Amazon Web Services (AWS) is a subsidiary of Amazon that provides on-demand cloud computing services. With over 165 services offered, AWS services can provide users with a comprehensive suite of infrastructure and computing building blocks and tools.
$0
per month
Google Compute Engine
Score 8.3 out of 10
N/A
Google Compute Engine is an infrastructure-as-a-service (IaaS) product from Google Cloud. It provides virtual machines with carbon-neutral infrastructure which run on the same data centers that Google itself uses.
$0.01
Hour
Pricing
Amazon Web Services
Google Compute Engine
Editions & Modules
Free Tier
$0
per month
Basic Environment
$100 - $200
per month
Intermediate Environment
$250 - $600
per month
Advanced Environment
$600-$2500
per month
Preemptible Price - Predefined Memory
0.000892 / GB
Hour
Three-year commitment price - Predefined Memory
$0.001907 / GB
Hour
One-year commitment price - Predefined Memory
$0.002669 / GB
Hour
On-demand price - Predefined Memory
$0.004237 / GB
Hour
Preemptible Price - Predefined vCPUs
0.006655 / vCPU
Hour
Three-year commitment price - Predefined vCPUS
$0.014225 / CPU
Hour
One-year commitment price - Predefined vCPUS
$0.019915 / vCPU
Hour
On-demand price - Predefined vCPUS
$0.031611 / vCPU
Hour
Offerings
Pricing Offerings
Amazon Web Services
Google Compute Engine
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
AWS allows a “save when you commit” option that offers lower prices when you sign up for a 1- or 3- year term that includes an AWS service or category of services.
Prices vary according to region (i.e US central, east, & west time zones). Google Compute Engine also offers a discounted rate for a 1 & 3 year commitment.
More Pricing Information
Community Pulse
Amazon Web Services
Google Compute Engine
Features
Amazon Web Services
Google Compute Engine
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
We are using RDS for the database services. With RDS, we don't have to manage much, as most of the DBA tasks are automated. For development purposes, we are using Kubernetes pods, which makes it easy to deploy applications and scale up as needed. AWS integration with in-house applications is seamless, making it easy to keep a data-sensitive application on-premises while still utilizing AWS services.
It is excellent if you have any workloads that need raw computing or plan to have any state-full services running in your environment like DBs (for which you don't want to use Managed services), cache, etc. It also gives you complete control over which versions of software, OS, etc., you need, and thus, you can build anything and deploy it on GCE.
A simple web-based interface that is a breeze to train new engineers to use. Our experienced engineers never have trouble finding or doing anything on GCE.
Sustained use and Committed use discounts mean we get top-tier VMs for an incredibly competitive price.
Wonderful identity and access management that gives us peace-of-mind when granting access to machines to contractors and other 3rd parties.
Fast VMs, lastest in hardware, and enough RAM to power even the hungriest of our services.
The L7 load balancer can be difficult to get set up. It's limited in its functionality, especially with the container engine.
It's hard to find certain objects on the web console. Often times the things I need to get to are buried in advanced menus.
Google's decision to only support MySQL on their relational DB service means that I have to manage Postgres instances in Compute on my own, managing everything from storage to backups.
I would gladly rely on AWS for any large-scale application deployment. For prototyping and small-scale applications, a more heavily managed environment on top of the 'bare metal' virtual infrastructure, such as Heroku or Elastic Bean Stalk, is probably a more productive approach in most cases
Its pretty good, easy and good performance. Also, interface is very good for starters compared to competitors. Infra as Code (IaC) using Terraform even added easiness for creation, management and deletion of compute Virtual Machines (VM). Overall, very good and very easy cloud based compute platform which simplified infrastructure, very much recommend.
Amazon Web Services is a great tool when it comes to middle size organizations like us. It provides multiple tools and functionalities in low costs. The best feature we have to pay as we go. No financial burden on company for the unused instances. It also comes with greater level of security such as two level authorization such as multi factor authorization.
Having interacted with several cloud services, GCE stands out to me as more usable than most. The naming and locating of features is a little more intuitive than most I've interacted with, and hinting is also quite helpful. Getting staff up to speed has proven to be overall less painful than others.
Google Compute Engine works well for cloud project with lesser geographical audience. It sometimes gives error while everything is set up perfectly. We also keep on check any updates available because that's one reason of site getting down. Google Compute Engine is ultimately a top solution to build an app and publish it online within a few minutes
AWS does not provide the raw performance that you can get by building your own custom infrastructure. However, it is often the case that the benefits of specialized, high-performance hardware do not necessarily outweigh the significant extra cost and risk. Performance as perceived by the user is very different from raw throughput.
The raw computer power is excellent; our applications feel snappy, pages load almos instantly for our customers and so on. The primary reason it is not a perfect 10 is that the native tools for monitoring individual VM performance can be complex, making it challenging to easily diagnose specific resource bottlenecks without significant configuration
The customer support of Amazon Web Services are quick in their responses. I appreciate its entire team, which works amazingly, and provides professional support. AWS is a great tool, indeed, to provide customers a suitable way to immediately search for their compatible software's and also to guide them in a good direction. Moreover, this product is a good suggestion for every type of company because of its affordability and ease of use.
The documentation needs to be better for intermediate users - There are first steps that one can easily follow, but after that, the documentation is often spotty or not in a form where one can follow the steps and accomplish the task. Also, the documentation and the product often go out of sync, where the commands from the documentation do not work with the current version of the product.
Google support was great and their presence on site was very helpful in dealing with various issues.
In my personal experience, AWS is superior to both GCP and Azure in the majority of usable applications. GCP suffers from the near total misunderstanding of how support system is even supposed to work, and while _some_ services are pretty nifty and well-polished, some are mindbogglingly designed black boxes with self-conflicting documentation. Some of it comes from having legacy systems, sure, but AWS somehow manages, even having a rather big lead start. Azure, from my limited experience, is limited to people somehow coerced into its usage by external constraints. That being said, IF you can design and implement something there, it will probably run fine.
When configuring Amazon ECS, it is a bit confusing as you are not able to find the actual issue. You need to enable Additional AppInsights to get detailed level info, which is not a concern when configuring on the Instance Level. Moreover, Azure VM does not provide an in-browser option; instead, it is Azure Bastion, but for that, you have to enable a dedicated subnet, which is a bit unnecessary.
Provisioning resources like large database instances is really quick. We can easily scale our instances up or down as per need.
Storing files in S3 instead of onprem NAS drives is much more economical, especially for the files stored in glacier deep archive for compliance purposes.
Backup snapshots of EBS volumes and RDS instances may increase the cost of cloud if not cleaned up properly.
Scalability means flexibility and less upfront costs
Can become expensive when hard set compute requirements are clear, but things like Spot VMs can help here too, or just having your own infrastructure and scaling up with Google. This is for more advanced cases though
Ramp up time is long, but after that it is quick to do many things and ROI is awesome