EC2 vs Azure VM vs GCP Compute Engine

Three brand names for the same rented machine. Once you see that, the comparison stops being about the products and starts being about where you already live.

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AWS EC2, Azure Virtual Machines, and GCP Compute Engine all rent you the same thing — a virtual machine you launch from an image, size from a family, wrap in a firewall, and pay for by the second or the hour. For most workloads the price and performance land close enough that the honest deciding factor is ecosystem, not a few cents an hour. Pick the cloud your identity, data, and team already sit in; then spend your energy on right-sizing and the right purchase model, which move the bill far more than the logo does.

I have run production on all three. The first time you move between them the vocabulary feels like a wall — different words, different consoles, a launch wizard that asks the same questions in a different order. Then you notice the wall is made of synonyms. An EC2 instance, an Azure VM, a Compute Engine instance are the same object: some vCPUs, some RAM, a disk, a network card, and a bill that ticks while it runs. Learn the object once and you have learned it three times.

The words are different; the thing is not

Most "vs" articles about these three are really glossaries. They walk you through the fact that AWS says security group and Azure says network security group and act like that is an insight. It is trivia. Useful trivia the first week, worthless by the second. Here it is in one table so you can stop looking it up, and then we can talk about the part that actually costs money.

That is the whole glossary the comparison articles stretch into 2,000 words. Screenshot it if you like. The moment you have built one VM on one cloud — which you can do free in an afternoon on our virtual machines class — this table reads as a rename sheet, not a study guide.

Naming differences are trivia. Right-sizing and the purchase model are the skill.

Sizing families: general, compute, memory, GPU

Every provider slices its VMs into families by what the workload leans on. The letters differ; the shape is identical. You are always choosing between a balanced machine, a CPU-heavy one, a RAM-heavy one, and specialized silicon for GPUs or high storage throughput.

Two things worth carrying out of this table. First, "burstable" (AWS T, Azure B) is the beginner's friend and the beginner's trap: cheap machines that bank CPU credits when idle and throttle hard when the credits run out. Great for a dev box, quietly awful under a steady load. Second, the Arm-based families — Graviton, Azure's Arm sizes, GCP's Tau — are usually the cheapest way to get the same work done if your software runs on Arm. That single choice beats most provider-shopping.

Pricing: same three models, three names

This is where the real money is, and where I will not quote you a per-hour dollar figure, because any number I print today is wrong by the time you read it. These prices move monthly, they differ by region, and every provider runs a calculator that will give you the current figure for the exact machine and region you care about. Bookmark all three. What does not move is the shape of the pricing models, and understanding the shape is what separates a $900 bill from a $300 one for identical compute.

Read that middle row twice. The commitment discount — AWS Savings Plans, Azure Reservations, GCP committed-use discounts — is the single biggest lever on a compute bill. Commit to a baseline of usage for one or three years and you routinely cut the rate by 40 to 70 percent versus on-demand, no engineering change required. The catch is honest: you are betting you will still be running roughly that much a year from now. For steady production, that bet is easy. For a workload you might kill next quarter, stay on-demand.

Spot is the other big lever, in the other direction. All three sell you their spare capacity at a steep discount on the condition that they can take the machine back with a couple of minutes' notice. For a stateless batch job, a CI runner, a render farm — anything that can be interrupted and restarted — spot is close to free money. For your database, it is a way to lose your afternoon.

So which one — honestly

If the machines are near-interchangeable and the pricing models are the same three ideas, the decision cannot be "which VM is best." There isn't a best VM. The decision is which ecosystem you are choosing to be fluent in, because the VM is the boring 10 percent — the identity system, the networking defaults, the managed services you'll wire the VM into, and the team that has to operate it are the other 90 percent.

For most people reading this, that answer is Azure, and not out of loyalty. Azure is where the enterprises are. If you already run Microsoft 365, Entra ID, and on-prem Windows Server, an Azure VM slots into identity and licensing you already own — Azure Hybrid Benefit alone can pay for the move by reusing Windows and SQL licences you're already holding. The job market follows the enterprises: the volume of "cloud engineer" postings that name a specific cloud leans heavily Microsoft, which is the whole reason this bootcamp teaches Azure first and treats AWS and GCP as accents you'll pick up in a week once the concepts are yours.

Pick AWS when the surrounding team already breathes it or you need the widest catalogue of niche services. Pick GCP when you're deep in data and Kubernetes and want those sustained-use discounts and clean networking. But notice none of those reasons is "the VM is cheaper," because at the machine level it basically isn't. The provider is an ecosystem decision. The bill is a right-sizing and purchase-model decision. Keep those two straight and you're already ahead of most people who've held the job for a year.

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