-driven scaling. Why, then, does In-Place Pod Resizing exist? What problem does it solve that the existing ecosystem doesn't already address?

What is In-Place Pod Resizing?

Simply put, In-Place Pod Vertical Scaling allows you to modify the CPU and memory resources of running containers without restarting the Pod. While this might sound like a minor improvement, it addresses a fundamental limitation that has plagued Kubernetes resource management since its inception.

Traditional vertical scaling in Kubernetes requires what you could call a "rip and replace" approach. When you need to adjust a Pod's resources, whether through manual updates or through one of the Pod autoscalers, Kubernetes would terminate the existing Pod and create a new one with the updated resource specifications. This process, while functional, introduced several disruptive side effects that could be problematic for certain workloads.

The most immediate impact was the disruption of TCP connections. When a Pod restarts, all existing network connections are severed, forcing clients to reconnect and potentially lose in-flight requests for stateful and stateless workloads that need to maintain steady connections with data stores.

In-place resizing eliminates this disruption by allowing the container runtime to adjust resource limits and requests without terminating the container process.

How does In-Place Pod Resizing work?

To understand how In-Place Pod Resizing (In-Place Pod Vertical Scaling) works, we can take a trip back to 2019, with the original enhancement proposal being opened in GitHub issue #1287.

At its core, in-place resizing introduces a clear distinction between what you want and what you currently have. The Pod.Spec.Containers[i].Resources field now represents the desired state of Pod resources—think of it as your target configuration. Meanwhile, the new Pod.Status.ContainerStatuses [i].Resources field shows the actual resources currently allocated to running containers, reflecting what's really happening on the node.

This architectural change enables a more sophisticated resource management workflow. When you want to resize a Pod, you no longer directly modify the Pod specification. Instead, you interact with a new /resize sub-resource that accepts only specific resource-related fields. This dedicated endpoint ensures that resource changes go through proper validation and don't interfere with other Pod operations.

Also introduced is the concept of allocated resources through Pod.Status.ContainerStatuses [i].AllocatedResources. When the Kubelet initially admits a Pod or processes a resize request, it caches these resource requirements locally. This cached state becomes the source of truth for the container runtime when containers are started or restarted, ensuring consistency across the resize lifecycle.

Below is the diagram from the original KEP, which shows a simplified workflow of how this orchestration happens:

source: Kubernetes Enhancements repo

From the diagram:

The API server receives resize requests
The Kubelet watches for Pod updates and calls the container runtime's UpdateContainerResources() API to set new limits,
The runtime reports back the actual resource state through ContainerStatus().

To track the progress of resize operations, the system uses two new Pod conditions: PodResizePending indicates when a resize has been requested but not yet processed by the Kubelet, while PodResizeInProgress shows when a resize is actively being applied.

These conditions provide visibility into the resize lifecycle and help operators understand what's happening during resource transitions.

Use cases for In-Place Pod Resizing

With some of the inner workings understood, you are likely wondering how this applies to your workloads going forward. Here are a few use cases.

Machine learning

Machine learning workloads are perhaps the most compelling case for in-place resizing. Consider a typical ML pipeline where a model training job starts with data preprocessing, a CPU-intensive phase that requires minimal memory. As training progresses to the actual model computation phase, the workload becomes memory-intensive while CPU requirements may decrease.

Traditional scaling would require terminating the Pod and losing hours of training progress just to adjust resource allocation. With in-place resizing, the same Pod can transition from a CPU-optimized configuration during preprocessing to a memory-optimized setup during training, and then scale down to a balanced configuration for model serving.

Maintaining database connections through resource changes

Without in-place resizing, requesting additional memory would sever the database connection, forcing the job to re-establish connections, potentially lose transaction context. With in-place resizing, the same Pod can request additional memory mid-processing while maintaining its database connections.

Cost optimization

Where in-place resizing can deliver measurable value is cost savings. Traditional resource management often leads to over-provisioning because teams need to account for peak resource usage across the entire application lifecycle. A Pod that needs 4GB of memory during peak processing but only 1GB during steady state would typically be allocated 4GB throughout its entire lifecycle.

Hands-on with In-Place Pod Resizing

With many of the fundamentals out of the way, here's how you can test in-place Pod resizing locally.

Prerequisites

To follow along with this guide, you need the following tools configured on your machine:

KinD: This enables you to create a local cluster, and more specifically, you can specify the version of Kubernetes you’d like to run
Kubectl: This is used for interacting with the cluster

Step 1: Create a cluster configuration

In order to specify the version and feature gates of Kubernetes you would like to enable, KinD allows you to define this using a configuration file.

Within your terminal, run the following command:

cat <<EOF > cluster.yaml
kind: Cluster  
apiVersion: kind.x-k8s.io/v1alpha4  
name: inplace  
featureGates:  
  "InPlacePodVerticalScaling": true 
nodes:  
- role: control-plane  
  image: kindest/node:v1.33.1@sha256:050072256b9a903bd914c0b2866828150cb229cea0efe5892e2b644d5dd3b34f  
- role: worker  
  image: kindest/node:v1.33.1@sha256:050072256b9a903bd914c0b2866828150cb229cea0efe5892e2b644d5dd3b34f
EOF

The important bit to note here is featureGates, which is where you specify what feature gate to enable. In this case, InPlacePodVerticalScaling and the node images v1.33.1 are specified. This was obtained from the kind release page on GitHub.

Provision the cluster by running the following command:

kind create cluster --config cluster.yaml

Your output should be similar to:

Step 2: Create a test deployment

First, deploy a simple application that we can resize. To do this, apply the following manifest:

cat <<EOF | kubectl apply -f -  
apiVersion: apps/v1  
kind: Deployment  
metadata:  
  name: app  
spec:  
  replicas: 1  
  selector:  
    matchLabels:  
      app: app  
  template:  
    metadata:  
      labels:  
        app: app  
    spec:  
      containers:  
      - name: nginx  
        image: nginx  
        resources:  
          limits:  
            memory: "1Gi"  
            cpu: 3  
          requests:  
            memory: "500Mi"  
            cpu: 2  
        resizePolicy:  
        - resourceName: cpu  
          restartPolicy: NotRequired  
        - resourceName: memory  
          restartPolicy: RestartContainer
EOF

Take note of the resizePolicy configuration, this is where you specify how your application should handle resource changes. For CPU, you've set restartPolicy: NotRequired, meaning the container can have its CPU allocation adjusted without restarting. For memory, you've specified restartPolicy: RestartContainer, indicating that memory changes will trigger a container restart.

