The current virtualization landscape has reached a critical crossroads. Market disruptions have forced organizations to look beyond short-term shocks and confront broader agility and resilience issues and rethink their entire virtualization approach. According to the Voice of Kubernetes report, 74% of organizations are planning to migrate their virtual machines (VMs) to Kubernetes.

Part of this change is that IT leaders are discovering their legacy stacks are fundamentally ill-equipped for the modern era. Compounding the issue is a pervasive context gap, a reliance on data silos that prevent organizations from building a unified understanding of their data, effectively turning both the data and the underlying infrastructure into a bottleneck.

Deciding how data is stored, protected and moved across any new platform is a data architecture decision - and many buyers are mistaking it for a compute one. Getting it wrong can mean they end up with the same silos, complexity and fragmentation. Organizations need to ensure that new architecture supports business modernization and new Artificial Intelligence (AI) initiatives, instead of adding another silo.

A modern virtualization approach means running virtual machines and data in containers side by side with Kubernetes as a single control layer. It bridges the gap between legacy applications and modern cloud-native architecture. Instead of maintaining two separate, siloed infrastructures with different tooling, teams can use Kubernetes as a single control plane to orchestrate both VMs and containers. This simplifies operations, and improves reliability and scalability. However, it only works if the data plane is shared too - otherwise an organization is replacing one silo with two.

The move to modern virtualization is already underway - Everpure customer CSX, a freight rail organization, moved around 80% of its 400+ applications onto Kubernetes, including Tier 0 systems where an outage can stop trains. CSX was driven by a desire for simplification and flexibility and didn't want to retrain staff or stand up a whole new platform. Instead it chose a modern virtualization path - one operating model across virtual machines and containers as a practical transition.

Every workload needs three things - storage to hold the data, compute to process it, and networking to transmit it. Previously, virtualization meant that storage came as part of the stack. In containerized environments, it's often abstracted away or managed separately, getting overlooked.

However, storage that isn't properly integrated adds risk and complexity. Storage is, in fact, a critical element in modern virtualization, as a storage failure can cause an entire virtualization stack to fail. Buyers really need to think this through from a data management perspective.

This is why 56% of organizations say that high availability, disaster recovery, and backup are the most critical capabilities to solve in a new virtualization platform. Knowing what data an organization has, where it is, and how to reduce silos - getting that right means virtualization stops being an 'infrastructure project' and becomes a way to modernize how a business runs.

With AI adoption increasing rapidly, and the volume of data that comes with it, getting data management and storage right is critical. AI workloads need high performance and organizations running on legacy infrastructure are struggling to move projects forward. In the Data on Kubernetes Community survey, storage was the most commonly cited cost concern among organizations running AI on Kubernetes.

Containers are an ideal place to run data-heavy AI projects and experiment. But choosing where to run AI workloads is a data architecture decision - it requires balancing cost, risk, skills, data strategy, and long-term architectural flexibility.

Done well, modern virtualization turns infrastructure from a bottleneck into a pool of resources that are fully utilized. Data scientists and AI leaders are no longer constrained and can focus on training and inference, and saving time and money.

Moving to modern virtualization doesn't have to mean adding more tools. Done properly, it means streamlining complexity and consolidating. One approach, less fragmentation, with the performance, mobility and resilience that modern organizations need for critical workloads.

Everpure customers already run hundreds of thousands of VM volumes on Kubernetes across multiple locations.

Buyers are no longer thinking about whether to make the move, they're asking 'how?' Focus just on the compute side of the equation and organizations will end up with another silo. Make the compute and data architecture decision together - rather than separately - and modern virtualization becomes the foundation for modernization and AI.