AWS vs Google Cloud Pricing - A Comprehensive Look - ParkMyCloud

AWS vs Google Cloud Pricing – A Comprehensive Look

aws vs google cloud pricing

Back in May 2017 I wrote a very popular blog about Cutting through the AWS and Azure Cloud Pricing Confusion.

Since ParkMyCloud also provides cost control for Google Cloud Platform (GCP) resources, I thought it might be useful to compare AWS vs Google Cloud pricing. An addition I will take a look at the terminology, and billing differences NOTE: There are other “services” involved, such as networking, storage and load balancing, when looking at your overall bill. I am going to be focused mainly on compute charges in this article.

AWS and GCP Terminology Differences

As mentioned before, in AWS, their compute service is called “Elastic Compute Cloud” (EC2). The virtual servers are called “Instances”.

In GCP, the service is referred to as “Google Compute Engine” (GCE). The servers are called also called “instances”. However, in GCP there are “preemptible” and non-preemptible instances.  Non-preemptible instances are the same as AWS “on demand” instances.  

Preemptible instances are similar to AWS “spot” instances, in that they are a lot less expensive, but can be preempted with little or no notice. The difference is that GCP preemptible instances can actually be stopped without being terminated. That is not true for AWS spot instances.

Flocks of these instances spun up from a snapshot according scaling rules are called “auto scaling groups” in AWS.

The similar concept can be created within GCP using “instance groups”. However, instance groups are really more of a “stack”, which are created using an “instance group template”. As such, they are more closely related to AWS CloudFormation stacks.


aws vs. google cloud pricing


AWS and GCP Compute Sizing

Both AWS and GCP have a dizzying array of instance sizes to choose from, and doing an apples-to-apples comparison between them can be quite challenging. These predefined instance sizes are based upon number of virtual cores, amount of virtual memory and amount of virtual disk.

They have different categories.

AWS offers:

  • Free tier – inexpensive, burst performance (t2 family)
  • General purpose (m3/m4 family)
  • Compute optimized (c4 family)
  • GPU instances (p2 family)
  • FPGA instances (f1 family)
  • Memory optimized (x1, r3/r4 family)
  • Storage optimized (i3, d2 family)


GCP offers the following predefined types:

  • Free tier – inexpensive, burst performance (f1/g1 family)
  • Standard, shared core (n1-standard family)
  • High memory (n1-highmem family)
  • High CPU (n1-highCPU family)


However, GCP also allows you to make your own custom machine types, if none of the predefined ones fit your workload. You pay for uplifts in CPU/Hr and memory GiB/Hr. You can also add GPUs and premium processors as uplifts.

Both providers take marketing liberties with things like memory and disk sizes.  For example, AWS lists its memory size in GiB (base2) and disk size in GB (base10).
GCP reports its memory size and disk size as GB. However, to make things really confusing this is what they say on their pricing page: “Disk size, machine type memory, and network usage are calculated in gigabytes (GB), where 1 GB is 230 bytes. This unit of measurement is also known as a gibibyte (GiB).”

This, of course, is pure nonsense. A gigabyte (GB) is 109 bytes. A gibibyte (GiB) is 230 bytes. The two are definitely NOT equal. It was probably just a typo.
If you look at what is actually delivered, neither seems to match what is shown on their pricing pages. For example, an AWS t2.micro is advertised as having 1 GiB of memory. In reality, it is 0.969 GiB (using “top”).

For GCP, their f1.micro is advertised as “0.6 GB”. Assuming they simply have their units mixed up and “GB” should really be “GiB”, they actually deliver 0.580 GiB. So, both round up, as marketing/sales people are apt to do.

With respect to pricing, this is how the two seem to compare, by looking at some of the most common “work horses” and focusing on CPU, memory and cost. (One would have to run actual benchmarks to more accurately compare):


aws vs. google cloud pricing


The bottom line:

In general, for most workloads AWS is less expensive on a CPU/Hr basis. For compute intensive workloads, GCP instances are less expensive

Also, as you can see from the table, both providers charge uplifts for different operating systems, and those uplifts can be substantial! You really need to pay attention to the fine print. For example, GCP charges a 4 core minimum for all their SQL uplifts (yikes!). And, in the case of Red Hat Enterprise Licensing (RHEL) in GCP, they charge you a 1 hour minimum for the uplifts and in 1 hour increments after that. (We’ll talk more about how the providers charge you in the next section.)