This configuration is particularly useful for memory-bound applications that need to restart anyway to take advantage of additional memory allocation. Applications like Java, processes with heap size configurations or databases with buffer pool settings often require a restart to properly utilize new memory limits, making the explicit restart policy a sensible choice.

Step 3: Verifying initial CPU allocation

Before making any changes, check the current CPU allocation by examining the container's cgroup settings:

kubectl exec -it $(kubectl get pods -l app=app -o jsonpath='{.items[0].metadata.name}') -- cat /sys/fs/cgroup/cpu.max

The command above checks /sys/fs/cgroup/cpu.max within the container because this is where the Linux kernel exposes the CPU quota and period settings that control how much CPU time a container can use.

The output shows two values:

The CPU quota (how much CPU time the container can use)
The period (the time window for that quota)

Together, these determine the effective CPU limit.

The output is similar to:

Step 4: Performing an in-place CPU resize

Now, increase the CPU limit from 3 to 4 cores using a patch operation:

kubectl patch deployment app --patch '{  
  "spec": {  
    "template": {  
      "spec": {  
        "containers": [{  
          "name": "nginx",  
          "resources": {  
            "limits": {  
              "cpu": "4"  
            }  
          }  
        }]  
      }  
    }  
  }  
}'

After applying the patch, check the CPU allocation again:

kubectl exec -it $(kubectl get pods -l app=app -o jsonpath='{.items[0].metadata.name}') -- cat /sys/fs/cgroup/cpu.max

The changes take a few seconds to reflect in the cgroup settings, but you should see output similar to:

Finally, you can verify there were indeed no restarts by running:

k get pods -o wide

The output is similar to:

Important nuances of in-place scaling (caveats)

Like all great things in software, there are some caveats to in-place Pod resizing.

Container runtime support

In-place resizing requires specific container runtime support, and not all runtimes are compatible. Currently, containerd v1.6.9+, CRI-O v1.24.2+, and Podman v4.0.0+ support the necessary APIs for in-place resource updates. If you're running an older runtime version, you'll need to upgrade before you can take advantage of this feature.

Default resize behavior

All new Pods are automatically created with a resizePolicy field set for each container. If you don't explicitly configure this field, the default behavior is restartPolicy: NotRequired, meaning containers will attempt in-place resizing without restarts. While this default works well for most applications, you should explicitly set resize policies for containers that require restarts to properly utilize new resource allocations.

Resource allocation boundaries

Requesting more resources than available on the node doesn't trigger Pod eviction, regardless of whether you're adjusting CPU or memory limits. This behavior differs from traditional resource management where resource pressure might cause Pod scheduling changes. Your resize request will simply remain pending until sufficient resources become available on the node.

Bringing Intelligence to Kubernetes Resource Management

The Octarine (v1.33) release of Kubernetes is a welcome development, reflecting the community's commitment to delivering innovative features. This blog covered in-place Pod resizing, what it is, why it exists and how you can use it in your Kubernetes environments.

As mentioned earlier, the Kubernetes autoscaling ecosystem consists of many tools to address different layers of an environment: Pods, resources, infrastructure, and external load.

If your current scaling setup relies solely on HPA and Cluster Autoscaler, you're likely leaving efficiency, resilience, and cost savings on the table. CloudPilot AI complements these tools by automating Spot instance management and intelligently selecting optimal nodes across 800+ instance types, helping teams scale smarter and spend less.

Welcome to join us at Slack channel / Discord

How to Deploy Karpenter on Google Cloud

CloudPilot AI — Wed, 03 Sep 2025 02:58:35 +0000

Karpenter GCP provider is now available in preview, enabling intelligent autoscaling for Kubernetes workloads on Google Cloud Platform (GCP). Developed by the CloudPilot AI team in collaboration with the community, this release extends Karpenter's multi-cloud capabilities.

⚠️ This is a preview release and not yet recommended for production use, but it's fully functional for testing and experimentation.

In this tutorial, you'll learn how to deploy the GCP provider using the Helm chart, configure your environment, and set up Karpenter to dynamically launch GCP instances based on your workloads.

Prerequisites

Before you begin, ensure the following are set up:

A running GKE cluster with Karpenter controller already installed (see Karpenter installation guide).
kubectl configured to access your GKE cluster.
helm (v3+) installed.
Karpenter CRDs already installed in your cluste
GCP permissions: The Karpenter controller and GCP provider need access to create instances, subnets, and disks.

Prepare the GCP Credentials

Enable Required APIs

Enable the necessary Google Cloud APIs for Karpenter to manage compute and Kubernetes resources:

gcloud services enable compute.googleapis.com
gcloud services enable container.googleapis.com

Create Service Account and Download Keys

Create a GCP service account with the following roles:

Compute Admin
Kubernetes Engine Admin
Monitoring Admin
Service Account User

These permissions allow Karpenter to manage GCE instances, access GKE metadata, and report monitoring metrics.

After creating the service account, generate a JSON key file and store it in a secure location. This key will be used to authenticate Karpenter with GCP APIs.

Create Cluster Secret

Create a Kubernetes Secret to store your GCP service account credentials:

apiVersion: v1
kind: Secret
metadata:
  name: karpenter-gcp-credentials
  namespace: karpenter-system
type: Opaque
stringData:
  key.json: |
    {
      "type": "service_account",
      "project_id": "<your-project-id>",
      "private_key_id": "<your-private-key-id>",
      "private_key": "<your-private-key>",
      "client_email": "<your-client-email>",
      "client_id": "<your-client-id>",
      "auth_uri": "https://accounts.google.com/o/oauth2/auth",
      "token_uri": "https://oauth2.googleapis.com/token",
      "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
      "client_x509_cert_url": "<your-client-x509-cert-url>",
      "universe_domain": "googleapis.com"
    }

Save the above as karpenter-gcp-credentials.yaml, then apply it to your cluster:

kubectl create ns karpenter-system
kubectl apply -f karpenter-gcp-credentials.yaml

Installing the Chart

Set the required environment variables before installing the chart:

export PROJECT_ID=<your-google-project-id>
export CLUSTER_NAME=<gke-cluster-name>
export REGION=<gke-region-name>
# Optional: Set the GCP service account email if you want to use a custom service account for the default node pool templates
export DEFAULT_NODEPOOL_SERVICE_ACCOUNT=<your-custom-service-account-email>

Then clone this repository and install the chart with the following command:

helm upgrade karpenter charts/karpenter --install \
  --namespace karpenter-system --create-namespace \
  --set "controller.settings.projectID=${PROJECT_ID}" \
  --set "controller.settings.region=${REGION}" \
  --set "controller.settings.clusterName=${CLUSTER_NAME}" \
  --wait

