Containers vs VMs: Making the Right Choice for Your Infrastructure

Containers and virtual machines (VMs) have become more popular due to the shift toward cloud-provided infrastructure. Each method of providing cloud-based services has its benefits and drawbacks — there’s orchestration complexity to consider, but also patch management and isolation boundaries. To be sure, tech teams will have to compare their architecture choices in the context of adapting container security practices or VM security to both environments. What’s that look like? We’re diving into the differences.

Let’s Review: Understanding Modern Virtualization and Containerization

A virtual machine (VM) is a software-based environment that emulates a physical computer so multiple operating systems can run independently on the same hardware. VMs virtualize hardware.

Virtualization originated in the 1960s when IBM pioneered the concept of running multiple independent computing environments on a single mainframe. That allowed one physical machine to serve multiple users simultaneously, marking the first use of a hypervisor — a technology that creates and manages virtual machines by abstracting hardware resources.

By the 1970s, the industry formally categorized hypervisors into two types:

• Type 1 Hypervisors (Bare Metal): Run directly on the physical hardware, providing high performance and security. 
• Type 2 Hypervisors (Hosted): Run on top of an existing operating system (OS), making them more suitable for desktop virtualization. 

By contrast, containers virtualize at the level of the operating system and share the host OS kernel. They isolate applications and dependencies, making them lightweight, faster to start, and more efficient than VMs.

Containers have their own filesystem, libraries, and dependencies, but all rely on the same underlying OS kernel. That means that applications in containers are isolated but don’t need their own OS, allowing them to be started, stopped, or moved quickly. Resource use is minimal because containers avoid duplicate OS overhead.

How popular is containerization? In 2023, 59% of organizations said containers were used for most or all applications and business segments, and 39% of development was fully cloud-native.

Containers have become ubiquitous, and it’s no wonder: they offer key advantages for teams. Containers are: 

• More efficient than VMs: They don’t replicate the OS layer. Multiple containers can run on a single OS instance.
• Portable: Containers package the application and dependencies together
• Flexible: They’re ideal for deploying microservices or cloud-native applications, while VMs run more diverse workloads requiring different OSes.

How Do Containers and VMs Really Differ?

Containers and virtual machines (VMs) share similarities as virtualization technologies, but their fundamental architectures and operational models are distinct. Understanding exactly how helps determine the best model for specific workloads or security needs.

Area	Containers	Virtual Machines
Technology architecture	Lightweight, share the host OS kernel, and package only libraries and dependencies required.	Fully replicate hardware and include a complete OS, creating a fully isolated environment.
Resource management	Managed by container orchestration platforms like Kubernetes.	Managed by a hypervisor that allocates resources for each VM.
Security boundaries	Isolated at the application level but share the host OS kernel, increasing risk of kernel exploits.	Fully isolated from each other and the hypervisor, with stronger boundaries.
Deployment model	Designed for cloud-native environments and managed at scale using orchestration tools.	Deployed on hosted servers or in the cloud; ideal for running diverse OS environments.
Operational overhead	Minimal, tied to container count and CPU/memory usage.	Higher, due to OS overhead and hypervisor resource requirements.

When Should You Choose Containers?

Containers are a good choice for teams that want to:

• Develop microservices-based applications: Containers are ideal for deploying and managing each microservice and the network that connects them. 
• Migrate legacy applications to the cloud: Containers allow for the replication of the original running environment of an application, making it easier to migrate to the cloud. 
• Support continuous integration and delivery (CI/CD): Containers can support automated processes for building, testing, and deploying applications. 
• Ensure portability: Containers can be built locally, deployed to the cloud, and run anywhere. 
• Scale up or down: Containers can be easily scaled up or down to meet demand. 
• Provide complete control: Containers give developers more control over the application environment, including the languages and libraries used. 
• Enhance security: Containers can be more secure than other options. 

When Do Virtual Machines Make More Sense?

Virtual machines can be more logical when teams have the following needs:

• Legacy Applications: VMs are better for running older applications that require a specific operating system or configuration, which containers may not support effectively.
• Diverse OS Requirements: If workloads need different operating systems (e.g., Windows and Linux), VMs are ideal as each VM runs its own OS.
• Strong Isolation Needs: VMs provide strong security boundaries through hardware-level isolation, making them preferable for high-security environments or multi-tenant workloads.
• Traditional IT Infrastructure: In environments where traditional IT practices dominate, VMs integrate more seamlessly with established workflows and tools.
• Lower Agility Demands: For workloads that don’t require rapid scaling or frequent updates, VMs offer sufficient stability without the added complexity of orchestration platforms.
• Resource Predictability: When predictable and fixed resource allocation is necessary, VMs provide dedicated resources without the dynamic sharing common in containers.
• Compliance and Regulation: For industries with strict compliance requirements, VMs often meet established standards more easily than containerized environments.
• Persistent Workloads: Long-running, stateful applications benefit from the persistence and stability of VMs compared to the ephemeral nature of containers.
• Reduced Complexity: Organizations without the expertise to manage container orchestration platforms (e.g., Kubernetes) may find VMs simpler to deploy and manage.
• Low Latency to Virtualization Transition: VMs can run on existing virtualized infrastructure without significant adjustments, so they’re faster to implement in many environments.

