Operations | Monitoring | ITSM | DevOps | Cloud

From Visibility to Real Savings: Turning FinOps Insights into Measurable Cost Reduction

FinOps programs are maturing, and most organizations have better visibility into cloud spend than ever before. Dashboards are full of data. And yet costs keep climbing. The problem isn’t the data. It’s the gap between knowing where the waste is and actually eliminating it. In this joint session, Tangoe and Kubex come together to bridge that gap. Tangoe brings deep expertise in spend management and FinOps discipline, while Kubex delivers infrastructure-level optimization across cloud, Kubernetes, and the AI and GPU workloads that are rapidly becoming the next frontier of cost pressure.

10 Enterprise AI Infrastructure Voices Worth Following

Enterprise AI has crossed an inflection point. The model problem is largely covered. What remains unsolved is the operational impact: how to run AI inference and agentic processes continuously, reliably, and at a cost that doesn’t cancel out the value. Most enterprises are discovering this the hard way. GPU utilization dashboards show 80%. Actual compute efficiency is half that. Token demand is compounding at 200-500% annually as agents multiply every action into dozens of model calls.

Kubernetes Optimization Beyond Requests and Limits - Node Scaling Blockers

Many of us understand the concept of Kubernetes Requests and Limits, and that by reducing over-sized resource requests we can reduce waste in our clusters. And for GKE Autopilot and EKS Fargate clusters that is true. Because you’re being billed directly for the resources you’re requesting, driving down requests can result in instantaneous savings. However in most hosted Kubernetes environments you’re not actually being billed for requests.

Autonomous K8s Optimization Involves Both Compute and Storage Resources - Are You Doing Both?

One of the most powerful capabilities in K8s is the ability to autoscale resources to meet demands, scaling resources up during peak periods to ensure performance, and down again during lower periods to save money. In this joint session, Lucidity and Kubex walk through what end-to-end K8s optimization looks like when you address both layers together. We cover: Expect real examples, not slides full of theory. You’ll leave with a clear picture of where waste is hiding in your environment and a prioritized approach to addressing it.

15: Optimizing AI Workloads: Balancing Cost, Performance, and Scalability with Bijit Ghosh

In this episode, Andrew Hillier and Bijit Ghosh discuss the evolving landscape of AI, discussing the growing prominence of inference over training, hybrid cloud strategies, balancing cost with performance, and the orchestration of complex hardware environments. The conversation also touches on emerging concepts like AI factories, the challenges of sovereign cloud, and how enterprises are navigating data gravity and regulatory constraints. It's a deep dive into optimizing AI infrastructure, managing costs, and the disruptive changes that are transforming both technology and business outcomes.

Kubex Named a 2026 Leader by GigaOm

Industry analyst recognition means something different from an award. GigaOm does not hand out trophies. They evaluate products against a defined capability framework and tell the market where vendors actually stand. By that measure, Kubex has been named a Leader in two of GigaOm’s 2026 Radar Reports: Kubernetes Resource Management and Cloud Resource Optimization. In the Kubernetes report, we are positioned as an Outperformer. In Cloud Resource Optimization, a Fast Mover.

AI Factories Will Be Won on Efficiency: Why the Kubex + Rafay Partnership Matters

The early era for AI was defined by experimentation, standing up isolated environments, and finding the first practical use cases. Today, the conversation is different. Enterprises are no longer asking whether AI matters. They are asking how to scale it sustainably, securely, and economically. That shift is giving rise to the AI factory: a repeatable, governed, production-ready environment where data scientists, platform teams, and application teams can build, train, deploy, and operate AI at scale.

Kubernetes GPU Resource Optimization: Top 10 Solutions in 2026

TL;DR: Most Kubernetes clusters waste GPU compute through over-provisioned pod requests and suboptimal node selection. This guide covers 10 tools that fix this across four layers: resource lifecycle (Kubex, ScaleOps, Cast.ai), hardware partitioning (GPU Operator, MIG, time-slicing), inference serving (Triton, KServe), and observability (DCGM Exporter, NFD). For most teams, the biggest gains are at the resource lifecycle layer: no model changes required.