Kubex

Agentic AI that combines optimization intelligence with natural language interaction, allowing users to query resources and receive intelligent recommendations via conversational interface

Enhanced sharing with interactive tables and deep links for exporting specific data elements

Analyzes historical usage data to identify patterns, peaks, seasonality, and trends across containers, deployments, and namespaces

Interactive dashboards for viewing optimization opportunities and comparing current vs. recommended resource settings

Reports categorized by namespace, cluster, team, and application for detailed visibility into resource consumption

Categorizes risks such as overprovisioned, underprovisioned, and anomalous resources to help prioritize remediation

Integrates recommendations into CI/CD pipelines via Helm charts, Kustomize, and Terraform for automated policy-driven optimization

Combines with GitOps to enforce or review changes based on data-backed recommendations

Automates infrastructure changes through Terraform, Pulumi, and other IaC frameworks to continuously align supply with demand

Integrates with Kubernetes to automatically apply optimization recommendations

Detects sub-optimal scaling scenarios such as saturation and idle resources, analyzes HPA replication patterns, and recommends optimal container resource specs and HPA configurations

Optimizes auto-scaling groups and VM scale sets across instance families to improve efficiency

Selects optimal cloud instances based on workload patterns and predicted utilization

Removes guesswork when sizing new containers by analyzing workload patterns and providing predictive resource recommendations for quick deployment

Right-sizes and optimizes managed database instances for cost and performance

Recommends optimal GPU types based on workload requirements and predicted utilization

Monitors GPU utilization and performance in real-time, tracking aggregate node-level GPU and GPU memory utilization

Optimizes GPU allocation and scheduling for ML workloads, controlling GPU-to-memory ratios and selecting optimal GPU node types

Plans Multi-Instance GPU partitioning for efficient sharing and evaluates GPU slicing algorithms like time slicing and MPS

Determines optimal node types, CPU to memory ratios and scaling parameters based on learned behavior using ML pattern models of resource utilization

Predictively pre-scales nodes before traffic spikes using ML pattern models to generate pre-warming plans, preventing scheduling delays

Automatically adjusts pod replicas based on real-time demand, running in recommendation or fully automated mode to optimize resources and reduce node count

Uses ML models to analyze workload patterns and generate request and limit scaling plans for peak and off-peak loads, reducing throttling and OOM kills

Single Sign-On integration with identity providers for secure access

Command-line interface for interacting with the optimization agent

Model Context Protocol interface for agent interaction

Intelligently schedules statefulsets and long-running workloads using ML pattern models to drive predictive pod placement and optimize elasticity