Why Not Use Both VMs and Containers?

For organizations with varied workloads, using both VMs and containers is sensible. It’s common to use VMs to run monolithic applications and deploy containers for microservices or apps. 

Keep in mind that it’s also possible to use the two together in a hybrid model, deploying containers inside VMs to combine the portability and efficiency of containers with the isolation of VMs. It’s also possible to forego the either-or altogether and choose alternative options like running bare-metal servers without virtualization, serverless computing, unikernels, or platform-as-a-service options.

While other options exist, containers and VMs remain dominant because together, they can support both legacy and cloud-native workloads and manage resource allocation for both stable and dynamic applications. That flexibility has made them indispensable and shifted the underlying debate about containers vs VMs to a more nuanced one: how to use the two options to complement one another. Containers provide agility and portability for cloud-native applications, while VMs offer isolation and compatibility for legacy or multi-OS environments. That interplay means organizations must adjust their own use of each to balance innovation and stability.

Security Considerations: Containers vs VMs

One of the great balancing acts of choosing containerization vs VMs for any given workload is handling security considerations, given the distinct architectures of VMs vs containers. Ultimately, containers will need runtime monitoring, image validation, and orchestration-layer protections, like configuration management and policy enforcement, to address their shared-kernel architecture and ephemeral nature. 

VMs, while offering stronger isolation and simpler security workflows, require diligent patching and hypervisor hardening. Understanding these trade-offs ensures teams can choose the right virtualization model and implement appropriate security measures.

Here are the distinctions in security strategy for each approach:

1. Isolation Boundaries

Containers share the host OS kernel, making them lightweight but more susceptible to kernel-level exploits. A compromised container could potentially lead to a breach of the host system or other containers. 

Virtual machines provide stronger isolation since each runs its own OS and interacts with the hypervisor. A VM compromise is less likely to impact others unless the hypervisor itself is exploited.

2. Ephemerality and Monitoring Challenges

Containers are ephemeral, spinning up and shutting down rapidly. This dynamic nature can make traditional security tools ineffective for tracking and analyzing runtime behavior. As with isolation boundaries, continuous runtime monitoring is a must.

VMs are persistent, allowing easier integration with existing logging and monitoring tools. Yet VMs are long-lived, making them susceptible to evolving threats and unnoticed compromises. Runtime monitoring is also key to ensuring VMs, with their larger attack surfaces and position vis à vis high-value systems, are protected.

3. Supply Chain Risks

Heavy reliance on publicly sourced images increases the risk of introducing vulnerabilities or malicious code into the environment. Regular image scanning and strict provenance controls are critical. Use SBOM explorers to manage the risk of supply chain attacks.

VMs typically use internally managed OS templates, reducing supply chain risks but requiring consistent patch management. Outdated templates can undermine any security advantages. While cloud security posture management (CSPM) tools help identify unpatched systems in cloud environments, runtime-powered vulnerability management adds deeper context, enabling teams to focus on what matters most

4. Patch Management

For containers, updates require rebuilding and redeploying container images, shifting the patching process to the DevOps pipeline. Automating image updates in the CI/CD pipeline is key to improvement.

Traditional patching methods, such as direct OS updates, apply to VMs, though they may require downtime or reboots. Tools that offer runtime vulnerability insights spot critical vulnerabilities in both VMs and containers; it’s the implications of patching and the remediation process that differ, as VMs must focus on operational workflows.

5. Orchestration Complexity

Managing containers at scale often involves platforms like Kubernetes, which introduce their own attack surfaces (e.g., misconfigured RBAC or exposed APIs). Containers need both configuration management and orchestration-level protection, like pod-level isolation and monitoring for Kubernetes logs.

Orchestration is less complex in VMs but lacks the agility of containerized platforms. VMs rely on strong patch management, runtime monitoring, and access controls to address risks at the hypervisor, OS, network, and data levels.

6. Resource Sharing Risks

For containers, the shared OS kernel increases the risk of resource-based attacks, including side-channel attacks and container escapes. Containers need resource governance and runtime controls, such as cgroup enforcement and syscall filtering, to limit resource abuse and restrict unauthorized system calls.

For VMs, dedicated hardware emulation isolates resources, reducing cross-workload risks. This strong isolation makes VMs less susceptible to resource contention, but proper hypervisor configuration and monitoring will remain key to preventing lateral movement or resource exhaustion.

