Karpenter Enterprise 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 the Karpenter Enterprise suite, see Karpenter Enterprise 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

Consolidation with minimal disruption

Cast AI's Evictor integrates with Karpenter to improve Node utilization while minimizing workload disruption.

What it adds to Karpenter:

• Coordination with Workload Autoscaler to consolidate Pods according to optimized resource requests, even if they are yet to be applied to the Pod and are pending
• Container Live Migration support for eligible workloads, preserving Pod state and TCP connections when moving Pods from Node to Node (graceful fallback to traditional eviction included)
• Progressive consolidation that respects Pod Disruption Budgets

How it differs from standard Cast AI:

[PLACEHOLDER: Specific behavior differences from engineering — e.g., how Evictor interacts with Karpenter's consolidation policy, whether Karpenter consolidation should be disabled]

For Evictor documentation, see Evictor.

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

How it differs from standard Cast AI:

[PLACEHOLDER: Specific behavior differences from engineering — e.g., how Rebalancer modifies NodePool CRDs, timing coordination with Karpenter, etc.]

For Rebalancer documentation, see Rebalancer.

Spot intelligence

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

What it adds to Karpenter:

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

How it differs from standard Cast AI:

[PLACEHOLDER: Specific behavior differences from engineering — e.g., how Spot Handler integrates with Karpenter's disruption budgets, whether interruption prediction triggers Karpenter consolidation]

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 Evictor and Rebalancer for coordinated optimization

How it differs from standard Cast AI:

Workload Autoscaler behavior is largely identical whether you're using Karpenter Enterprise 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.

→ Pod mutations documentation

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.

Features not available with Karpenter

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

If you would like to benefit from these capabilities, consider migrating to Cast AI Autoscaler.

Related resources

• Karpenter Enterprise overview — Conceptual introduction
• Getting started — Connect your cluster
• Migration from Karpenter — Move to full Cast AI Autoscaler