Operations | Monitoring | ITSM | DevOps | Cloud

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

<span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_start"></span> Andrew Hillier sits down with Dan Ciruli, who leads the cloud native product management team at Nutanix.

As organizations scale their Kubernetes infrastructure, one truth becomes clear: no single team can optimize it alone. Efficiency, resilience, and cost-effectiveness in Kubernetes environments depend on the collective effort of multiple personas, each bringing essential knowledge and responsibility. But it’s not just about division of labor. It’s about active collaboration across roles to unlock the full potential of the platform.

Kubernetes gives you the amazing power to deploy and manage containerized applications. But this power comes with a trade-off. Instead of letting you focus only on writing code and delivering features, Kubernetes also shifts the burden of resource optimization i.e., cost control, performance, and scalability, directly onto your shoulders. The answer to these challenges is automation. Automated optimization takes the guesswork out of resource allocation.

We’re thrilled to announce our partnership with Nutanix, integrating Densify’s AI-powered Kubernetes optimization solution, Kubex, with the Nutanix Kubernetes Platform (NKP). This collaboration brings together two leading technologies to radically improve how enterprise Kubernetes environments are managed—through AI-driven insights and end-to-end automation of resource optimization.