Migrate First, Modernize Later: A Leadership Guide to Converging VMs and Containers to Run AI Workloads
The Tipping Point
Every so often the ground under enterprise IT moves. It’s moving now. Across industries, organizations are consolidating fragmented infrastructure onto a single, self-hosted platform capable of running both containers and virtual machines side by side. The motivation is simple: simplify operations, lower cost and reallocate resources & budget to AI initiatives. Kubernetes is emerging as the primary platform for many of these workloads.
For most IT leaders, the compute and storage portions of a VM migration are manageable. Storage arrays and hypervisor CPU/memory allocation translate fairly directly to Kubernetes equivalents. Networking is where migration plans stall. A VM’s network identity — its IP, its VLAN membership, its firewall rules — is wired into surrounding infrastructure, monitoring, compliance controls, and business processes that nobody wants to touch during a migration window.
Teams accustomed to NSX for this work find that native Kubernetes networking wasn’t built with VM administrators in mind, and the functionality gap becomes the reason migration projects get bigger or are stalled. If the networking problem is solved — if a VM can move to Kubernetes and keep its IP, its policy, and its security posture intact — then the rest of the platform consolidation stops Continue reading
