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.

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.