Virtual Machines (VMs) and Containers are complimentary and similar – both improve IT efficiency, application portability, and enhance DevOps. However, understanding the difference between them is a key component of developing an agile, cloud-native, application-driven strategy.
VMs include the guest operating system (OS) along with all the code for their applications and application dependencies that formerly ran on a single server or from a server pool. The size of VM images is generally measured in gigabytes. Multiple VMs can exist on a single physical server even if they are running on different operating systems. VMs abstract servers from the underlying hardware and typically persist throughout their useful life.
Containers share the host OS and include only the applications and their dependencies. The size of container images is generally measured in megabytes. Every container running on a single server shares the same underlying OS. Containers thus can spin up in milliseconds and are more efficient for ephemeral use cases where instances must be spun up and down with changes in demand.
VMs vs Containers
Size in Gigabytes
Size in Megabytes
Spin up time in minutes
Spin up time in milliseconds
Every VM requires its own OS
Share host OS
Resource and memory intensive
Less resource and memory used
Fewer VMs per physical server
Many more containers per physical server
Completely isolated from other VMs
Applications isolated from each other
Although both containers and VMs help improve the utilization of IT resources, each has its pros and cons. Virtual machines have been around for decades, allowing enterprises to combine several servers running different applications onto a single physical server, even if they run different operating systems. This has enabled substantial savings in server hardware and software as what used to run on several servers now runs on a single server instead. VMs are also the underpinnings of most cloud services. AWS, Azure, and other public clouds use the VM as one of their standard offerings. However, since they encapsulate an entire server in each virtual machine, the amount of CPU and RAM that VMs require can become unwieldly and limit the number of VMs that can exist on a single server.
Containers have rapidly gained popularity since the release of Docker in 2013, partly as a response to the amount of overhead that VMs consume. Since containers ride on a server’s OS, they share that single OS instance and other binaries and libraries, so containers need to only include application code, whether in the form of a single monolithic application or microservices that are bundled together in one or more containers to encompass a business function.
Thus, while VMs let an organization run several virtual servers on a single piece of hardware – regardless of their operating systems, containers offer lightweight, high-density application virtualization, the ability to spin applications and instances up and down in seconds, and some measure of security inherent in separating applications in their own containers.
VMs have and will continue to play a role in migrating legacy applications to the cloud and hybrid environments. Due to VM maturity there is a high comfort level as well as a level or inertia for those organizations that have settled into an IT architecture that revolves around VMs. As a result, VMs will continue to be the abstraction solution of choice for many persistent, monolithic, enterprise applications, especially those applications that do not have frequent updates.
However, containers lend themselves to modern practices and uses cases, such as CI/CD in agile, DevOps environments. Containers enhance portability of apps between vastly different configurations, so an application developed on a laptop and tested in a sandbox can run in the cloud with no changes required to support all three environments. Containers also offer near limitless scalability. Microservices-based applications that separate user interaction from back-end processing let front and back end each scale separately, and containers light weight mean nearly instantaneous spin-up of new instances as needed. Furthermore, taking a microservices approach to development encourages sharing common microservices routines between multiple applications and business processes, further improving developer efficiency.
As machine learning and artificial intelligence (ML/AI) applications permeate deeper into enterprises, these resource-heavy applications will favor VMs for deployment. Additionally, new network architectures like 5G which require more computing power at the edge will favor VMs to do the heavy lifting of these software-defined networks.
Containers will thrive in the world of modern, customer-facing applications and web services that require scalability, thanks to their near-instantaneous start-up time, and containers exceed as a development platform, since coders need to no longer worry about how development infrastructure varies from deployment infrastructure.
Finally, since containers were originally designed to be transient, they lend themselves well to network daemons, caching, and web services functions.
Both containers and virtual machines will continue to play important roles. Containers can run on VMs, enabling an organization to leverage its existing tools for automation, backup, and monitoring. Containers on VMs enable IT to use existing VM-savvy teams to manage a containerized environment as well. VMs will have new use cases as enterprises seek to leverage the power of their infrastructure – or the cloud – in new ways to support heavy-duty application and networking workloads.