How Karpenter Works

In today’s world, learning DevOps is essential, and staying updated with new trends is equally important. Today, let’s explore a powerful tool called Karpenter. Today, let’s explore what is Karpenter, why it’s gaining attention, and how it works behind the scenes to make Kubernetes scaling faster and smarter.

Let’s Understand:

• Introduction to Karpenter

• How Karpenter works

• Why teams are switching to it from the default Cluster Autoscaler

• Key benefits and real-world use cases

Introduction to Karpenter

It is an open-source tool built for Kubernetes to manage its nodes better. Adding Karpenter to a cluster can improve the efficiency and lower the cost of running workloads on the cluster.

How Karpenter works

From the above diagram let’s know how karpenter works

Pending Pods

• There are some pending pods waiting because no existing node in the cluster has enough free resources for them.

• Kubernetes Scheduler first tries to fit them onto existing nodes.

Scheduler

• If resources are available in any existing worker node, the scheduler will place the pods there.

Unschedulable Pods

• If the scheduler can’t place the pods anywhere (due to CPU, memory, affinity rules, memory, labels, etc.), they become unschedulable.

• At this point, Karpenter steps in.

Karpenter Provisioning (Just-in-Time Capacity)

• Karpenter understands pod needs and checks what each pod requires (CPU, memory, tolerations, memory, labels, affinities etc.).

• Based on the pod requirements karpenter provisions new EC2 nodes just in time to handle the pending workload.

• It Adds new nodes that match those needs so the pods can start running.

• It Removes unused nodes that are no longer needed, helping reduce costs.

Optimized Capacity

• Karpenter makes sure your cluster always has the right amount of resources enough to handle your workloads, without over-provisioning, and not so much that you’re paying for idle nodes.

• Once workloads finish and nodes are no longer needed, Karpenter can remove them to save costs.

For example: You have an e-commerce site gets a traffic spike during a sale, Karpenter will quickly add extra nodes to keep things running smoothly, then shut them down once the sale ends.

Why teams are switching to it from the default Cluster Autoscaler

1. Faster Scaling

   • Cluster Autoscaler can take minutes to bring new nodes online.

   • Karpenter provisions nodes in seconds, reducing pod wait times and improves application responsiveness.

2. Smarter Node Selection

   • Cluster Autoscaler works with predefined node groups.

   • Karpenter can launch any EC2 instance type that matches the workload’s needs, giving more flexibility and better cost efficiency.

3. Better Cost Optimization

   • Karpenter takes advantage of AWS Spot Instances and right-sizes nodes automatically.

   • This reduces over-provisioning and saves cost without manual tuning.

4. Simpler Management

   • No need to maintain multiple Auto Scaling Groups (ASGs).

   • Karpenter dynamically manages capacity, cutting down on operational overhead.

5. Advanced Scheduling Features

   • Karpenter understands complex scheduling rules like affinities, taints, tolerations, and topology spread constraints out of the box.

Teams switch because Karpenter is faster, cheaper, and easier to manage, while giving more flexibility in choosing the right nodes for the job.

Karpenter vs. Kubernetes Cluster Autoscaler

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Real-world use cases

• E-commerce during Flash Sales

  • During high traffic karpenter quickly scales up to handle massive traffic spikes. Once the traffic is low, it scales back down, so you’re not paying for extra capacity you don’t need.

• Data Processing & Analytics

  • Handles large batch jobs by provisioning powerful, short-lived nodes, then terminates them once the job is done.

• Gaming Backends

  • Scales game servers up and down in near real time based on player load.

• Machine Learning Training

  • Launches GPU nodes only when training workloads are running, keeping GPU costs low.

• Dev/Test Environments

  • Creates capacity only when developers are actively testing or deploying features.

Karpenter-specific concepts that define:

how it provisions nodes, what kind of nodes it provisions, how scheduling works, and when nodes get replaced or removed.

1. NodeClasses

NodeClass defines cloud provider–specific settings for nodes. In AWS, this is called an EC2NodeClass, which contains EC2-specific configurations such as AMI type, networking, storage, and tags.

2. NodePools

• What it is:
  A NodePool is a set of rules that describes what kind of capacity Karpenter should manage. Such as instance types, capacity type (On-Demand or Spot), zones, and labels as well as rules that determine which pods can be scheduled onto those nodes.

  What it contains:

  • Constraints like instance types, zones, AMI families, architectures.

  • Provisioning limits (max CPU, memory).

  • Labels and taints to match or isolate specific workloads.

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2. NodeClaims

• What it is:
  Karpenter uses NodeClaims to manage the lifecycle of Kubernetes Nodes with the underlying cloud provider. NodeClaims are created or deleted automatically based on the demands of pods in the cluster. When pods are pending, Karpenter evaluates their requirements, identifies a compatible NodePool and NodeClass pair, and then creates a NodeClaim that satisfies both sets of constraints.

• Although NodeClaims are immutable resources managed by Karpenter, you can monitor NodeClaims to keep track of the status of your Nodes.

• A NodeClaim is an actual request to create a single node.
  It’s like an order slip that says, “Hey AWS, give me one EC2 node with these specs.”

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3. Scheduling

• What it is:
  Scheduling in Karpenter is the process of matching pending pods with the best node(s) to run them.
  While Kubernetes also has a scheduler, Karpenter works alongside it to decide what nodes to create for those pods.

By using Karpenter’s layered constraints model (NodePool + NodeClass + pod scheduling requirements), you can ensure that the right type and amount of capacity is provisioned for your workloads.

• Key points:

  • Karpenter looks at resource requests, node selectors, affinity rules, and taints/tolerations.

  • It tries to pack pods efficiently to reduce cost.

  • It provisions nodes just in time no idle overprovisioning.

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4. Disruption

• What it is:
  Disruption is Karpenter’s way of shutting down or replacing nodes to save cost or improve efficiency. Karpenter automatically discovers disruptable (e.g., empty, underutilized, outdated, or Spot instances marked for termination) and spins up replacements when needed. Karpenter uses disruption budgets to control the speed at which these disruptions occur, ensuring workload stability during scaling or consolidation.

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Here a question arises:

Do you really need HPA/VPA if you’re already using Karpenter?

Yes, but why we still need HPA and VPA let’s understand.

Karpenter: Focuses on provisioning number and type of nodes based on pod needs to run Kubernetes workflow. It doesn’t increase pods inside those machines, and it doesn’t change pod CPU/memory.

HPA(Horizontal pod auto-scaling): Scales the number of pod replicas based on metrics like CPU or memory utilization, helps to handle fluctuating traffic and workload demands by adding or removing pods. If you skip HPA, your app will be stuck with the same number of pods, even if they are overloaded.

VPA (Vertical Pod Autoscaler): Automatically adjusts the CPU and memory requests/limits of individual pods based on their actual usage. The goal of VPA is to optimize resource allocation, help apps that don’t scale well horizontally (like databases or batch jobs), prevent under-provisioning (crashes) and over-provisioning (wasting resources).

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