Explore All Features

Continuous pod rightsizing based on actual usage

Adjusts Pod CPU/memory in place, without restart, on live workloads

AS-IS → TO-BE savings, quantified per namespace.

ML-driven real-time horizontal scaling in <10 seconds

Schedule scaling for predictable traffic events

AI-recommended min/max replica boundaries for HPA

Real-time Karpenter node monitoring with cost tracking and disruption detection

Proactive node pre-provisioning with image pre-caching — 10x faster pod startup

Zero-touch spot/on-demand splitting via webhook injection

Root-cause CrashLoopBackOff and container failures fast.

Detect memory leak patterns before OOM failures occur

Identify pod scheduling delays before they impact performance

Detect pod restarts, throttling, and resource inefficiencies

Identify and reclaim underutilized nodes automatically

Detect and clean up unused PersistentVolumes to save costs

Predict CPU/memory/pod-count limit breaches 7-30 days ahead

Forecast PersistentVolume capacity exhaustion 7-30 days in advance

Traffic management with priority queuing and throttling

Unified NetFUNNEL + autoscaling traffic control

Every card in the cluster on one screen, built for 100 GPUs and more

See what each GPU workload is actually doing, not just what it reserved

29 checks: 12 on the hardware, and 17 on the sharing layer when card sharing is in use

Correct guessed GPU reservations with measured demand

Share one card across several workloads, split by the MiB

Idle models move to host memory so the busy ones keep the card

Production is served first, and the job behind it pauses instead of being killed

Close to the speed of an unshared card, with memory isolation still on

Raise the GPU memory limit during a spike, without restarting the container