Testing Node Creation

1. Create NodeClass and NodePool

Apply the following manifests to define how Karpenter should provision nodes on GCP. Be sure to replace <service_account_email_created_before> with the email of the service account you created in the previous step.

cat > nodeclass.yaml <<EOF
apiVersion: karpenter.k8s.gcp/v1alpha1
kind: GCENodeClass
metadata:
  name: default-example
spec:
  serviceAccount: "<service_account_email_created_before>"
  imageSelectorTerms:
    - alias: ContainerOptimizedOS@latest
  tags:
    env: dev
EOF

kubectl apply -f nodeclass.yaml

cat > nodepool.yaml <<EOF
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: default-nodepool
spec:
  weight: 10
  template:
    spec:
      nodeClassRef:
        name: default-example
        kind: GCENodeClass
        group: karpenter.k8s.gcp
      requirements:
        - key: "karpenter.sh/capacity-type"
          operator: In
          values: ["on-demand", "spot"]
        - key: "karpenter.k8s.gcp/instance-family"
          operator: In
          values: ["n4-standard", "n2-standard", "e2"]
        - key: "kubernetes.io/arch"
          operator: In
          values: ["amd64"]
        - key: "topology.kubernetes.io/zone"
          operator: In
          values: ["us-central1-c", "us-central1-a", "us-central1-f", "us-central1-b"]
EOF

kubectl apply -f nodepool.yaml

2. Create a Workload

Deploy a simple workload to trigger Karpenter to provision a new node:

cat > deployment.yaml <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: inflate
spec:
  replicas: 1
  selector:
    matchLabels:
      app: inflate
  template:
    metadata:
      labels:
        app: inflate
    spec:
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                  - key: karpenter.sh/capacity-type
                    operator: Exists
      securityContext:
        runAsUser: 1000
        runAsGroup: 3000
        fsGroup: 2000
      containers:
      - image: public.ecr.aws/eks-distro/kubernetes/pause:3.2
        name: inflate
        resources:
          requests:
            cpu: 250m
            memory: 250Mi
        securityContext:
          allowPrivilegeEscalation: false
EOF

kubectl apply -f deployment.yaml

Once the workload is created, check if Karpenter has successfully provisioned a node:

$ kubectl get node
NAME                                       STATUS   ROLES    AGE     VERSION
gke-cluster-1-test-default-1c921401-kzbh   Ready    <none>   17d     v1.32.4-gke.1415000
gke-cluster-1-test-default-84243800-v30f   Ready    <none>   17d     v1.32.4-gke.1415000
gke-cluster-1-test-default-b4608681-5zq5   Ready    <none>   17d     v1.32.4-gke.1415000
karpenter-default-nodepool-sp86k           Ready    <none>   18s     v1.32.4-gke.1415000

$ kubectl get nodeclaim
NAME                     TYPE       CAPACITY   ZONE            NODE                               READY   AGE
default-nodepool-sp86k   e2-small   spot       us-central1-a   karpenter-default-nodepool-sp86k   True    46s

Nodes created by Karpenter will typically have a karpenter.sh/provisioner-name label and may include taints or labels defined in your NodeClass and NodePool.

Join the Community

Have questions, feedback, or want to follow development?

👉 Join our Slack channel

👉 Or hop into Discord to connect with fellow contributors and users

Your feedback will help shape the future of multi-cloud autoscaling with Karpenter!

Karpenter GCP Provider (Preview) is Now Available!

CloudPilot AI — Thu, 24 Jul 2025 02:19:12 +0000

We're excited to share that the Karpenter GCP Provider is now available in preview! This milestone brings Karpenter's powerful autoscaling capabilities to Google Cloud, helping users optimize resource efficiency and reduce infrastructure costs.

This new provider was initiated and primarily developed by CloudPilot AI, with close collaboration from the open-source community. It marks a major step toward making Karpenter a truly multi-cloud autoscaler.

GitHub repo: https://github.com/cloudpilot-ai/karpenter-provider-gcp
Discord: https://discord.gg/WxFWc87QWr
Join Slack channel to give us feedback!

What's Included in the Preview?

This early release gives GCP users a chance to experience Karpenter’s unique capabilities, tailored for the Google Cloud environment:

Smart node provisioning and autoscaling: Automatically launch the right instance types at the right time based on real-time workload requirements.
Cost-optimized instance selection: Choose the most efficient GCP instances by balancing cost and availability — without manual configuration.
Deep integration with GCP services: Work seamlessly with GCE, IAM, and other core GCP services to ensure smooth provisioning and lifecycle management.
Fast node startup and termination: Improve scheduling performance with quick provisioning and handle scale-in events gracefully to minimize disruption

This is a preview release, NOT yet recommended for production use. We're actively improving it and would love your feedback, testing, and issues to help shape the GA version. If you run into anything, feel free to reach out in Slack or Discord!

Thanks to the community!

A huge shoutout to everyone in the community who contributed to this release. Your support and collaboration made it possible.

Special thanks to:

@jwcesign

@dm3ch

@patrostkowski

@joshuajebaraj

Let's build this together. Try it out, give feedback, and help shape the future of autoscaling on Google Cloud!

K8s Cost Optimization: The Metrics That Actually Matter

CloudPilot AI — Mon, 21 Jul 2025 02:49:30 +0000

Kubernetes platforms like Amazon EKS have made it easier than ever to run Kubernetes clusters at scale—but with great flexibility comes great responsibility. Left unchecked, resource inefficiencies can silently drive up cloud costs. That's where smart resource monitoring comes into play.

In this blog, we'll walk through the key metrics you should monitor to optimize Kubernetes resource usage and reduce costs—especially in cloud environments. Whether you're running production workloads on EKS or just getting started, these best practices can help you stay lean and efficient.

Why Resource Monitoring Matters for K8s Cost Optimization

Kubernetes abstracts infrastructure away, but cloud bills remain painfully real. Poor observability often leads to

Overprovisioned workloads (paying for unused CPU/memory)
Underutilized nodes (wasting instance hours)
Zombie workloads (idle pods or forgotten namespaces)
Unbalanced scheduling (causing skewed utilization)

Monitoring helps you catch these early and make informed decisions on scaling, scheduling, and rightsizing.

Key Metrics to Monitor for Cost Optimization

Let's break down the metrics that matter most and what you can do with them.

1. CPU and Memory Requests vs Usage

Why it matters: Over-provisioning leads to wasted resources; under-provisioning causes instability.

