Virtualized Containers vs. Bare Metal: The Winner Is…

The blanket statement that bare metal is superior to containers in VMs for running containerized infrastructure, such as Kubernetes, no longer holds true. Each has pros and cons, so the right choice depends heavily on specific workload requirements and operational context.

Bare metal was long touted as the obvious choice for organizations seeking both the best compute performance and even superior security when hosting containers compared to VMs. But this disparity in performance has slowly eroded. For security, it is now hard to make the case for bare metal’s benefits over those of VMs, except for very niche use cases. 

Today, it can be validated that a VM infrastructure will offer a long list of superior operational capabilities and almost on-par performance for managing containers in cloud-native environments as well as in the private cloud. The actual compute and performance gap continues to narrow and, in many cases, has almost closed completely. VM platform providers can also offer abstraction layers for private clouds and edge deployments that combine the advantages of both VMs and containers in a unified configuration. That said, the current state of adoption and applicability shows that containers running on VMs are the better choice for the vast majority of use cases.

The Consensus

The analysts and HyperScalers consensus across ReveCom, 451 Research, and Gartner advocates for containers running on a VM layer in at least many, if not the vast majority of, use cases. That choice favors operational efficiency over the very slight and usually negligible performance advantage measured largely by latency metrics of containerized infrastructure running on bare metal. While bare metal may still provide marginal latency benefits in specific scenarios, the performance gap has become negligible for many, if not the majority, of workloads. In fact, industry benchmarks like MLPerf have demonstrated that VM-based container environments can match — or even outperform — bare metal in latency-sensitive tasks such as machine learning inference.

Beyond performance, VMs continue to offer a wider range of operational control, strong isolation (think "hard" resource limits), and better security than bare metal. Whether for managing numerous workloads at scale or maintaining stability in multi-tenant setups, VMs have the edge.

While the performance gap has largely decreased to become negligible according to analyst consensus and hyperscalers, bare metal — just by pure definition or configuration — does still offer a very small performance advantage depending on the application. After all, the containerized infrastructure is running directly on the server. The VMs provide an additional layer between the application and the server. So on that very rudimentary level, just by definition alone, the performance is superior when running on containers, but reality is even bare metal containers run on underlying Linux. 

Meanwhile, the advantages of VMs extend to traditional and more recent workloads, such as for Kubernetes infrastructure. VMs continue to offer superior elasticity for scaling, rollback, snapshot capabilities, and provisioning in both cases. These are reasons why an organization operating a hypervisor at scale, extending across numerous clusters, would likely be unenthusiastic about manually managing individual bare-metal server operating systems and infrastructure. The hypervisor can offer control at scale over a vast number of Kubernetes deployments on VMs, which maintain consistently superior operational control, as described above.

Hyperscalers offer containers primarily on top of VMs instead of bare metal. This approach provides stronger security and isolation, evidenced by better isolation between different workloads as each VM has its own kernel. An attacker is still confined within the VM if there is a container escape vulnerability. Additionally, this enables efficient resource management, highlighted by the ability to enforce hard resource limits (CPU, memory, disk) at the VM level. Guaranteed performance characteristics are achieved due to dedicated vCPU, memory, and I/O queues for VMs. These aspects directly support the operational control, security, and isolation wins of virtualized containers.

In multi-tenant environments, running containers directly on bare metal can lead to unpredictable performance and stability issues. A single misbehaving or resource-hungry container can impact other workloads sharing the same physical resources. This is particularly problematic in industries where reliability is critical. For example, in financial services, a resource spike in a risk analysis job could disrupt real-time transaction processing. In healthcare, intensive medical imaging analysis could slow down patient record systems. Online education platforms might see grading automation or video transcoding containers affect the entire student experience during peak times. Similarly, SaaS providers risk one customer’s workload impacting others, and media companies could see a single streaming job degrade the quality of other live broadcasts.

VMs address these challenges by providing strong isolation and hard resource boundaries for each workload. The hypervisor ensures that each VM receives dedicated CPU or GPU, memory, and I/O resources, preventing one workload from interfering with another. This results in more predictable performance, greater stability, and improved security.

Come Together

The advantages of VMs for containerized infrastructure are certainly an easier sell than in the past.

When determining whether to opt for, or when it's appropriate to offer, VMs, bare metal, or a hybrid alternative for running containerized workloads, especially at scale, careful consideration is needed.

Elasticity, isolation, and other operational advantages that VMs offer can arguably become buzzwords when taken out of context. But a proper assessment will reveal how, again, the number of workloads or microservices — or workloads per node — makes the case for bare metal especially hard. When a single microservice can cause a container to malfunction and the entire server’s kernel to crash. Conversely, VMs support one of the key functionalities that Kubernetes is meant to do: offering the ability to scale up and down resources as needed, i.e., elasticity and isolation to the extent that separate microservices’ damage radius remain limited to the container.

On a more rudimentary level, in reference to elasticity, an organization running Kubernetes and containerized workloads in a private cloud data center — or even in a traditional data center — will face difficulties in determining its future needs. Because of that, redundancy and over-provisioning must be added to bare metal servers in anticipation of future needs. Those scenarios include when saturation is reached or when business operations can no longer function once full capacity has been achieved. Therefore, the required capacity for bare metal — and the associated cost — will often outstrip the more elastic options that VMs offer, whether used on private clouds or not.

The elasticity that VMs provide for running containers at scale aligns with the ability to manage and extend Kubernetes in a unique way. With a platform that allows VMs to be managed in a flexible and elastic manner, Custom Resource Definitions (CRDs) can be applied by separate teams for various K8s versions on the same host as their needs fluctuate and evolve more dynamically. Bare metal is limited to one K8s version per server, and VMs eliminate that challenge. 

This flexibility extends across environments, whether the actual data or applications are housed on a single server or across Kubernetes environments in a multi-cluster structure. Such CRD flexibility, while a core Kubernetes feature, can pose significant challenges when attempting to achieve the same level of isolation, elastic management, and support of multiple K8s versions on a bare metal server as is readily available with virtualized infrastructure.

Again, whether it makes sense to run containers bare metal or to run containers on the abstraction of VM comes down, of course, to the individual needs of the organization. Technology leaders and DevOps teams should assess the environment accordingly and determine what makes the most sense, which likely still will be VMs for the vast majority of their containerized workloads.