MCP: The Protocol That Taught AI to Use Tools
Introduction
Generations of infrastructures have been shaped by a simple question: ‘How do you run software reliably and at scale on shared hardware?’ Virtual machines were among the first answers to this question, allowing teams to carve out isolated environments on physical servers and run multiple operating systems side by side. Containers arrived later, promising something lighter, faster, and more flexible without abandoning isolation altogether.
Today, both coexist inside modern platforms. Enterprises run legacy workloads on VMs, cloud-native services in containers, and increasingly complex systems that combine the two. Yet the distinction between them is often reduced to oversimplified comparisons: “VMs are heavy, containers are light,” or “containers replaced VMs.” In practice, the relationship is more nuanced.
Understanding how virtual machines and containers actually work and why both still matter is essential for anyone building modern infrastructure, especially as AI, Kubernetes, and distributed systems push workloads to scale in new and unpredictable ways. This matters even more when infrastructure is supporting enterprise AI workloads.
What Are Virtual Machines
A virtual machine is an abstraction of a physical computer. It behaves like a full system, complete with its own operating system, kernel, system libraries, and applications. This illusion is created by a hypervisor, a layer of software that sits between the hardware and the operating system.
The hypervisor’s job is to multiplex physical resources CPU, memory, storage, and networking across multiple VMs – while maintaining strong isolation. Each VM believes it owns the machine it’s running on, even though it’s sharing hardware with others.
This model brought several advantages that shaped enterprise infrastructure for years.
VMs allowed multiple teams to share expensive hardware without interfering with each other. They enabled running different operating systems on the same server. They also introduced a clean security boundary: if one VM crashed or was compromised, the others remained unaffected.
However, this isolation comes at a cost. Every VM carries an entire operating system, consuming memory and storage before a single application starts. Boot times are measured in minutes, not seconds. Resource utilization tends to be conservative because resizing a VM is rarely trivial.
Virtual machines prioritize stability and isolation over agility a trade-off that made sense when workloads were static and release cycles were slow.
The Limits of the VM-Centric Model
As software delivery accelerated, the friction inherent in VMs became more visible. Developers wanted faster feedback loops. Operations teams wanted better utilization. Businesses wanted to scale applications dynamically rather than plan capacity months in advance.
While VMs could scale, they didn’t scale gracefully. Spinning up a new VM meant provisioning storage, booting an OS, configuring networking, and deploying software all before the application could serve traffic. This becomes painful in modern AI tech stacks as well.
The mismatch became clearer as microservices emerged. Running dozens or hundreds of small services inside full operating systems was inefficient. The overhead of VMs began to dominate the cost and complexity of infrastructure. This set the stage for containers.
Containers: A Different Kind of Abstraction
Containers solve the same problem as VMs isolating workloads on shared hardware but approach it from a different angle. This OS-level approach shows up often in AI neocloud discussions.
A container packages an application and its dependencies but shares the host’s kernel. Isolation is enforced through kernel features like namespaces and cgroups (control groups), which limit what a process can see and how much it can consume.
The result is lighter weight isolation. Containers start in seconds or milliseconds. Hundreds can run on a single host. Resource usage is more granular and dynamic. Applications can be packed densely without paying the cost of redundant operating systems.
This model aligns naturally with modern software practices. Applications are deployed frequently. Services scale up and down based on demand. Failures are expected and automatically recovered from. Containers make infrastructure behave more like software, but this flexibility comes with new responsibilities.
Containers Don’t Eliminate Complexity They Move It
While containers simplify packaging and execution, they do not eliminate complexity. They shift it upward.
Because containers share a kernel, isolation boundaries are thinner than those of VMs. Security now depends on kernel hardening, runtime controls, and correct configuration. Networking becomes more dynamic as containers are constantly created and destroyed. Storage must handle ephemeral lifecycles.
Most importantly, containers assume orchestration. Running a few containers manually is easy. Running thousands reliably is not. Scheduling, health checks, restarts, networking, service discovery, and scaling all require coordination.
This is why containers and Kubernetes are inseparable in practice. Containers provide the unit of execution; Kubernetes provides the system that makes them usable at scale. At that point, teams also need clarity on how model inference behaves in production.
VMs and Containers Are Not Competitors
A common misconception is that containers replaced virtual machines. In reality, containers in most cases run on top of VMs in most production environments.
Cloud providers, enterprises, and platforms typically deploy Kubernetes clusters on VM-based infrastructure. The same layered approach is common in enterprise GPU clouds. Containers provide speed, density, and operational flexibility inside those boundaries.
This layered model combines the strengths of both approaches. VMs isolate clusters and workloads from each other. Containers enable rapid application delivery within a cluster. The boundary between them is intentional.
For regulated industries, this model is especially important. VMs help satisfy compliance requirements around isolation and auditability. Containers enable modern development practices without compromising control.
Rather than choosing one over the other, modern infrastructure stacks deliberately use both.
Choosing Between VMs and Containers
The decision is rarely binary. It depends on workload characteristics.
Virtual machines remain well suited for:
- Legacy applications that assume full OS control
- Stateful workloads that change infrequently
- Strong isolation requirements
- Mixed operating system environments
Containers excel when:
- Applications are modular and stateless
- Scale and elasticity matter
- Deployment frequency is high
- Infrastructure must respond quickly to change
AI workloads illustrate this distinction clearly. Training jobs often benefit from containerization for portability and reproducibility, while running inside VM-backed clusters for isolation and hardware control. Inference services rely on containers for fast scaling but depend on VM-level guarantees for stability and security.
Understanding this balance is what separates ad hoc infrastructure from deliberate platform design.
Why This Matters for Modern Platforms
As systems grow more complex, infrastructure decisions compound. The cost of choosing the wrong abstraction shows up later as operational friction, performance bottlenecks, or security gaps.
Teams that treat VMs and containers as interchangeable primitives often struggle. They either over provision resources or overcomplicate deployments. Teams that understand where each abstraction fits can design systems that scale cleanly.
This is especially true for distributed systems, data platforms, and AI pipelines, where workloads are heterogeneous and resource intensive.
Neysa Velocis: Built for Stability and Speed
At Neysa, the goal is not to force teams into a single abstraction, but to let workloads run where they make the most sense.
Velocis environments are built on a VM-backed foundation that provides isolation, compliance, and predictable performance. On top of this, Kubernetes and container-native workflows enable fast iteration, dense scheduling, and elastic scaling for modern applications.
This layered approach allows AI workloads to move seamlessly from experimentation to production. Training jobs can run in containers with controlled resource boundaries. Inference services can scale dynamically without sacrificing isolation. Teams don’t have to choose between stability and speed the platform supports both.
Instead of treating VMs and containers as competing philosophies, Neysa treats them as complementary building blocks in a single, coherent system.
Conclusion
Virtual machines and containers represent two different approaches to the same fundamental challenge: running software reliably on shared infrastructure. VMs emphasize isolation and stability. Containers prioritize agility and efficiency.
Modern platforms succeed by combining them, not by choosing sides.
As infrastructure continues to evolve driven by Kubernetes, AI workloads, and increasingly dynamic systems this layered model becomes even more important. The abstractions we choose shape how fast we can build, how safely we can scale, and how confidently we can operate.
In the end, good infrastructure is not about adopting the newest primitive. It is about understanding the trade-offs and designing systems that let each layer do what it does best.