What to monitor:

kube_pod_container_resource_requests_cpu_cores vs container_cpu_usage_seconds_total
kube_pod_container_resource_requests_memory_bytes vs container_memory_usage_bytes

What to look for:

Workloads consistently use <30% of their requested resources.
Pods OOM-killed due to under-provisioned memory.

Actionable tip: Use Vertical Pod Autoscaler (VPA) in recommendation mode to identify tuning opportunities.

2. Node Utilization (CPU/Memory)

Why it matters: Low node utilization means you're paying for idle EC2 capacity.

What to monitor:

node_cpu_utilization
node_memory_utilization

What to look for:

Nodes consistently are under 50% utilization.
Skewed workloads causing some nodes to stay mostly empty.

Actionable tip: Use tools like Karpenter to consolidate underutilized nodes.

If you're looking for an autonomous solution that does this (and more) out of the box, CloudPilot AI intelligently monitors node utilization and automatically replaces underutilized infrastructure with more cost-effective options—no manual tuning required.

3. Pod Scheduling Failures

Why it matters: Failed pod scheduling may lead to cluster overprovisioning.

What to monitor:

kube_pod_status_unschedulable
kube_pod_status_phase{phase="Pending"}

What to look for:

Frequent unschedulable events due to insufficient memory or CPU.
Scheduling constraints (e.g. taints, affinities) that reduce packing efficiency.

Actionable tip: Revisit affinity/anti-affinity rules, tolerations, and resource requests to allow better bin-packing.

Also consider cost-aware autoscalers like Karpenter or CloudPilot AI to rebalance workloads dynamically and reduce failed scheduling events.

4. Persistent Volume Usage

Why it matters: EBS volumes incur ongoing costs, even if idle or unmounted.

What to monitor:

kubelet_volume_stats_used_bytes
kube_persistentvolumeclaim_info (to detect unbound PVCs)

What to look for:

Volumes with little or no data but large allocations.
Orphaned PVCs and EBS volumes are not attached to any pod. Actionable tip: Regularly audit unused volumes. Consider lifecycle policies to auto-delete old EBS snapshots.

5. Idle Namespaces & Resources

Why it matters: Forgotten test workloads or zombie services can drain resources and rack up costs.

What to monitor:

Namespaces with no active pods.
Services without endpoints.

What to look for:

Old, unused dev/test namespaces.
CronJobs or Deployments with no traffic.

Actionable tip: Use cleanup scripts or TTL controllers to automatically clean up idle resources over time.

Setting Up Metrics Monitoring on EKS

To track these metrics effectively, you'll need a robust monitoring stack. Here’s a simple setup to get started:

Use Prometheus + Grafana

Installation:

Use Helm to install the kube-prometheus-stack:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update

helm install monitoring prometheus-community/kube-prometheus-stack --namespace monitoring --create-namespace

This will deploy:

Prometheus (metrics collection)
Grafana (visualization)
Alertmanager (optional)

Tip: Use default dashboards for node and pod resource usage. Customize them for idle resource detection and request-vs-usage comparisons.

Enable Cloud Cost Allocation

AWS supports native cost metrics via CloudWatch Container Insights. You can also enrich these metrics by exporting them to Prometheus or third-party cost observability platforms for deeper analysis.

Automate Alerts for Cost Risks

Use Prometheus alert rules for:

CPU/memory usage below thresholds
Unschedulable pods
Unused PVCs
Underutilized nodes

You can route these alerts to Slack, PagerDuty, or email.

Tools That Make It Easier

Tool	Use Case
CloudPilot AI	AI-powered automation to optimize node usage, spot pricing, and cost efficiency across EKS clusters
Karpenter	Smart autoscaling with efficient bin-packing
VPA	Suggests optimal resource requests
Goldilocks	Helps rightsize deployments using VPA
Lens	GUI to monitor pods, nodes, and workloads

Conclusion

Kubernetes doesn't magically reduce your cloud bill. In fact, without visibility, it's easy to overspend. But with the right metrics and monitoring practices in place, you can make smart decisions that balance performance and cost.

Start with small wins: identify underutilized pods, tweak requests, and reclaim idle volumes. Or go a step further with tools like CloudPilot AI, which brings intelligent automation to your EKS cluster—detecting cost risks, optimizing node selection, and managing Spot interruptions in real time.

Less waste, more performance—because every core and gigabyte counts.

CloudPilot AI vs Karpenter: Smarter Kubernetes Autoscaling, Lower Cloud Costs

CloudPilot AI — Fri, 04 Jul 2025 02:42:13 +0000

Karpenter is a powerful Kubernetes Node Autoscaler built for flexibility, performance, and simplicity. It automatically provisions compute resources in response to unschedulable pods, enabling faster scaling and better utilization compared to traditional cluster autoscalers.

However, when used in production environments with diverse workloads and dynamic spot pricing, teams often encounter non-obvious tradeoffs where availability risks or cost inefficiencies emerge.

CloudPilot AI is designed to address these advanced operational challenges. As an Autopilot for Kubernetes, it builds on the core principles of autoscaling while adding intelligent, context-aware behaviors that improve service resilience and optimize cloud costs—without adding operational complexity.

Here's a detailed comparison of how CloudPilot AI improves upon Karpenter's behavior in critical scenarios.

1. High Availability for Single Replica Workloads

Karpenter:

During consolidation or rebalancing, Karpenter may terminate a node that hosts a single-replica pod before the replacement is fully provisioned, leading to service downtime—even if briefly.

CloudPilot AI:

CloudPilot AI delays node termination until the new node is ready and the pod is confirmed running. This graceful handoff mechanism maintains availability for critical services like queues, databases, and stateful gateways, where even a few seconds of downtime can be unacceptable.

2. Predictive Spot Interruption Mitigation

Karpenter:

Karpenter reacts to the standard 2-minute spot interruption notice provided by AWS or other cloud providers. This may be insufficient in high-load situations, resulting in pod eviction delays and scheduling contention.

CloudPilot AI:

CloudPilot AI's Spot Prediction Engine uses predictive modeling to detect interruption signals up to 45 minutes in advance. It proactively drains and replaces high-risk nodes, dramatically reducing the chance of disruption during traffic spikes or deployment events.

3. Instance Type Diversification for Greater Resilience

Karpenter:

Karpenter often selects a single instance type to binpack workloads for cost efficiency. While performant, this can lead to instance-type lock-in, which amplifies risk during spot price spikes or batch interruptions.

