Virtual machines and containers operate at different layers of the infrastructure stack, yet many production environments rely on both. Virtual machines provide operating system isolation through a hypervisor, while containers package applications to run on a shared kernel and are typically orchestrated using an automated system that schedules, scales and manages container workloads across servers.
As organizations modernize their infrastructure, legacy systems, databases and containerised services often need to operate side by side. The architectural question is how these workload types can coexist without introducing operational complexity, resource contention or governance gaps.
Running containers and virtual machines together is technically straightforward when orchestration and resource management are designed deliberately. Stability depends on how networking, storage, security controls and lifecycle processes are structured across both workload types.
What are containers and virtual machines?
A virtual machine is created through a hypervisor, the software layer that abstracts physical hardware and allocates virtual CPU, memory and storage to each VM. Each VM runs its own guest operating system, along with virtualized CPU, memory and storage resources. This model provides strong isolation and remains widely used for legacy applications, commercial off-the-shelf software and stateful database workloads, which retain data between restarts and depend on persistent storage.
Containers use operating system level virtualization. Rather than including a full guest operating system, they package an application and its dependencies while sharing the host kernel. Containers are lightweight, portable and typically managed at scale using Kubernetes, which handles scheduling, scaling, service discovery and policy enforcement.
These differences influence how infrastructure behaves in production.
Virtual machines carry additional overhead because each instance runs a full OS. Patch management occurs at the guest operating system level, and resource density is typically lower. Containers start quickly and can scale horizontally, but require clearly defined resource limits and network policies to prevent instability in multi-tenant clusters.
Storage models also differ. VM-based workloads often rely on persistent virtual disks, while container platforms use persistent volumes and storage classes to support stateful services. Backup, recovery and data protection processes must reflect these differences.
Understanding these behaviors is essential before designing a shared environment.
How can containers and VMs run together?
Virtual machines and containers can coexist in three primary models.
Separate infrastructure
Some organizations operate a traditional virtualization platform for VMs and a separate Kubernetes cluster for containers. This provides clear boundaries between workload types and can simplify initial adoption of container technologies.
However, separate environments introduce duplicated monitoring stacks, networking configuration, security policy enforcement and lifecycle management processes. Governance standards, including defined access controls, patching policies and configuration management rules, must be applied consistently across both platforms to avoid configuration drift, where systems gradually diverge from approved settings, and the operational blind spots that follow.
Shared physical infrastructure
VMs and container clusters can run on the same underlying hardware while remaining logically distinct. A hypervisor layer may host virtual machines alongside worker nodes dedicated to container orchestration.
This improves hardware utilization but does not eliminate operational separation. Networking policies, storage backends and identity management systems may still be managed independently unless deliberate consolidation occurs.
Unified platform
A unified platform allows virtual machines and containers to operate within the same orchestration environment. Technologies such as Red Hat OpenShift enable traditional virtual machines to run alongside container workloads within a Kubernetes-based platform.
In this model, scheduling, network policies, role-based access control and monitoring operate through a shared control plane, meaning the central management layer that coordinates scheduling, policy and cluster operations. Persistent storage classes can be standardized, and visibility across both workload types is consolidated.
Consolidation reduces infrastructure silos, but resource quotas (defined limits on CPU and memory usage per workload), upgrade coordination and security standards must be designed carefully to prevent contention between VMs and containers.
Operational considerations when running both workload types
Coexistence of VMs and containers introduces practical infrastructure considerations.
Networking
Virtual machines typically rely on established virtual networking models, while container platforms use software defined networking and overlay networks, which create virtual network layers on top of physical infrastructure to manage container traffic dynamically. When both operate in the same environment, segmentation policies must account for dynamic container scaling while preserving stable connectivity for VM-based services.
Storage and persistence
Stateful services, which retain data between restarts, require persistent storage whether they run in a VM or a container. Backup and disaster recovery processes must align across both models.
VM snapshotting strategies and container volume backups should be integrated into a unified recovery plan to avoid inconsistent restore points. Storage performance characteristics must also be evaluated to prevent bottlenecks under mixed workload conditions.
Security controls
Virtual machines require operating system patching and vulnerability management at the guest level. Containers depend on image scanning, runtime policy enforcement and cluster-level role-based access control.
When both operate together, access controls, audit logging and patch management cycles must be coordinated to maintain consistent security posture and compliance coverage.
Lifecycle management
Kubernetes clusters require version upgrades and regular maintenance. Virtual machines require operating system updates and configuration management.
