Operations | Monitoring | ITSM | DevOps | Cloud

As excitement builds for KubeCon North America 2025 in Atlanta, Densify has released its latest innovation for Kubernetes and AI-driven infrastructure resource management: the Densify Model Context Protocol (MCP) Server. This new capability enables organizations to securely integrate Densify’s Kubex resource optimization intelligence directly into popular LLM-powered tools — including ChatGPT, Claude, Cursor, and Gemini CLI.

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.

We’re excited to announce the release of Densify 3.0, a major step forward in our mission to deliver precision-driven cloud optimization across Kubernetes and public cloud environments. This release brings to life a new user experience, powerful automation capabilities, richer data insights, and smarter APIs—all designed to simplify and scale your optimization journey.

To coincide with KubeCon Europe 2025, we’re excited to announce the latest release of Densify’s Kubernetes optimization engine, Kubex, which delivers full-stack resource management and seamless automation resource optimization at enterprise scale. This release delivers the advanced controls enterprises have been asking for—without sacrificing the intelligence and precision that sets Densify apart.

EKS Auto Mode is a huge step forward in managing your EKS clusters by automating complex tasks, enhancing cost efficiency, simplifying management, and ensuring resource optimization. These features make Kubernetes more accessible and manageable, particularly for organizations looking to leverage containerized environments without the overhead of extensive manual configuration and management.

Kubernetes (K8s) has transformed cloud-native infrastructure, enabling enterprises to build scalable, agile environments. Yet, alongside its benefits, Kubernetes introduces a significant blind spot —a rapidly growing part of cloud budgets that has become more and more difficult to optimize. Excess spend is common, and is caused by incorrectly specified container resources, stranding tremendous amounts of resources while at the same time introducing significant operational risk.