Cast AI for Karpenter brings Cast AI's optimization capabilities to clusters running open-source Karpenter. This page provides an overview of available features and how they integrate with your existing Karpenter setup.

For a conceptual introduction to Cast AI for Karpenter, see Cast AI for Karpenter overview.

Feature availability

The following features are available for Karpenter-managed clusters:

How Cast AI features work with Karpenter

Cast AI features are designed to extend Karpenter rather than replace it. The integration follows these principles:

Karpenter remains the provisioner
Node creation and deletion continue to flow through Karpenter. Cast AI influences decisions by modifying Karpenter CRDs and providing optimization signals, but Karpenter executes the actual infrastructure changes.

CRD-native configuration
Where possible, Cast AI stores configuration in Kubernetes-native formats. Your existing NodePools and EC2NodeClasses remain the source of truth for provisioning constraints.

Incremental enablement
Each feature can be enabled independently. You can start with cost reporting only, then gradually enable optimization features as you build confidence.

Feature details

Continuous rebalancing

Kentroller's Continuous Rebalancing monitors your cluster on a recurring cycle and consolidates underutilized nodes. When enabled, it takes over consolidation from Karpenter entirely — Karpenter continues to handle drift detection, but cost-driven consolidation is managed exclusively by Kentroller.

What it adds to Karpenter:

• Coordination with Workload Autoscaler to consolidate Pods based on actual resource usage, including pending rightsizing recommendations not yet applied
• Container Live Migration for eligible workloads, preserving Pod state and TCP connections (graceful fallback to traditional eviction included)
• Configurable modes — delete-empty, drain-only, and full — for progressive or aggressive consolidation
• Savings thresholds that ensure rebalancing only runs when projected gains are worthwhile
• Fine-grained per-Pod and per-Node eviction policies via eviction config

Protecting Nodes from consolidation

To exclude specific Nodes from Continuous Rebalancing, apply the autoscaling.cast.ai/removal-disabled label:

kubectl label node <node-name> autoscaling.cast.ai/removal-disabled=true

For full configuration details, see Continuous rebalancing.

Rebalancer

The Rebalancer optimizes your entire cluster by identifying Nodes that could be replaced with more cost-effective alternatives for your workloads.

What it adds to Karpenter:

• Cross-NodePool optimization that Karpenter doesn't perform natively
• Awareness of Reserved Instances and Savings Plans
• Coordinated replacements that maintain workload stability
• Integration with Workload Autoscaler to rebalance based on optimized resource requirements

How it differs from standard Cast AI:

Rebalancing scope

Cast AI for Karpenter only rebalances nodes that were created and are managed by Karpenter. The following nodes are excluded from rebalancing:

• Legacy nodes created before Karpenter was installed
• EKS-managed node group nodes
• Nodes created by other provisioners (Cluster Autoscaler, etc.)

This limitation exists by design: Cast AI for Karpenter operates through Karpenter's CRDs and permissions, and does not have cloud-side control over non-Karpenter nodes. If you need to optimize non-Karpenter nodes, consider migrating them to Karpenter management or using Cast AI's standard Autoscaler.

Spot intelligence

Cast AI improves Karpenter's Spot Instance handling with predictive capabilities and reliability improvements.

What it adds to Karpenter:

• Interruption prediction — Identifies at-risk Nodes before AWS announces interruptions
• Spot reliability model (coming in future releases) — Steers toward historically stable Spot pools
• Spot fallback recovery (coming in future releases) — Automatically returns to Spot when capacity becomes available again

How interruption prediction works

Cast AI's Spot interruption prediction provides up to 30 minutes of advance warning before interruptions occur—significantly extending AWS's standard 2-minute notice. The prediction mechanism differs based on whether Container Live Migration is enabled:

In both cases, the extended prediction window provides significantly more time for graceful workload migration compared to waiting for AWS's native interruption signal.

How it differs from standard Cast AI:

For Spot handling documentation, see Spot Instances and Spot Handler.

Workload Autoscaler

Workload Autoscaler continuously rightsizes workloads based on actual resource usage.

What it adds to Karpenter:

• Automatic adjustment of CPU and memory requests to match actual usage
• Tighter bin-packing as rightsized workloads require less capacity
• Integration with Continuous Rebalancing and Rebalancer for coordinated optimization

How it differs from standard Cast AI:

Workload Autoscaler behavior is largely identical whether you're using Cast AI for Karpenter or Cast AI's Autoscaler—it operates at the workload level independently of Node provisioning.

For Workload Autoscaler documentation, see Workload Autoscaling.

Pod mutations

Pod mutations automate Pod spec adjustments to simplify workload configuration and reduce manual efforts by teams.

What it adds to Karpenter:

• Automatic application of labels, tolerations, and NodeSelectors
• Simplified onboarding without modifying Deployment manifests
• Consistent Pod configuration across workloads

How it differs from standard Cast AI:

Pod mutations work identically with Karpenter and standard Cast AI. The mutations apply to Pod specs before creation, independent of which autoscaler provisions Nodes.

For Pod mutations documentation, see Pod mutations.

Cost reporting

The savings report and other cost monitoring capabilities provide visibility into your cluster's optimization potential without making any changes.

What it provides:

• Current vs. optimized cost comparison
• Node utilization and bin-packing analysis
• Commitment utilization tracking
• Spot adoption opportunities
• Workload rightsizing recommendations

How it differs from standard Cast AI:

Cost reporting works identically for Karpenter clusters. The analysis examines your current state and models what Cast AI optimization could achieve.

For general cost monitoring, see Cost Monitoring.

Labels reference

Cast AI for Karpenter uses specific labels to coordinate optimization activities with Karpenter. Understanding these labels helps with troubleshooting and protecting critical nodes.

Node labels

Workload labels

Protecting nodes from optimization

To exclude a node from Continuous Rebalancing:

kubectl label node <node-name> autoscaling.cast.ai/removal-disabled=true

To also prevent Karpenter from disrupting the node (including drift-triggered replacements):

kubectl label node <node-name> karpenter.sh/do-not-disrupt=true

Features not available with Karpenter

Some Cast AI capabilities require tighter integration with Node scheduling than the Karpenter-layered approach allows:

Additionally, the following Rebalancer capabilities are not available when using Karpenter (see Rebalancing scope for details):

• Aggressive mode
• Graceful eviction controls
• Paused drain configuration
• Progress bar in UI

To benefit from these capabilities, consider migrating to Cast AI Autoscaler.

Related resources

• Cast AI for Karpenter overview — Conceptual introduction
• Getting started — Connect your cluster