Most people start with a container instance because it’s the quickest way to get a workload running. That’s a good default. The problem starts when you’re spending more time wrestling the environment than doing the work you came to do.
Switching to a virtual machine (VM) isn’t a “more advanced” choice for its own sake. It’s a practical choice when you need a normal Linux server shape and the container runtime keeps getting in the way.
If you want the quick “which should I pick?” version, read this first: VM or container: how to choose in 60 seconds. If you want the product update that introduced VMs, start here: Compute now supports virtual machines (VMs).
The signals that it’s time to switch
You don’t need a grand reason. You need one solid reason that keeps coming back. These are the ones that matter in real life.
You keep needing sudo, system packages, or OS-level changes. If your setup instructions start with “install these packages” and you’re stuck because you can’t do it cleanly, you’re already paying the tax. A VM gives you a full OS and the control that goes with it.
You want Docker to work the normal way. If your workflow depends on Docker itself (especially Docker Compose or multi-service stacks), a VM is the cleanest route. You’ll spend less time on workarounds and more time shipping. Run Docker the normal way on a Compute VM
You’re trying to run a “server-shaped” workload. Some software expects system services, background daemons, and a machine that behaves like a classic Linux host. You can sometimes bend that into a container. It’s rarely worth the fight when a VM is available.
You need tighter isolation for peace of mind. Containers are efficient. VMs give you a stronger boundary because they’re a full virtualized environment. If you’re in a multi-tenant situation, doing benchmarks, or working with stricter operational requirements, that isolation can be the difference between confidence and constant second-guessing.
You want a stable, persistent system environment for iterative work. If your workflow involves gradually shaping the machine (toolchains, system settings, long-lived dependencies), it’s more natural to do that on a VM than inside a container runtime that’s meant to stay lean.
A simple rule we like: if you’ve spent more than an hour trying to “make the container behave like a VM,” stop and use a VM.
When you should stay on a container
Sometimes the honest answer is “don’t switch.”
If your workload is a single service, or it’s based on a known template, containers usually stay simpler. They’re also easier to reproduce and replace. If you’re doing quick experiments, short jobs, or anything you want to spin up and tear down without thinking about OS maintenance, containers are still the right tool.
If you’re unsure, you can also treat containers as the scouting phase and VMs as the build phase. That path is common, and it’s sane.
What changes when you move to a VM
A VM gives you full OS control. That means you pick a Linux OS, connect over SSH, and manage the machine like you would on any other server. It also means you own more of the setup. You’ll install system dependencies, keep an eye on disk usage, and decide how your services run.
The rest should feel familiar. You still choose location and hardware the same way, and you still manage everything from the same Compute console.
For lifecycle details like stop/start behavior and what persists, treat the docs as the source of truth: Start, stop, and terminate instances.
How to switch without drama
You don’t need a big migration project. You need a controlled move with a rollback plan.
Start by listing what your container setup needs to run: environment variables, open ports, external data, and any services it talks to. This is the stuff that makes a workload “yours,” regardless of runtime.
Create a VM that matches your current shape. Pick the same location and roughly the same hardware class. That keeps the performance comparison honest, and it avoids introducing a second variable while you migrate. If you need a refresher on VM creation, use: Compute quickstart.
Decide how you want to run the workload on the VM. You have two common options. If your workload already ships as containers and you chose a VM mainly for “Docker works normally,” then the simplest path is often to run the same stack on the VM using Docker. If you’re switching because you need OS-level tooling, you might run the app directly on the VM instead. Both are valid. Pick the one that reduces setup time and future maintenance for your team.
Move data deliberately. Don’t rely on “it’ll probably still be there.” Put important artifacts in a place designed for storage and sharing (object storage, a repo, a dataset bucket, whatever your team already trusts). If you need help thinking about persistence, this explainer is meant to remove ambiguity: Does a VM keep my changes? Persistence on Compute explained.
Test the VM before you switch traffic. Run the same workload, confirm it behaves, then switch over. Keep the container instance around long enough to roll back if you notice something weird.
If you expose services to the internet, plan ports early. This is where people lose time. Set up connectivity once you know what you need, and keep it narrow. If you want the plain-English version of the connection options, use: SSH, HTTPS, TCP, UDP: how to expose a service from a Compute VM.
Common questions people ask before switching
Will switching break my workflow?
It shouldn’t, if you separate “workload configuration” from “runtime.” Your environment variables, ports, and data paths are the real workflow. The runtime is the container or the VM that hosts it.
Do I have to learn a lot of new things?
You’ll do more Linux-y setup on a VM, yes. That’s the point. The trade is that you stop fighting the environment and start using normal tools. For many teams, that’s a net reduction in effort after the first setup.
Should I switch just because VMs exist now?
No. If containers already fit, keep using them. Switch when you have a concrete need: Docker, OS control, system services, isolation, or reproducibility.
Try Compute
If you’re hitting limits with a container instance, don’t treat it as a personal failure. Treat it as a signal. Launch a VM, give yourself OS control, and keep the work moving.
