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Google Compute Engine (GCE) is Google Cloud’s infrastructure-as-a-service (IaaS) product for creating and running self-managed virtual machines (VMs) and bare-metal instances on Google’s infrastructure. In simple terms, it gives you configurable computers in the cloud: you choose the resources and operating system, while Google supplies the underlying infrastructure.
What Compute Engine provides
Compute Engine instances can be virtual machines or bare-metal machines. VMs run using virtualization; Google’s documentation says Compute Engine VMs use KVM. You can configure and manage instances in the Google Cloud console, with the Google Cloud CLI, or through REST APIs.
Compute resources and operating systems
You select a predefined machine type or, where supported, configure a custom one. The machine type sets resources such as virtual CPUs (vCPUs) and memory. Google groups machine families around workload profiles, including general-purpose, compute-optimized, memory-optimized, storage-optimized, network-optimized, and accelerator-optimized options.
You can use public Linux or Windows images, or create and use custom images. You are responsible for managing the operating system and software inside the instance.
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Storage and networking
Compute Engine offers durable network-attached disks as well as Local SSD. Local SSD is physically attached to the host and is not durable across instance stops; durable disk options preserve data when an instance stops. Choose storage based on the required durability, performance, and cost.
Each VM belongs to a virtual private cloud (VPC) network. Region, subnet, connectivity, and bandwidth requirements therefore matter when planning an instance.
How Compute Engine differs from a managed platform
Compute Engine is IaaS: you have control over the VM and its guest operating system, and you also manage that environment. A managed platform takes responsibility for more of the underlying stack, which can reduce operational work but offers less direct control. Compute Engine is a fit when your workload needs control over its VM, operating system, or software stack.
What to consider before choosing it
- Workload resources: Estimate CPU and memory needs, then compare machine families and types.
- Storage: Decide how much performance you need and whether data must persist when an instance stops.
- Location and connectivity: Account for region, VPC and subnet design, connectivity, and bandwidth.
- Cost: Charges depend on VM compute, storage, networking, and configuration. Check Google’s Compute Engine pricing or Google Cloud pricing calculator for current figures; prices and free-tier conditions can change, so a single starting price or allowance is not universal.
Availability commitment
Google’s current Compute Engine overview documentation, accessed in 2026, states a 99.5% minimum uptime service-level objective (SLO). Its applicability varies by region, Network Service Tier, and deployment configuration, so treat it as a qualified SLO rather than a uniform commitment for every setup. See Google’s Compute Engine overview for the current details.
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