One Engine. Autonomous Optimization.

A single optimization engine that understands your entire Kubernetes environment, from containers to nodes to cloud instances, eliminating the need for separate tools and manual tuning.

Human Users

Customer LLMs/Agents

Agentic AI

Predictive Pod Scaler

HPA Optimizer

Karpenter Optimizer

Node Pre-Warmer

Bin Packer

Agent Builder

Core Engines

Deterministic ML Engine

Automation Engine

Infra Matching Engine

Apps and Infrastructure

Workloads

Microservices

Stateful

AI Inference + Training

K8s

K8s Scheduler

Node Autoscaler

HPA KEDA

NVIDIA KAI Scheduler

Karpenter

OpenShift Autoscaler

Infra

CPU

GPU/XPU (MIG, Timeslicing, MPS)

Storage & Network

Provider

Hyperscalers

NeoClouds

On-Prem

For Who

Designed for Humans and AI Systems

Platform Teams & Engineers

Monitor, control, and optimize infrastructure without manual tuning.
AI Agents & LLM Systems

Programmatically interact with Kubex to manage workloads dynamically.

How Kubex Works

Observes

Kubex continuously monitors workloads, usage patterns, and infrastructure signals.

Understands

A deterministic ML engine analyzes demand and predicts future behavior.

Decides

Optimization strategies are generated across Kubernetes and infrastructure layers.

Autonomously Acts

Automation engine applies changes in real-time — scaling, scheduling, and resource allocation.

Matches Infrastructure

Workloads are dynamically mapped to the most efficient compute (CPU, GPU, etc).

Engines

Core Engines Behind Kubex

Deterministic ML Engine

Predicts workload behavior with precision and consistency.
Automation Engine

Executes optimizations without manual intervention.
Patent Pending

Infra Matching Engine

Aligns workloads with the most efficient compute resources.