In a shared platform, lifecycle planning must consider compatibility between orchestration components and virtual machine workloads. Monitoring and observability tooling should provide visibility across both workload types to identify resource contention or performance degradation early.
Strategic use cases for running VMs and containers together
There are several practical reasons why organizations run virtual machines and containers together within the same environment.
Phased modernization
Legacy applications may remain on virtual machines while new services are deployed as containers. APIs and middleware can bridge these environments, enabling gradual modernization without destabilising core systems.
This approach spreads risk and investment over time.
Regulated environments
Finance, healthcare and other regulated sectors require strict change control, auditability and documented governance processes. Virtual machines may continue to host core regulated systems, while containerised services support analytics or customer-facing applications.
Hybrid infrastructure
Infrastructure often combines databases, middleware and front-end services. Vendor licensing or support constraints may require databases to remain on VMs, while stateless application layers benefit from container scalability.
Designing interoperability between these components requires coordinated network and storage planning.
Cost and capacity planning
Virtual machines and containers use infrastructure differently. A VM typically reserves a defined amount of CPU and memory, even if it is not fully using it. Containers are often scheduled more dynamically, sharing available resources across services.
In virtualized environments, it is common to allocate more virtual CPU or memory than the physical server provides, based on the assumption that not every workload will peak at the same time. When VMs and containers run together, these allocation assumptions need to be reviewed carefully to avoid performance bottlenecks.
A unified platform can improve visibility into how resources are actually consumed across both workload types. However, capacity planning and cost modelling should reflect how each workload behaves in practice, rather than treating them as identical from a utilization perspective.
Kubernetes alone vs Red Hat OpenShift
Running upstream Kubernetes, the standard open source distribution maintained by the Kubernetes project, provides core orchestration capabilities. However, integration of identity controls, policy management, security hardening and lifecycle automation remains the responsibility of the internal team.
An enterprise platform such as OpenShift builds on Kubernetes by integrating security policies, lifecycle tooling and operational guardrails into the distribution. It also supports running virtual machines within the same cluster, enabling consolidation without abandoning existing virtualized workloads.
For organizations without dedicated platform engineering teams, a managed OpenShift environment can provide consistent policy enforcement and lifecycle oversight while preserving infrastructure control.
Managed oversight for a unified OpenShift platform
Running virtual machines and containers within a unified OpenShift platform centralizes orchestration, networking policy and access control. It also centralizes operational responsibility for how that platform behaves day to day.
Cluster upgrades, security patching, storage configuration and performance tuning become interdependent. Changes to Kubernetes components can affect virtual machine workloads, and resource scheduling decisions influence both container density and VM performance. Without clear ownership, these dependencies can introduce instability and unplanned effort for internal teams.
Managed oversight introduces clear operational ownership of the platform. A managed partner takes responsibility for lifecycle management, elements of security, coordinated patching and capacity planning across both workload types. Monitoring, incident response and performance optimization are handled continuously, rather than reactively.
This allows internal teams to focus on application development and business priorities rather than the configuration and day-to-day running of the infrastructure.
What you should consider before consolidating
Before consolidating VMs and containers onto a unified platform, you should assess:
- The architecture and dependencies of each application
- Compliance and audit obligations
- Internal expertise, and whether a managed or unmanaged platform would be appropriate
- Existing monitoring and configuration management tooling
- Long-term infrastructure strategy
Consolidating VMs and containers onto a single platform can reduce duplicated tooling and centralize monitoring, access control and lifecycle management. However, this only works if resource limits, network segmentation and patching standards are applied consistently across both workload types.
In some environments, keeping platforms separate may be more appropriate. The right approach depends on your infrastructure needs, and it is important to strategically review your requirements before migrating workloads.
Evaluating your next steps
Running containers and virtual machines together is technically viable and often strategically sensible. The determining factor is how the environment is designed, governed and maintained over time.
A structured assessment of your current application estate can identify which workloads are suitable for containerisation, which should remain virtualized, and whether consolidation would improve operational clarity.
Our approach follows a four stage methodology: consult, design, migrate, manage. We begin by assessing workload dependencies and compliance requirements. We then design a unified platform with defined resource limits, segmentation policies and lifecycle standards. Migration is phased to reduce disruption, and ongoing management focuses on coordinated upgrades, security controls and capacity planning across both workload types.
This structured methodology allows you to modernize incrementally while preserving stability within existing systems.
If you are evaluating OpenShift hosting or planning phased infrastructure modernization, a consultative discussion can clarify your options and support your strategy.
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