CloudPilot AI:

CloudPilot AI deliberately distributes workloads across multiple instance types and availability zones, balancing cost efficiency with resilience. This reduces over-reliance on any one spot market and improves cluster availability during market fluctuations.

4. Automatic Anti-Affinity Enforcement

Karpenter:

Unless developers define pod anti-affinity, Karpenter may co-locate replicas of the same workload on the same node. This can create a single point of failure for multi-replica services.

CloudPilot AI:

CloudPilot AI enforces anti-affinity policies by default for replica workloads. It automatically ensures that replicas are spread across at least 2 nodes, helping teams achieve high availability without having to manage complex affinity rules manually.

5. Balanced Workload Placement for Safer Consolidation

Karpenter:

Karpenter's binpacking strategy tends to concentrate workloads on fewer large nodes to minimize spend. But when these nodes are reclaimed or rebalanced, the resulting disruption can be significant.

CloudPilot AI:

CloudPilot AI uses a balance-first placement strategy, spreading workloads across nodes of various sizes to reduce the impact of node terminations and support safer consolidation events.

6. Intelligent Scheduling for Persistent Volume Workloads

Karpenter:

If a Pod in a group depends on a Persistent Volume (PV) in a specific Availability Zone, Karpenter schedules the whole group in that zone. When the zone has limited capacity or higher prices, this can increase costs and risk service disruption.

CloudPilot AI:

CloudPilot AI detects which Pods depend on PVs and schedules only those in the required zone. The rest are placed in cheaper zones with better availability—reducing waste and avoiding scaling bottlenecks.

7. More Flexible Resource Allocation

Karpenter:

Karpenter doesn't take actual Pod resource usage or limits settings into account. If requests are misconfigured, it can lead to resource waste or increased risk of OOM (Out of Memory) errors during consolidation.

CloudPilot AI:

CloudPilot AI includes built-in Pod rightsizing. It continuously analyzes resource usage and dynamically adjusts CPU and memory settings in real time. Unlike Karpenter, which relies on users to manually configure requests, CloudPilot AI proactively optimizes this critical parameter—enabling more reliable and efficient autoscaling, reducing resource waste, improving scheduling stability, and minimizing risks like OOM errors and CPU throttling.

8.More Intuitive Visualization

Karpenter:

Karpenter relies on the command line for viewing resource states and activity logs. Information is fragmented and not easily visualized.

CloudPilot AI:

Comes with a real-time visual dashboard that consolidates resource changes, event logs, monthly spend, and historical cost trends—giving you a clear, centralized view of infrastructure activity at a glance.

Conclusion

Karpenter brings powerful, flexible autoscaling capabilities to Kubernetes. But for teams operating in fast-changing environments, where every minute of downtime or dollar spent matters, an additional layer of automation and intelligence is often required.

CloudPilot AI serves as the Autopilot for Kubernetes—building on the foundation of node autoscaling while solving the hidden challenges of production workloads. By combining predictive spot awareness, smart placement, and resilient scheduling, it helps organizations achieve both cloud cost optimization and autoscaling stability at scale.

Learn how CloudPilot AI can help your infrastructure scale safely, cost-effectively, and autonomously in minutes, not weeks.

Visit cloudpilot.ai to get started.

Netvue Achieves 52% Netvue Achieves 52% Reduction in GPU Costs using Automation

CloudPilot AI — Tue, 17 Jun 2025 02:57:40 +0000

Company Overview

Founded in 2010, Netvue is a global leader in smart home hardware and software solutions, with a strong focus on home security monitoring.

By combining advanced surveillance hardware with intelligent cloud services, Netvue enables real-time video monitoring and automated threat detection. The company serves over 1 million users worldwide and holds more than 40 patents.

Challenges

High GPU Costs and Limited Elasticity

To meet compliance requirements and manage traffic surges, Netvue deployed its AI inference services on GPU instances in Google Cloud. However, as the user base expanded, the associated GPU costs grew rapidly, becoming a major barrier to business scalability.

While Netvue had some auto-scaling capabilities in place, instance selection remained largely manual. This made it difficult to take advantage of more cost-effective resources like spot instances.

The lack of a cloud-native scheduler (e.g., Kubernetes) further limited flexibility and the GPU services were locked into Google Cloud, complicating upgrades and deployments.

Spiky Traffic and Inconsistent Demand

User traffic showed significant day-night fluctuations. During peak hours, GPU workloads surged rapidly, exposing the limitations of traditional scheduling strategies. This occasionally led to resource contention and cold starts, impacting model inference speed and user experience.

Cross-Cloud Overhead and Latency

Netvue stored image and video data in AWS S3, while its inference services ran on GCP, connected via dedicated interconnect. This cross-cloud setup introduced high bandwidth costs and increased inference latency due to inter-cloud data transfers — negatively affecting overall service performance.

Solution: Rebuilding GPU Scheduling Architecture

Results

52% reduction in GPU costs
Optimized instance selection and adoption of Spot GPUs reduced per-GPU monthly cost from over $180 to around $80.
Flexible and cloud-agnostic scheduling
Built a Kubernetes-based elastic GPU architecture, eliminating vendor lock-in.
5× faster response time
Co-locating compute and data eliminated cross-cloud latency.
Stable operations at scale
Rapid scaling during peak hours and precise downscaling during off-peak times ensured both cost efficiency and service stability.

To address rising costs and limited flexibility, Netvue partnered with CloudPilot AI to systematically optimize its GPU architecture—without overhauling its existing service logic.

Migrating to Kubernetes for Cloud-Agnostic Elasticity

With CloudPilot AI's support, Netvue migrated its inference services to Kubernetes and launched dedicated GPU clusters on AWS. This enabled dynamic GPU scheduling, automatic scaling, and unified management across multiple cloud environments. The new architecture decoupled workloads from the underlying platform and laid the foundation for multi-cloud expansion.

Intelligent Instance Selection: Smooth GPU Migration from GCP to AWS

Initially, Netvue deployed GPU workloads on GCP due to unavailable suitable resources on AWS. However, with most data residing in AWS, cross-cloud transfers introduced significant performance bottlenecks.

Using CloudPilot AI's instance recommendation engine, Netvue defined precise requirements (e.g., prioritizing T4/T4G families), located suitable Spot GPUs on AWS, and migrated inference workloads seamlessly—eliminating dependency on interconnects. CloudPilot's Spot interruption prediction engine further ensured workload stability.

Broader GPU Coverage via Multi-Architecture Support

Netvue expanded GPU availability by enabling scheduling across x86 and ARM-based architectures, easing supply pressure and lowering per-unit compute cost.

