Containers vs Virtual Machines in AWS

What you’re actually comparing

A virtual machine (EC2 instance) runs a full operating system on top of a hypervisor. It has its own kernel, its own OS processes, its own file system. The hypervisor sits between the VM and the physical hardware and handles isolation. The VM doesn’t know it’s a VM — it thinks it’s a real computer.

A container shares the host OS kernel. You package your application with its libraries, config, and runtime into an image, and the container runtime (Docker, containerd) uses Linux namespaces and cgroups to isolate it from other containers on the same host. The container sees its own file system and process space, but it shares the kernel with everything else on that host.

This is not a minor implementation detail — it has real implications for security, startup time, and what you can run.

Containers vs VMs comparison

What containers add over VMs

Faster startup. A container image is already built. Starting a container is pulling layers from a registry (once) and running a process. No OS boot sequence, no kernel initialization. This matters for auto-scaling — ECS can launch a new container task in seconds; EC2 Auto Scaling takes a minute or more.

Immutability. Container images are built once and run many times. The image you tested is identical to the image you deploy to production. With EC2, you can configure instances differently over time through user data scripts, manual changes, or configuration drift. Containers eliminate that class of problem.

Density. You can run 20 containers on a single EC2 instance if they’re not CPU/memory intensive. Running 20 separate EC2 instances for 20 small services wastes money. Containers let you pack workloads together efficiently.

Portability across environments. A Docker image runs the same way in a developer’s local environment, in CI/CD, and in production ECS. The “works on my machine” problem disappears when the machine is always the same image.

CI/CD integration. Building a container image in a pipeline, pushing it to ECR, and deploying it to ECS is a clean, repeatable flow. The equivalent EC2 workflow — building AMIs, managing launch templates, rolling updates — is more complex and error-prone.

What VMs still give you that containers don’t

OS-level isolation. If a container escapes its namespace (a real attack vector called container breakout), it can access the host. A VM that’s compromised cannot cross the hypervisor boundary to the host. For workloads with strict security requirements — PCI, HIPAA, multi-tenant environments where different customers’ workloads must never interact — VM-level isolation may be required.

Run any OS. Need Windows Server 2019? A specific RHEL kernel? A custom BSD variant? Containers on Linux can’t run a different kernel. VMs can boot any OS. This is why Windows-native applications and workloads requiring specific kernel patches still belong on EC2.

GPU and hardware access. GPU instances (EC2 p3, p4, g4) expose GPU hardware directly to the VM. While containers can access GPUs via NVIDIA’s container runtime, it requires careful configuration and the EC2 instance must still have a GPU. If you’re doing machine learning training, EC2 with GPU instances is the standard approach.

Legacy applications. An application that writes to local disk, holds state in memory across requests, or depends on specific OS libraries installed outside the app is hard to containerize. Running it on EC2 directly is often the pragmatic choice until refactoring is justified.

Real scenario: containerizing a Flask app

A team runs a Flask API on an EC2 t3.medium. Deployment means SSHing in, pulling from git, running pip install -r requirements.txt, and restarting gunicorn. Configuration differences between dev and prod cause occasional environment-specific bugs.

They containerize it:

1. Write a Dockerfile — FROM python:3.12-slim, copy requirements, install dependencies, copy app code, expose port 8000
2. Build the image locally, push to ECR
3. Write an ECS task definition pointing to the image
4. Create an ECS service behind an ALB

Now deployment is: push to ECR, update the ECS service. ECS drains the old tasks, starts new ones with the new image, and flips the ALB target. Zero downtime, zero SSH, and dev runs the exact same image as prod.

The EC2 instance still exists — ECS on EC2 launch type runs containers on EC2 instances. The difference is they no longer interact with the EC2 instance directly. If they switched to ECS Fargate, the EC2 instances disappear entirely.

See EKS overview if the team later needs Kubernetes-style orchestration for multiple services.

When to still use EC2 directly

Containers are not always the right abstraction. Use EC2 directly when:

• GPU workloads — machine learning training jobs where you need direct GPU access and don’t want the container runtime overhead
• Legacy apps with complex OS dependencies — apps that require specific kernel modules, custom device drivers, or system services that predate containerization
• Windows Server workloads — particularly anything using Windows-native authentication, COM objects, or IIS that doesn’t fit the Linux container model
• Windows licensing is already paid — Bring Your Own License (BYOL) on EC2 can be significantly cheaper than Windows containers or Fargate Windows tasks
• Stateful single-tenant workloads — databases, message brokers, and other stateful services that store data on local disk and don’t benefit from container portability

See the EC2 overview for the full range of instance types, including GPU and high-memory options.