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Kubernetes is Not the New VM: Choosing the Right Abstraction for Modern Infrastructure - Stoil Stoilov

In this talk, we challenge the “Kubernetes replaces VMs” myth and instead present a practical, operator-focused framework for deciding when to use Kubernetes and when to rely on virtual machines—especially in CloudStack environments. We will explore the fundamental differences between VMs and containers, including isolation boundaries, lifecycle management, networking models, and operational complexity. Through real-world scenarios, we’ll highlight where Kubernetes excels (cloud-native workloads, horizontal scaling, stateless services) and where VMs remain the better choice (stateful systems, legacy applications, strong isolation requirements, predictable performance). Finally, we’ll discuss how CloudStack users can combine both paradigms effectively—using VMs as the foundation and Kubernetes as a workload layer—rather than treating them as competing technologies.

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1 Kubernetes is Not the New VM: Choosing the Right Abstraction for Modern Infrastructure @CloudStack European User Group, The Hague, NL Stoil Stoilov, DevOps Engineer, StorPool
2 “Ladies and gentlemen… according to the internet, infrastructure engineers are no longer needed.”
3 Infrastructure Evolution Is Not Replacement — It Is Layering Evolution Physical servers Virtualization Cloud / Orchestration Containers Kubernetes
4 From Bare Metal to Kubernetes Tracing the structural evolution of modern computing layers K8s Node VM Containers / Pods A Cube in 1996 Physical Host A Kube in 2026 Physical Host Virtualization Kubernetes Control Plane Physical Host Virtualization VM 1 VM 2 VM 3 K8s Node VM Containers / Pods
5 Bare Metal world Rigid infrastructure created significant operational bottlenecks  Slow Provisioning Weeks or months spent on hardware procurement, shipping, and manual racking before a single line of code could run.  Resource Silos Hardware was locked to specific apps. One server might sit at 5% utility while another crashed from over-utilization.  Manual Failover Hardware failure meant immediate downtime. Recovery required physical intervention or complex, manual clustering.  Hard Scaling Scaling required buying new physical assets. There was no "elasticity"—you either over-provisioned or ran out of capacity. Bare metal represents maximum control but carries the burden of maximum operational responsibility.
6 Virtualization Changed Infrastructure Forever Decoupling software from hardware created new paradigms of efficiency  Resource Pooling Aggregating physical resources to serve multiple VMs efficiently.  Isolation Sandboxing environments to ensure security and stability across workloads.  High Availability Continuous operation through automated failover and redundancy.  Scalability Rapidly adjusting resources to meet changing demand without physical limits. Virtualization remains the cornerstone of modern data center orchestration and cloud economics.
7 Cloud Changed Expectations From Manual Operations to API-Driven Agility  API-Driven Infrastructure Developers gained direct control over infrastructure through code, bypassing manual ticketing and long lead times.  Elasticity & Dynamic Load The shift to dynamic loads meant infrastructure had to scale instantly. CloudStack was a pioneer in this orchestration.  Self-Service Culture Empowered users to provision resources on-demand, drastically changing expectations for speed and reliability. CloudStack emerged as one of the first orchestration platforms to manage this new paradigm of scale and automation.
8 Containers and Kubernetes Containers solved packaging • application packaging • portability • dependency consistency • faster deployment Kubernetes solved orchestration • orchestration • scheduling • elasticity • self-healing • distributed operations Hardware Hypervisor VM Guest OS Application Hardware OS Kernel Container Runtime Containers Application
9 Operational Overhead Layering abstracts complexity for developers but increases overall operational burden. Software Development Focus Infrastructure Operations Focus Applications & Services Kubernetes / Orchestration Container Runtime & OS Virtualization / Hypervisor Bare Metal / Hardware Cleaner Abstraction Developers only interact with high-level APIs. Underlying complexity is hidden. Operational Overhead Infrastructure engineers must manage interconnects, security, and updates for 5+ layers. Each layer adds management overhead
10 Mapping the Infrastructure Landscape Scalability Complexity Q1: Legacy Complexity High management overhead with limited scaling. Traditional bare-metal silos that are difficult to automate and slow to update. Q2: Cloud Native Powerhouse Maximum scalability but extreme operational complexity. Multi-layer stacks requiring expert orchestration and constant patching. Q3: Simplified Operations Lean abstractions for stable workloads. Focus on developer productivity where massive scale isn't the primary driver. Q4: Strategic Efficiency The "Sweet Spot": High scalability with abstracted overhead. Developers interact with APIs while infra is managed as a service.