"As our business scaled rapidly, GPU costs in the cloud became a major constraint," said Oliver Huang, Head of Platform Development at Netvue. "CloudPilot AI not only helped us find the most cost-effective resources to meet our needs, but also gave our infrastructure the flexibility to evolve and operate more efficiently over time."

Running AI Inference in the Cloud: Challenges in Scaling and Cost

How does the Infra team at Netvue support business growth?

Our infrastructure team is responsible for keeping all of Netvue's cloud services running smoothly. We handle everything from cluster management and resource scheduling to performance tuning and cost control. We work closely with the engineering team to make sure users get a stable, low-latency experience across the globe.

Real-time performance is critical for us. For example, users rely on our cameras to monitor their children or pets in real time. That means we need to process image uploads, run inference, and deliver results as fast as possible.

To support this, we run large-scale GPU inference workloads in the cloud. With elastic scheduling, we can quickly scale up during traffic spikes and scale down during quiet hours. In a way, Infra is the backbone of the entire AI product experience.

What made you decide to optimize cloud costs?

There were two main reasons. First, GPU costs were growing rapidly. As our user base expanded, the number of inference requests surged, and our cloud bill started to climb fast. Second, our early architecture wasn't very flexible when it came to resource scheduling. During peak traffic, we often had to just ride it out — which isn't a sustainable strategy.

We needed a better way to balance performance and cost — something that could scale efficiently and reduce our reliance on a single cloud provider. That's why we partnered with CloudPilot AI to take a more systematic approach to cost optimization.

Cloud GPU Cost Optimization in Practice

What was your onboarding experience with CloudPilot AI like?

We took a careful approach when first integrating CloudPilot AI. The team worked closely with us to make sure everything fit our infrastructure. That hands-on support helped us quickly understand how to get value from the tool.

CloudPilot AI started by analyzing and assessing our environment, then provided valuable recommendations. Initially, we piloted their automation strategies—such as Spot GPU instance recommendations and scheduling optimizations—in our non-production environment. We were very careful not to disrupt production, so we ran thorough testing there first.

After multiple stable validation rounds in the test environment, we gradually rolled the strategy out to production. Throughout the process, we were impressed by CloudPilot AI's transparency and controllability—every suggestion was backed by data and could be implemented incrementally rather than forcing full automation right away.

Which CloudPilot AI features helped your team the most?

The features that benefited us most were intelligent node selection and multi-architecture GPU scheduling.

We used to rely on GCP because we couldn't find suitable GPUs on AWS. With CloudPilot AI, we defined requirements like "prefer A10 or T4," and it automatically found stable, cost-effective Spot instances on AWS—enabling us to migrate workloads back.

Additionally, multi-architecture support greatly expanded our resource pool, so we're no longer dependent on just the most popular instances.

How exactly do you use the intelligent node selection feature?

We set criteria like "prefer A10 or T4 GPUs," and CloudPilot AI automatically searches for the most stable and cost-effective Spot instances on AWS matching these specs. Previously, we couldn't find suitable AWS GPUs because no tools supported this kind of filtering, so we gave up. With CloudPilot AI, we quickly pinpointed available instances and successfully migrated our services back.

What results have you achieved with CloudPilot AI?

The most direct impact is a 52% reduction in GPU costs. We also built a Kubernetes-based, cloud-agnostic architecture with more flexible resource scheduling. After moving services to AWS, both data and inference workloads run on the same platform, significantly reducing latency.

More importantly, we can now easily handle traffic spikes without worrying about cold starts or resource limits. This combination of cost savings and performance gains has turned our infrastructure from a bottleneck into a driver for business growth.

What's next?

With CloudPilot AI, Netvue has optimized GPU scheduling, reduced inference costs, and turned infrastructure spending into a growth enabler. Ongoing optimization continues to enhance service quality, resource flexibility, and market competitiveness.

Next, Netvue will integrate Spot GPU interruption prediction to improve stability during peak loads and build a globally distributed, highly available inference network to support the global scaling of its AI services.

Hands-On with MCP Server: Simplifying AWS Cloud Cost Analysis

CloudPilot AI — Wed, 04 Jun 2025 03:11:26 +0000

As cloud-native architectures grow increasingly complex and resource usage becomes more fragmented, managing and optimizing cloud costs has become a critical challenge for engineering teams and organizations alike. The key question is how to "spend smarter"—avoiding unnecessary compute overhead and hidden waste.

This is particularly relevant for teams running on AWS. While pay-as-you-go pricing offers flexibility, misconfigured or idle resources can lead to significant waste. That's why cost visibility and analysis are becoming essential capabilities to improve efficiency.

In this article, we'll take a closer look at the Cost Analysis MCP Server, an open-source tool developed by AWS Labs. You'll learn how it leverages the Model Context Protocol (MCP) to simplify cloud cost analysis and deliver actionable insights.

GitHub repo: https://github.com/awslabs/mcp/tree/main/src/cost-analysis-mcp-server

What Is the MCP Server?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic. It provides a unified interface for large language models (LLMs) to interact with external data sources and tools.

The diagram below illustrates the key components of the MCP architecture:

MCP Host: The LLM-powered application that initiates the request, such as Claude or an IDE.
MCP Client: Maintains a 1:1 connection with the MCP Server, acting as a communication bridge.
MCP Server: Supplies context, tools, and prompt information to the MCP Client.

Why Use MCP Server?

Standardized Integration: MCP offers a consistent interface that simplifies the integration of AI models with external tools, accelerating development workflows.
Real-Time Communication: Supports technologies like Server-Sent Events (SSE) to enable real-time data exchange between models and servers.
Secure and Auditable: Built-in access control and logging features ensure secure and traceable interactions.
Highly Extensible: Easily integrates with a variety of tools, allowing teams to tailor functionality to specific business needs.

In the context of cloud cost analysis, MCP Server acts as a bridge between AI models and AWS cost data—enabling real-time cost insights, analysis, and optimization directly from the model.

Key Features

✅ Visual AWS Cost Analysis

Break down AWS costs with precision—organized by service, region, and usage tier.
Quickly identify which services drive your cloud spend and uncover opportunities for targeted optimization.

💬 Natural Language Cost Queries

No need to write complex queries. Ask questions like you would with ChatGPT: “Which service costs the most?” or “Why did my S3 spending spike?”
The server pulls real-time data from AWS pricing pages and the AWS Pricing API—no manual digging required.

📊 One-Click Cost Reports and Optimization Suggestions

Automatically scans your Infrastructure as Code (IaC) and generates tailored cost reports.
Get intelligent recommendations based on actual usage—for example, whether to switch to Reserved Instances or identify underutilized resources.

