How going dual-native means VM/container co-existence and sovereign security in the AI era

Seasoned techies recall with nostalgia a simpler time when the “single-stack” model of enterprise computing held as the default approach to solving interoperability headaches.

Back then IT decision-makers were prepared to forfeit some choice and flexibility in how they specified IT systems in return for the availability and manageability that came with a homogenous operating environment.

With the subsequent rise of multi-clouds, application-specific hardware, ever-changing business requirements, and the race for AI, the old “one-size-fits-all” playbook has been largely abandoned.

Most IT estates have embraced technological diversity, some by design, some through standardization, some by inertia.

Modern enterprise environments have become innately heterogeneous – and innately complex.

They run a diverse combo of system software, hardware devices, cloud services, legacy applications, et al .

The advance of AI has brought standard CPUs, specialized GPUs, fast networking fabrics, distributed data pipelines, and new deployment models that span private clouds, public clouds, edge – and ever-pressing ROI expectations.

But that doesn’t mean that companies’ existing applications and data simply disappear.

One trying but inevitable outcome of rapid AI deployment is the so-called “VM-container divide”.

This is where critical business applications tipped to transition to containers continue to run on virtual machines, because cloud-native services are taking precedence, nabbing container and Kubernetes availability.

So companies face the challenge of operationalizing AI, without being forced into complex migrations or creating new infrastructure silos.

Dealing with this technological schism poses acute challenges for IT management.

Many new AI deployments are going straight to containers, while existing data and applications – which the AI needs to access – continue to run on VMs.

Running separate stacks for VMs and containers creates a number of issues, such as duplicate capacity plans, separate support contracts, and fragmented governance.