11 Modern Infrastructure Is Hybrid by Design Evolution through layering, not replacement Hybrid Coexistence Most production environments combine multiple models. Kubernetes did not replace virtualization; it became another layer on top. Unified Foundation Storage and networking layers are shared underneath regardless of whether the workload is a VM or a Container. Workload-Centric Designing around behavior: AI on bare metal, DBs on VMs, and APIs in Kubernetes clusters running on VMs. Kubernetes / Orchestration Layer Virtualization / Hypervisor Layer Shared Infrastructure: Distributed Storage & SDN  
12 The Real Question Is Not “VMs or Kubernetes?” What does the workload need?
13 Hyperconverged (HCI) Characteristics ● Compute and storage scale together ● Unified lifecycle management & simplicity ● Easier deployment; fewer layers Good Fit For ● General-purpose clouds & mixed workloads ● Smaller operational teams & Edge Tradeoff Less independent scaling flexibility Disaggregated Characteristics ● Storage and compute scale independently ● Workload-specific tuning & optimization ● Higher flexibility at scale Good Fit For ● Specialized high-performance workloads ● Very large cloud platforms Tradeoff Higher complexity; more networking/layers The Infrastructure Trade Off Principle Every architecture optimizes for one factor (e.g., simplicity) while sacrificing another (e.g., flexibility). Architecture is a balancing act between performance, flexibility, scalability, and overhead. HCI vs Disaggregated Infrastructure Simplicity vs specialization
14 Kubernetes vs Virtual Machines Choosing the right model for the workload Virtual Machines Strengths ● Strong isolation & mature tooling ● Operational simplicity ● Predictable performance ● Easier troubleshooting Common Fits ● Enterprise apps & Databases ● Monolithic systems ● Stable workloads ● Smaller operational teams Kubernetes Strengths ● Orchestration & Elasticity ● Horizontal scaling ● Rapid deployments & Self-healing ● Distributed applications Common Fits ● Microservices & APIs ● AI inference platforms ● CI/CD-heavy environments "Does this workload benefit from orchestration and elasticity?"
15 A Workload That Naturally Fits Kubernetes Characteristics ● Horizontally scalable ● Bursty traffic ● Distributed by nature Why Kubernetes Fits Well ● Rapid scaling ● Orchestration ● Fault isolation Infrastructure Priorities ● East-west traffic ● Observability ● Elasticity Kubernetes works best when the app is distributed by nature. Cloud-native by design
16 A Workload That May Not Naturally Fit Kubernetes Characteristics ● Vertically scaled workers ● Long-running tasks ● Low user concurrency Operational Reality ● Queue-based scheduling ● Distributed over time ● Vertically intensive tasks Infrastructure Priorities ● Large VMs or Bare Metal ● Predictable performance ● Vertical scalability Horizontal task distribution ≠ horizontal scaling
17 AI Workloads Bring Back Hardware Awareness Modern AI ● Massive parallel processing ● Large datasets ● Resource hungry Implications ● Locality matters ● Huge east-west traffic ● Latency Architectural ● Storage requirements ● Network requirements ● Nodes topology AI brought back physical reality AI workloads are reminding us that infrastructure abstractions never removed the importance of hardware behavior.
18 Kubernetes Is More Capable Than Many Assume Kubernetes CAN support: ● stateful applications ● databases ● distributed storage ● high availability ● cluster-aware systems ● object storage ● operators ● persistent volumes Cloud-Native Characteristics ● horizontally scalable ● failure-tolerant ● orchestration-aware ● externally managed state ● distributed by design ● API/service-oriented Kubernetes is more than a container runner; it's a distributed systems platform.
19 The Philosophical Shift Modern infrastructure is hybrid by necessity The future is not choosing one abstraction. The future is understanding which abstraction fits the workload best. “hybrid application aware infrastructure” BARE METAL VMs Containers Kubernetes
20 Orchestrating Kubernetes-Ready Clouds CloudStack  Multi-tenancy  CSI Integrations Hybrid Coexistence  Physical Infrastructure Hypervisor Layer Apache CloudStack Orchestration Virtual Machines Kubernetes Clusters Containers / Applications
21 HCI node Bare metal Unified Infrastructure Requires Unified Storage kubelet Container Engine Pod Pod Pod Pod Pod Pod CSI Hypervisor VM VM VM cloudstackagent VM VM VM Worker node (VM) v kubelet CSI Container Engine Pod Pod Pod Pod Pod Pod Pods Container Storage Interface Unified Storage Orchestration CloudStack
22 Key takeaways Decoupling software from hardware created new paradigms of efficiency  Layering Infrastructure didn’t evolve through replacement  Complexity Kubernetes is extremely capable, but comes at a cost  Workload Behavior matters more than technology trends  Trade Offs There are no universal answers Virtualization remains the cornerstone of modern data center orchestration and cloud economics.
23 Good infrastructure follows workload behavior. Great infrastructure understands operational context.
24 Questions? “Or has AI already answered them?” linkedin.com/in/stoil-stoilov/