Getting Started

Prerequisites

Install uv via Astral
Use uv python install 3.10 to install Python 3.10
Set up credentials with permissions to access AWS services. Make sure you have:
- An AWS account with the necessary permissions
- AWS credentials configured via aws configure or environment variables
- IAM roles or users with access to the AWS Pricing API

Installation

Step 1: Install the AWS CLI

Use the following command to install the AWS Command Line Interface (CLI):

 curl "https://awscli.amazonaws.com/AWSCLIV2.pkg" -o "AWSCLIV2.pkg"

 sudo installer -pkg AWSCLIV2.pkg -target /

Once the AWS CLI is installed, configure your credentials:

 aws configure

You'll be prompted to enter your AWS Access Key ID, Secret Access Key, region, and output format.

Make sure the IAM user or role you're using has permission to access the AWS Pricing API.

Step 2: Install Amazon Q

Download and install Amazon Q by following the official documentation:
👉 Installing Amazon Q

Follow the on-screen instructions in Amazon Q to register an account—just use your email address. Once registered, log in to access the Amazon Q interface.

Step 3: Set Up the Configuration File

Create a configuration file at the following path:

~/.aws/amazonq/mcp.json

This file will define how Amazon Q connects to the MCP Server.

{
  "mcpServers": {
    "awslabs.cost-analysis-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cost-analysis-mcp-server@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "AWS_PROFILE": "your-aws-profile"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

AWS Authentication
The MCP Server uses the AWS credentials specified by the AWS_PROFILE environment variable.

If AWS_PROFILE is not set, it will fall back to the default profile in your AWS CLI configuration.

"env": { 
    "AWS_PROFILE": "your-aws-profile"
    }

Step 4: Start a Session

After installation, the MCP Server will create a boto3 session using the specified configuration file. This session will be used to authenticate with AWS services.

Your AWS IAM credentials will always remain local, used only for accessing AWS services.

q chat

Output (Start watching from 00:47 in the following video):

Watch Here

Conclusion

The AWS Cost Analysis MCP Server provides businesses with an efficient and intelligent solution for cloud cost analysis. By leveraging the standardized MCP protocol, companies can easily integrate cost analysis features to enhance their cloud cost management capabilities.

If you're ready to go beyond just cost analysis and begin optimizing your AWS cloud costs, consider trying CloudPilot AI, an intelligent cloud cost optimization platform. With just a few clicks, you can start optimizing your cloud spend. Below is a real-world example of the results our customers have achieved.

We offer a 30-day free trial — feel free to give it a try!

Unveiling the Truth Behind AWS Savings Plans: Cost Savings or Hidden Constraints?

CloudPilot AI — Fri, 30 May 2025 02:13:04 +0000

In the realm of cloud computing, AWS Savings Plans are often touted as a comprehensive solution for AWS cost optimization. Introduced by Amazon Web Services in November 2019, these plans promise significant discounts compared to on-demand pricing, aiming to reduce computing costs for users.

However, a deeper examination reveals that relying solely on AWS Savings Plans might not always lead to the anticipated savings and could introduce certain limitations.

Understanding AWS Savings Plans

AWS Savings Plans offer a flexible pricing model that provides up to 72% savings compared to on-demand pricing. By committing to a consistent amount of usage (measured in $/hour) for a 1- or 3- year term, businesses can unlock substantial discounts across various AWS services.

Types of AWS Savings Plans

Compute Savings Plans: These plans offer the most flexibility, applying to any EC2 instance regardless of region, instance family, operating system, or tenancy. They also extend to AWS Fargate and AWS Lambda usage, making them ideal for dynamic workloads.
EC2 Instance Savings Plans: Tailored for specific instance families within a region, these plans provide the highest discount rates, up to 72%. They are suitable for predictable workloads with consistent usage patterns.

Potential Pitfalls of AWS Savings Plans

While AWS Savings Plans are designed to aid in AWS cost optimization, they come with certain caveats:

Commitment Risks: Committing to a fixed usage level for 1 or 3 years can be risky if your organization's needs change, potentially leading to underutilization and wasted expenditure.
Limited Flexibility: Although more flexible than Reserved Instances, Savings Plans still require adherence to specific usage patterns to maximize benefits.
Complexity in Management: Effectively managing and monitoring Savings Plans necessitates a thorough understanding of AWS billing and usage patterns, which can be complex and time-consuming.

The Hidden Costs of Savings Plans

Though AWS Savings Plans offer pricing flexibility across instance types and services (including EC2, Fargate, and Lambda), they still require a fixed hourly spend. This commitment model can result in unnecessary costs and limit your architectural agility in several ways:

1. You Pay for What You Don't Use

If your actual usage drops below your committed hourly spend—due to scaling down, seasonal demand, or architectural changes—you still pay the full rate. In fast-changing environments, this often results in overpayment and wasted budget.

2. Reduced Flexibility for Evolving Architectures

As organizations modernize their infrastructure—shifting to Kubernetes, containers, serverless, or adopting spot instances for cost optimization—usage patterns become more dynamic and harder to predict.

Savings Plans, by contrast, assume consistent usage. This mismatch can result in underutilized commitments and wasted spend, particularly during architectural transitions.

3. Expensive Charges Outside the Plan

Savings Plans only apply to specific instance families, regions, or compute types, depending on the plan you choose. Any usage outside the committed scope is billed at the full On-Demand rate—often the most expensive pricing tier.

If your workloads deviate from the original assumptions, you risk incurring high, unexpected charges that negate your savings.

Strategies for Effective AWS Cost Optimization

To truly harness the benefits of AWS Savings Plans, consider the following strategies:

Regular Monitoring: Utilize AWS Cost Explorer to track usage and ensure that your Savings Plans align with actual consumption.
Diversify Cost Optimization Tools: Don't rely solely on Savings Plans; explore other AWS cost optimization tools and practices to achieve comprehensive savings.
Flexible Planning: Anticipate potential changes in workload and usage patterns to adjust commitments accordingly.

CloudPilot AI: Flexible, Intelligent AWS Cost Optimization

At CloudPilot AI, we help teams unlock the full potential of the cloud without long-term lock-in. With our platform, you will have:

45-minute spot interruption prediction for proactive, disruption-free workload autoscaling.
Predictive algorithms that reduce spot instance interruptions by up to 90%, enhancing reliability.
Intelligent instance selection across pricing models, availability zones, and instance types for optimal performance and cost.
Real-time, commitment-free cost optimization that automatically adjusts to changing workload demands.