Overall, containers demand robust runtime monitoring, image validation, and orchestration-layer protections to mitigate risks like kernel exploits and supply chain vulnerabilities. VMs, on the other hand, provide stronger isolation but require diligent patch management and hypervisor hardening to ensure security over their longer lifecycle and broader attack surface.

While comprehensive security tools like runtime-powered CNAPPs provide solutions for runtime monitoring, vulnerability management, and orchestration-level protections across containers and VMs, they won’t explicitly address hypervisor-specific vulnerabilities. Organizations that rely heavily on hypervisors for VM management should complement their toolkits with solutions for hypervisor hardening.

Container Security with Upwind

The choice to use containers or VMs shouldn’t matter for security when organizations deploy a layered security approach, ensuring that each virtualization model is protected at every level, from orchestration and runtime to the underlying infrastructure.

Upwind enhances security teams’ ability to protect containerized and microservices architectures with industry-leading container and Kubernetes security capabilities:  

• Supporting container and Kubernetes security regardless of cloud provider. Whether Amazon EKS & ECS, Google GKE, Microsoft AKS, or OpenShift, Upwind secures all containers across the environment. 
• Enhancing visibility with DevOps context: With a real-time, graph-based network and process-level topology of container infrastructure, teams can correlate CI/CD events to understand the root cause of changes and events.
• Correlating events: With Upwind, it’s possible to detect and respond to container and Kubernetes threats in real time. Regardless of threat — like abnormal data access or lateral movement — Upwind lets teams kill malicious processes and automate prevention policies. 

With machine learning-powered anomaly detection runtime, Upwind powers lightning-fast response times and can highlight unknown threats within containerized applications for proactive risk identification, too.

To learn more about Upwind’s container security, schedule a demo.

Frequently Asked Questions

Can containers and VMs work together effectively?

Yes, containers and virtual machines (VMs) can work together effectively, with many organizations choosing to use both technologies to leverage their individual strengths, such as running containers within VMs to gain the benefits of both portability and security; this allows for flexible deployment, efficient resource utilization, and improved application management across different environments. Why choose this approach? There are a few great use cases:

• A cloud-based Kubernetes cluster may use VMs to host containerized applications, ensuring isolation between clusters or nodes.
• A financial institution may run critical legacy applications in VMs while developing new customer-facing services using containers.
• Hosting multi-tenant container workloads in separate VMs means no tenant can access another’s data, even if a container is compromised.
• A VM may host a containerized database workload, allowing for the dedicated resources they need for high performance while allowing scalability for other containers on the same VM.
• A development team might build new container applications while the operations team continues to manage existing VMs.

What’s the cost difference between containers and VMs?

Containers are generally significantly less expensive than VMs due to their lighter weight. Containers require fewer resources, from CPU to memory, since they share the host operating system kernel instead of having their own full OS. That also leads to lower overall infrastructure costs when deploying multiple applications.

Essentially, teams can run more containers on the same hardware compared to VMs, making them more cost-effective for most cloud-native applications. 

How do you choose between Docker and traditional VMs?

This question is closely related to the more general “containers vs VMs?” so let’s look at exactly what’s different:

•  Docker is popular, easy to use, and has its own ecosystem. That makes it a good choice for teams getting started with containerization. General container runtimes may require more expertise.
• Docker is built for developers. With development workflows and CI/CD integration, its tooling integrates well with DevOps workflows.
• Docker Hub is a large, validated public repository for container images. That makes it easier to source validated prebuilt images.
• It’s simple to orchestrate. Docker works with Docker Swarm and Kubernetes but is not required for Kubernetes. That makes for a flexible container workflow.

Docker offers an easy entry to containerization, so it may tip the scales to containerization for teams otherwise wary. Looking for agile development options alongside a developer-friendly ecosystem? Docker may fit the bill. More generally, Docker offers the same benefits as other containers when compared to VMs: Docker is lightweight, scalable, portable, and handles microservices and apps well.

What role do containers play in modern cloud architecture?

Containers are a fundamental building block for deploying and managing applications in the modern cloud.

Here’s how it started: virtual machines divided computing resources between users and gave rise to companies sharing those resources with many users over the internet. That idea grew into the cloud, where anyone could rent a small piece of a larger computer. Computing power was further divided into containers when those users realized they didn’t need an entire virtual machine, and its operating system, to themselves. They simply needed a small space to run a single program. Containers allow that flexibility, letting companies run programs that scale up to use more computing power instantly or scale down when it’s unneeded. 

Containers offer flexibility and allocate resources efficiently. But these advantages exist alongside the cloud’s overall distributed nature, so it’s harder to see workloads and control infrastructure than it used to be.  Ultimately, the benefits have outweighed the risks, and containers play a significant role in how users deploy workloads in the cloud.