Google Compute Engine (GCE): Virtual Machines on Google Cloud Explained

Google Compute Engine (GCE) is Google Cloud’s Infrastructure-as-a-Service (IaaS): it lets you create and run virtual machines (VMs) on Google’s data centres and pay per second for what you use. You choose the operating system, the number of vCPUs, memory, disks and network, and you manage everything from the OS upward. It is Google’s direct equivalent of Amazon EC2 and Azure Virtual Machines.

What is Google Compute Engine used for?

• Hosting websites, APIs and backend servers that need a full Linux or Windows machine.
• Running databases such as MySQL or PostgreSQL yourself, when you want control that a managed service does not give.
• Training and serving machine-learning models on attached GPUs.
• Batch jobs, rendering, scientific simulation and CI build agents, often on cheap Spot VMs.
• “Lift and shift” migration: moving existing on-premises servers to the cloud without rewriting them.

How Compute Engine works

Every VM (Google calls it an instance) is made of a few building blocks that you pick when you create it:

Under the hood each VM runs on Google’s KVM-based hypervisor. Google can live-migrate running VMs to another host during maintenance, so most hardware work happens without a reboot.

Machine families and series

Machine types are grouped into families by workload. The series names change as Google releases new hardware; this is the line-up listed in Google’s documentation as of 2026:

Machine type names follow a pattern: series-class-vCPUs. So n2-highmem-8 is an N2 machine with 8 vCPUs and extra memory per vCPU. You can also create custom machine types on several series (for example 6 vCPUs with 20 GB) if no predefined size fits.

Storage options

• Persistent Disk: network block storage that survives VM deletion if you choose. Types include standard (HDD), balanced and SSD.
• Hyperdisk: the newer block storage where you set capacity, IOPS and throughput separately. Newer series such as C4 and N4 use it.
• Local SSD: physically attached to the host, very fast, but data is lost when the VM stops.
• Snapshots and images: point-in-time backups of disks, and templates for creating identical VMs.

Creating a VM: a quick example

From the Cloud Shell or any machine with the gcloud CLI installed:

gcloud compute instances create web-1 \
  --zone=asia-south1-a \
  --machine-type=e2-medium \
  --image-family=debian-12 --image-project=debian-cloud \
  --tags=http-server

That creates a Debian 12 VM with 2 shared vCPUs and 4 GB RAM in Mumbai. Add a firewall rule for the http-server tag and install a web server over SSH, and you have a live site. The same thing can be done by clicking through the Cloud Console.

Scaling and reliability

• Instance templates save a VM configuration so you can stamp out copies.
• Managed instance groups (MIGs) run many identical VMs, recreate any that fail health checks, and autoscale on CPU load or request rate.
• Cloud Load Balancing spreads traffic across MIGs in one or several regions behind one IP address.
• Regional MIGs spread VMs across zones so one zone going down does not take the service offline.

Pricing basics

Compute Engine bills per second, with a one-minute minimum. Exact rates vary by region and machine series, so check the Google Cloud pricing calculator before you commit. The main ways to pay less:

Older “preemptible VMs” were the forerunner of Spot VMs and were always stopped after 24 hours; Spot VMs have no fixed maximum run time.

Free tier

Google Cloud’s always-free tier includes one e2-micro VM per month in us-west1 (Oregon), us-central1 (Iowa) or us-east1 (South Carolina), 30 GB-months of standard persistent disk and 1 GB of outbound data transfer from North America per month. GPUs are never free. New accounts also get a $300 credit for 90 days. These figures are from Google’s free-tier documentation as of 2026 and can change.

Compute Engine vs App Engine vs Cloud Run vs GKE

GCE also sits alongside Amazon EC2 and Azure Virtual Machines, which offer the same IaaS model. For big-data platforms that often run on cloud VMs, see Hadoop pros and cons and what Databricks is. For the OS concepts behind VMs, virtual memory is a good starting point. Google’s own reference is the Compute Engine documentation.

Pros and cons of Compute Engine

• Pros: full control of the machine; per-second billing; custom machine sizes; live migration during maintenance; strong global network; wide GPU choice.
• Cons: you patch and secure the OS yourself; costs grow quietly if VMs are left running; more set-up work than serverless options; egress (outbound data) charges add up for high-traffic sites.

FAQs

Is Google Compute Engine IaaS or PaaS?

IaaS. It gives you virtual machines and you manage the operating system and software. App Engine is Google’s PaaS.

Is Google Compute Engine free?

Partly. The always-free tier covers one e2-micro VM a month in three US regions, 30 GB of standard disk and 1 GB of egress. Anything beyond that is billed.

What is the difference between Compute Engine and App Engine?

Compute Engine gives you a whole VM to manage. App Engine runs your code on servers Google manages, so you never see or patch the machine.

What is the AWS equivalent of Google Compute Engine?

Amazon EC2 (Elastic Compute Cloud). Azure’s equivalent is Azure Virtual Machines.

What are Spot VMs in Compute Engine?

Discounted VMs that run on Google’s spare capacity. Google can reclaim them at any time, so they suit batch or fault-tolerant jobs, not live websites.

Related Topics on EngineeringHulk

• 👉 Big Data – Meaning & Applications
• 👉 Databricks – Big Data Platform
• 👉 AI on Cloud (AWS, Azure, GCP) – Course