Google Cloud Machine Types Comparison

Google Cloud Platform offers a range of machine types optimized to meet various needs. Machine types provide virtual hardware resources that vary by virtual CPU (vCPU), disk capability, and memory size, giving you a breadth of options. But with so much to choose from, finding the right Google Cloud machine type for your workload can get complicated.

In the spirit of our recent blog on EC2 instance types, we’re doing an overview of each Google Cloud machine type. This image shows the basics of what we will cover, but remember that you’ll want to investigate further to find the right machine type for your particular needs.

Predefined Machine Types

Predefined machine types are a fixed pool of resources managed by Google Compute Engine. They come in five “classes” or categories:

Standard (n1-standard)

Standard machine types work well with workloads that require a balance of CPU and memory. The n1-standard family of machine types come with 3.75 GB of memory per vCPU. There are 8 total in the series and they range from 3.75 to 360 GB of memory, corresponding accordingly with 1 to 96 vCPU.

High-Memory (n1-highmem)

High memory machine types work for just what you’d think they would – tasks that require more system memory as opposed to vCPUs. The n1-highmem family comes with 6.50 GB of memory per vCPU, offering 7 total varieties ranging from 13 to 624 GB in memory, corresponding accordingly with 2 to 96 vCPUs.

High-CPU (n1-highpcu)

If you’re looking for the most compute power, the n1-highcpu series is the way to go, offering 0.90 GB per vCPU. There are 7 options within the high cpu machine type family, ranging from 1.80 to 86.6GB and 2 to 96 vCPUS.

Shared-Core (f1-micro)

Share-core machine types are cost-effective and work well with small or batch workloads that only need to run for a short time. They provide a single vCPU that runs on one hyper-thread of the host CPU running your instance.

The f1-micro machine type family provides bursts of physical CPU for brief periods of time in moments of need. They’re like spikes in compute power that can only happen in the event that your workload requires more CPU than you had allocated. These bursts are only possible periodically and are not permanent.

Memory Optimized (n1-ultramem or n1-megamem)

For more intense workloads that require high memory but also more vCPU than that you’d get with the high-memory machine types, memory-optimized machine types are ideal. With more than 14 GB of memory per vCPU, Google suggests that you choose memory-optimized machine types for in-memory databases and analytics, genomics analysis, SQL analysis services, and more. These machine types are available based on zone and region.

Custom Machine Types

Predefined machine types vary to meet needs based on high memory, high vCPU, a balance of both, or both high memory and high vCPU. If that’s not enough to meet your needs, Google has one more option for you – custom machine types. With custom machine types, you can define exactly how many vCPUs you need and what amount of system memory for the instance. They’re a great fit if your workloads don’t quite match up with any of the available predefined types, or if you need more compute power or more memory, but don’t want to get bogged down by upgrades you don’t need that come with predefined types.

About GPUs and machine types

On top of your virtual machine instances, Google also offers graphics processing units (GPUs) that can be used to boost workloads for processes like machine learning and data processing. GPUs typically can only be attached to predefined machine types, but in some cases can also be placed with custom machine types depending on zone availability. In general, the higher number of GPUs attached to your instances, the higher number of vCPUs and system memory available to you.

What Google Cloud machine type should you use?

Between the predefined options and the ability to create custom Google Cloud machine types, Google offers enough variety for almost any application. Cost matters, but with the new resource-based pricing structure, the actual machine you chose matters less when it comes to pricing.

With good insight into your workload, usage trends, and business needs, you have the resources available to find the machine type that’s right for you.


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