With CloudPilot AI, you get the elasticity of Spot, the reliability of On-Demand, and the intelligence to balance both—without the constraints of a Savings Plan.

Conclusion

AWS Savings Plans can be valuable in specific use cases, but they are not a one-size-fits-all solution. It's crucial to approach them with a clear understanding of their limitations and to integrate them into a broader, more flexible cost management plan. By doing so, businesses can avoid potential pitfalls and truly capitalize on the savings opportunities AWS offers.

AWS Cost Optimization with Spot Instances: The Ultimate Guide to Saving Big

CloudPilot AI — Thu, 15 May 2025 02:22:09 +0000

In today's fast-moving digital landscape, optimizing cloud costs is a top priority for businesses using Amazon Web Services (AWS). Spot Instances offer a powerful way to cut expenses by tapping into unused EC2 capacity at steep discounts—often up to 90% off on-demand pricing. However, their ephemeral nature and market-driven pricing require a strategic approach.

This article explores how Spot Instances can transform AWS cost optimization, helping organizations scale efficiently while keeping budgets under control.

Understanding Spot Instances and Cost Optimization on AWS

AWS offers flexible and scalable cloud computing, and Spot Instances are one of the most cost-effective options. They allow users to access unused EC2 capacity at significantly lower prices — up to 90% cheaper than On-Demand instances.

Spot Instance Pricing Mechanism

Spot pricing is determined dynamically based on supply and demand, rather than user bidding. When demand increases or capacity decreases, AWS may reclaim Spot Instances, resulting in spot instances sudden termination.

While AWS provides only a 2-minute interruption notice, CloudPilot AI extends this window to 45 minutes, giving users more time to react. Additionally, CloudPilot AI can automatically fallback services to more stable instances, including both Spot and On-Demand instances, ensuring workload continuity.

By strategically integrating Spot Instances, businesses can cut costs while maximizing resource efficiency, making them a powerful tool for AWS cost optimization.

Spot Instances vs. Reserved Instances vs. On-Demand Instances

Choosing the right AWS instance type depends on your workload's cost sensitivity, availability requirements, and tolerance for interruptions. Here’s how Spot Instances, Reserved Instances (RIs), and On-Demand Instances compare:

Instance Type	Cost Savings	Availability	Use Case
Spot Instances	Up to 90% cheaper than On-Demand	Can be interrupted with 2-minute notice	Ideal for fault-tolerant workloads (batch processing, ML training, CI/CD)
Reserved Instances (RIs)	Up to 72% cheaper for 1- or 3-year commitments	Always available	Best for predictable, steady-state workloads
On-Demand Instances	Most expensive option	Guaranteed availability	Used for mission-critical, unpredictable workloads

Which One Should You Use?

For cost-sensitive workloads: Use Spot Instances with automated fallback to On-Demand.
For long-term, stable workloads: Reserved Instances provide the best savings.
For unpredictable traffic spikes: On-Demand Instances ensure immediate capacity.

A hybrid approach—mixing Spot, RIs, and On-Demand—often yields the best balance between cost efficiency and reliability.

Key Considerations for Using Spot Instances

Spot Instances offer significant AWS cost savings, but their price fluctuates based on demand, and AWS can reclaim them at any time. To use them effectively, businesses must evaluate workload suitability, interruption handling, and monitoring strategies.

Workload Suitability – Spot Instances work best for stateless, fault-tolerant workloads like batch processing, big data analysis, and CI/CD pipelines.For mission-critical applications that require high availability, On-Demand or Reserved Instances should be used instead.
Interruption Handling – AWS may reclaim instances when demand rises. While standard mitigation strategies include checkpointing and failover to On-Demand instances, CloudPilot AI goes further by offering a 120-minute interruption notice and automated fallback to more stable instances, reducing downtime and manual intervention.
Monitoring & Optimization – Tracking Spot pricing trends and performance is essential for cost efficiency. AWS CloudWatch provides basic monitoring, but Spot Insights offers real-time price fluctuations, interruption probabilities, and instance availability trends, helping users make smarter, data-driven allocation decisions.

By carefully planning for these factors, organizations can maximize cost savings while maintaining operational stability.

Best Practices for Leveraging Spot Instances on AWS

Spot Instances can cut AWS costs by up to 90%, but but leveraging them effectively requires strategic planning.

By adopting the right workload strategies, optimizing instance selection, and using automation tools like Karpenter, businesses can achieve substantial cloud cost reductions while maintaining reliability.

1. Adopt a Hybrid Instance Strategy

Combining Spot, On-Demand, and Reserved Instances ensures both cost efficiency and stability:

Reserved or On-Demand Instances provide stability for critical workloads.
Spot Instances can dynamically scale to handle fluctuations in demand.
AWS Auto Scaling or Karpenter can intelligently provision and balance instances based on workload needs.

2. Architect for Spot Interruptions

Since AWS can reclaim Spot Instances with a 2-minute notice, resilience is key:

Use Auto Scaling groups, Karpenter or CloudPilot AI to automatically replace interrupted instances.
Implement checkpointing in long-running jobs for fast recovery.
Leverage Kubernetes with Karpenter to dynamically adjust instance allocation across multiple instance types and availability zones.

3. Optimize Instance Selection with Karpenter

To improve reliability, avoid depending on a single instance type:

Use Karpenter’s Spot capacity-aware scheduling to automatically select the best-priced, most available instances across different families and zones.
Monitor Spot price trends and historical availability using Spot Insights to make data-driven decisions.

4. Smart Scheduling and Workload Management

Some workloads align better with Spot Instances, especially those that can tolerate interruptions:

Batch jobs, big data processing, and ML training are well-suited for Spot.
Schedule workloads during off-peak hours for better availability and lower prices.
Use AWS Batch or Kubernetes job scheduling with Karpenter to dynamically distribute workloads across Spot and On-Demand Instances.

By following these best practices and leveraging Spot Insights for deeper visibility, businesses can maximize Spot Instance savings while maintaining a resilient and cost-effective cloud infrastructure.

Conclusion: Maximizing AWS Cost Efficiency with Spot Insights

Mastering Spot Instances is key to driving AWS cost efficiency, offering savings of up to 90% on EC2 capacity. However, their fluctuating availability demands a strategic approach to workload management.

By architecting for interruptions, optimizing bidding strategies, and leveraging a mix of instance types, businesses can unlock the full potential of Spot Instances. Tools like Spot Insights provide real-time interruption predictions, price trends, and availability zone fluctuations, enabling smarter decision-making and maximizing cost savings while ensuring workload reliability.