AWS vs. Google Cloud Pricing – Examining the Differences

Cost/Hr is only one aspect of the equation, though. To better understand your monthly bill, you must also understand how the cloud providers actually charge you. AWS prices their compute time by the hour, but requires a 1 hour minimum. If you start an instance and run it for 61 minutes then shut it down, you get charged for 2 hours of compute time.

Google Compute Engine pricing is also listed by the hour for each instance, but they charge you by the minute, rounded up to the nearest minute, with a 10 minute minimum charge. So, if you run for 1 minute, you get charged for 10 minutes. However, if you run for 61 minutes, you get charged for 61 minutes. On the surface, this sounds very appealing (and makes me want to wag my finger at AWS and say, “shame on you, AWS”).

You also really need to pay attention to the use case and the comparable instance prices. Let me give you a concrete example. So, here is a graph of 6 months worth of data from an m4.large instance. Remember that our goal at ParkMyCloud is to help you “park” non-production instances automatically, when they are not being used, to save you money.

This instance is on a ParkMyCloud parking schedule, where it is RUNNING from 8:00 a.m. to 7:00 p.m. on weekdays and PARKED evenings and weekends. This instance, assuming Linux pricing, costs $0.10 per hour in AWS. From November 6, 2016 until May 9, 2017, this instance ran for 111,690 minutes. This is actually about 1,862 hours, but AWS charged for 1,922 hours and it cost $192.20 in compute time.


aws vs. google cloud pricing


Why the difference? ParkMyCloud has a very fast and accurate orchestration engine, but when you start and stop instances, the cloud provider and network response can vary from hour-to-hour and day-to-day, depending on their load, so occasionally things will run that extra minute. And, even though this instance is on a parking schedule, when you look at the graph, you can see that the user took manual control a few times, perhaps to do maintenance. Stuff happens!

What would it have cost to run the similar instance in GCP?  If you look at the comparable GCP instance, (the n1-standard-2), it costs $0.1070/hour. So, this workload running in GCP would have cost $199.18 (not including Sustained Use Discounts). Since this instance really only ran 42.6% of the time (111,690 minutes out of 262,140 minutes), it would qualify for a partial Sustained Use Discount. With those discounts the actual cost would have been about $182.72. This is about $10 cheaper than AWS, even though per hour cost for AWS was lower). That may not seem much, but if you have hundreds or thousands of instances, it adds up.

AWS Reserved Instances vs GCP Committed Use

Both providers offer deeper discounts off their normal pricing, for “predictable” workloads that need to run for sustained periods of time, if you are willing to commit to capacity consumption upfront. AWS offers Reserved Instances. Google offers Committed Use Discounts (currently in beta). An in-depth comparison of these is beyond the intent of this blog (and you have already been very patient, if you made it this far). Therefore, I’ll reserve that discussion for a future blog.


If you are new to public cloud, once you get past all the confusing jargon, the creative approaches to pricing and the different ways providers charge for usage, the actual cloud services themselves are much easier to use than legacy on-premise services.

The public cloud services do provide much better flexibility and faster time-to-value. The cloud providers simply need to get out of their own way. Pricing is but one example where AWS and GCP could stand to make things a lot simpler, so that newcomers can make informed decisions.

When comparing AWS vs. Google Cloud pricing AWS oEC2 n-demand pricing may on the surface appear to be more competitive than GCPPpricing for comparable compute engine’s. However, when you examine specific workloads and factor in Google’s more enlightened approach to charging for CPU/Hr time and their use of Sustained Use Discounts, GCP may actually be less expensive. AWS really needs to get in-line with both Azure and Google, who charge by the minute and have much smaller minimums. Nobody likes being charged extra for something they don’t use.

In the meantime, ParkMyCloud will continue to help you turn off non-production cloud resources, when you don’t need them and help save you a lot of money on your monthly cloud bills, regardless of which public cloud provider you use.

About Dale Wickizer

Dale brings over 30 years of technology and engineering experience to his role as co-founder and Chief Technology Office (CTO) at ParkMyCloud. After experiencing the problem of growing cloud spend first-hand, and discovering that there was no simple way to solve it, Dale teamed up with co-founder Jay Chapel to create ParkMyCloud to solve the problem of cloud waste. Before founding ParkMyCloud, Dale was the CTO of the U.S. Public Sector at NetApp, Inc. where he set the future technology and product direction and managed key customer relationships. Prior to NetApp, Dale was an Associate Partner and IT Infrastructure Architect at Accenture, where he helped large enterprises plan and execute IT transformations, data center consolidations, and application deployments. Dale holds both a Bachelor's and a Masters Degree in Electrical Engineering from the Georgia Institute of Technology. He and his wife, Barbara, reside in Springfield, VA.

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