Containers vs virtual machines: what’s the difference?

How each one works

A virtual machine is created by a hypervisor — software that carves one physical computer into several complete virtual ones. Each VM gets virtual hardware and boots its own full operating system, exactly as a physical machine would. The software inside a VM cannot tell it is sharing hardware; from its perspective it owns an entire computer. This is the virtualisation that underpins cloud IaaS — an Amazon EC2 instance is a virtual machine.

A container takes a shallower cut. Instead of virtualising hardware, a container runtime uses isolation features built into the host operating system to give each container its own view of the filesystem, processes and network — while every container shares the host’s OS kernel. A container image packages the application plus everything it needs — runtime, libraries, configuration — but no operating system kernel of its own. The result is an isolated environment measured in megabytes that starts in moments, rather than a full machine that must boot.

The practical consequences

That one architectural difference — own kernel versus shared kernel — explains every practical trade-off between the two:

• Size — a VM carries a full operating system and is typically far larger; a container image carries only the application and its dependencies.
• Start-up speed — a VM must boot an OS; a container starts about as fast as launching a process.
• Density — because containers skip the per-instance OS overhead, one host can run many more containers than VMs, using hardware more efficiently.
• Isolation strength — a VM’s hardware-level boundary is the stronger wall; containers share a kernel, so their isolation, while good, is inherently lighter.
• Portability — a container image runs identically anywhere a compatible runtime exists, which is the property that made containers the standard shipping format for software.

Why containers solved “works on my machine”

Before containers, moving an application between a developer’s laptop, a test server and production meant hoping every environment had the same operating system packages, language runtime, libraries and configuration — and debugging the inevitable drift when they did not. “It works on my machine” became the industry’s bitter joke because environments were assembled by hand and never quite matched.

A container image ends that by making the environment part of the artefact. The application and its exact dependencies are built into one immutable, versioned image; whatever runs that image runs the same stack, everywhere. Popularised by Docker, this idea turned deployment from “install and configure software on a server” into “run this image” — a shift so useful that container images became the default unit of software delivery in the cloud era.

Orchestration: running containers at scale

One container is easy; a production system of dozens of services, each running multiple container copies across a fleet of machines, is not something to manage by hand. Orchestration platforms do that job: they schedule containers onto hosts, restart failures, scale copies up and down with demand, and wire up networking so containers can find each other. Kubernetes is the industry’s dominant orchestrator — powerful, and famously complex to operate.

On AWS the main options are ECS (Elastic Container Service), Amazon’s own simpler orchestrator, deeply integrated with the rest of AWS; EKS (Elastic Kubernetes Service), which runs Kubernetes for you — the standard platform without the burden of operating its control plane; and Fargate, a serverless compute engine that works with both, running your containers without you managing any underlying servers at all. Fargate is where the container and serverless stories meet: containers describe how software is packaged, serverless describes who manages the machines, and Fargate offers both at once.

When a virtual machine is still the right answer

Containers did not make VMs obsolete — cloud container services themselves run on virtualised infrastructure, and choosing between exposing VMs or containers to your team is a real architectural decision. A VM remains the better fit in several situations: legacy applications built to own a whole machine, which often migrate to a VM unchanged but would need re-engineering to containerise; software that needs a different operating system kernel from the host, since containers share the host’s kernel and cannot pretend otherwise; and workloads where the strongest isolation boundary is a requirement, because hardware-level virtualisation is a thicker wall than kernel-level separation.

The pragmatic industry position is “both, by workload”: containers as the default for new, service-shaped applications; VMs for legacy systems, different-kernel needs and hard isolation. Understanding why each fits where — rather than declaring a winner — is exactly the judgement the associate-level exams reward.

Where this shows up in certification study

Containers versus VMs is quietly load-bearing across the AWS path. The Cloud Practitioner expects the conceptual distinction and recognition of the compute family: EC2 as virtual machines, ECS and EKS as container services, Fargate as serverless container compute, Lambda as functions. The Solutions Architect Associate turns that vocabulary into decisions — scenario questions routinely hinge on choosing between EC2, containers on ECS or EKS, Fargate and Lambda on cost, operations and scaling grounds.

The Developer Associate expects familiarity with building and shipping container images to AWS’s container services, and the CloudOps Engineer Associate approaches the same services from the operations side. If you can explain, in plain words, why a container starts faster than a VM and what Fargate removes from your plate, you have the conceptual core those questions test. From here, our explainer on serverless computing covers the neighbouring concept in the same depth.