Aws vs Google Cloud


While AWS is undoubtedly the benchmark of cloud service quality, it has some drawbacks.Today we compare Amazon Web Services (AWS) with Google Cloud Platform (GCP).

AWS definitely is the leader of the cloud computing services, due to being the pioneer in the IaaS industry since 2006 and being 5 years ahead of other popular cloud service providers. However, this leads to certain inconveniences and drawbacks that can be exploited by the competition. Essentially, the sheer amount of AWS services is overwhelming.

While Google Cloud Platform does not boast such an ample list of services, it rapidly adds new products to the table. The important thing to note is that while AWS does offer a plethora of services, many of them are niche-oriented and only a few are essential for any project. And for these core features, we think Google cloud is a worthy competitor, even a hands-down winner sometimes, though many of essential features, like PostgreSQL support are still in beta in GCP.


Google Cloud can compete with AWS in the following areas:

  • Cost-efficiency due to long-term discounts
  • Big Data and Machine Learning products
  • Instance and payment configuration
  • Privacy and traffic security
Cost-efficiency due to long-term discounts

Customer loyalty policies are essential as they help the customers get the most of each dollar, thus improving commitment. However, there is an important difference here: AWS provides discounts only after signing for a 1-year term and paying in advance, without the right to change the plan. This, obviously, is not the perfect choice, as many businesses adjust their requirements dynamically, not to mention paying for a year in advance is quite a significant spending.

GCP provides the same flexibility, namely the sustained-use discounts, after merely a month of usage, and the discount can be applied to any other package, should the need for configuration adjustment arise. This makes long-term discount policy from GCP a viable and feasible alternative to what AWS offers, and rather an investment, not an item of expenditure. Besides, you avoid vendor lock-in and are free to change the provider if need be, without losing all the money paid in advance.

Big Data and Machine Learning products

AWS is definitely the leader for building Big Data systems, due to in-depth integration with many popular DevOps tools like Docker and Kubernetes, as well as providing a great solution for server less computing, AWS Lambda, which is a perfect match for short-time Big Data analysis tasks.

At the same time, GCP is in possession of the world’s biggest trove of Big Data from Google Chrome, which supposedly deals with more than 2 trillion searches annually. Having access to such a goldmine of data is sure to lead to developing a great kit of products, and Big query is definitely such a solution. It is capable of processing huge volumes of data rapidly, and it has a really gentle learning curve for such a feature-packed tool (it even produces real-time insights on your data). The best thing about it is that Big query is really user-friendly and can be used with little to none technical background, not to mention $300 credit for trying out the service.

Instance and payment configuration

As we explained in our article on demystification of 5 popular Big Data myths, cloud computing can be more cost-efficient as compared to maintaining on-prem hardware. Essentially, this really goes down to using the resources optimally and under the best billing scheme. AWS, for example, uses prepaid hourly billing scheme, which means running a 1 hour and 5 minute-long task would cost 2 full hours.

In addition, while AWS offers a plethora of various EC2 virtual machines under several billing approaches, these configurations are not customizable. This means if your task demands 1.4GB RAM, you have to go with the 2GB package, meaning you are overpaying. Of course, there are several ways to save money with Amazon, from bidding for Spot instances to lending Reserved instances and opting for per-second billing. Unfortunately, the latter option is currently available only for Linux VM’s.

GCP, on the contrary, offers per-second billing as an option for ALL their virtual machines, regardless of the OS’s they run on, starting 26th of September 2017. What’s even more important, their instances are fully configurable, so the customers can order 1 CPU and 3.25GB RAM, or 4.5GB, or 2.5GB — you get the meaning.

Privacy and traffic security

As The Washington Post told us, NSA has infiltrated the data center connections and eavesdropped on Google once (many more times, supposedly). This breach has lead to Google opting for full-scale encryption of all their data and communication channels. Even the stored data is encrypted, not to mention the traffic between data centers.

AWS is still lagging in this regard. Their Relational Database Service (RDS) does provide data encryption as an option, yet it is not enabled by default and requires intense configurations if multiple availability zones are involved in the equation. The inter-data center traffic is also not encrypted by AWS as of now, which poses yet another potential security threat.



You can equip AWS EC2 instances with up to 128 vCPUs and 3,904 GB of RAM.


You can equip Google Compute Engine instances with up to 96 vCPUs and 624 GB of RAM




General with volume sizes from 1GB to 16TB, and Provisioned IOPS SSD from 4GB to 16 TB



SSD, volume sizes from 1 GB to 64 TB




Amazon EC2 instances have a maximum bandwidth of 25 Gbps, however, this is only on the largest instance sizes. Standard instances max out at 10 Gbps/second.



Each core is subject to a 2 Gbits/second (Gbps) cap for peak performance. Each additional core increases the network cap, up to a theoretical maximum of 16 Gbps for each virtual machine.


Billing and Pricing

AWS simple monthly calculator


Google Cloud Platform pricing calculator



AWS Documentation

AWS ForumsGoogle


Cloud Forums

Google Cloud Documentation



AWS platform security model includes:

  • All the data stored on EC2 instances is encrypted under 256-bit AES and each encryption key is also encrypted with a set of regularly changed master keys.
  • Network firewalls built into Amazon VPC, and web application firewall capabilities in AWS WAF let you create private networks and control access to your instances and applications.
  • AWS Identity and Access Management (IAM), AWS Multi-Factor Authentication, and AWS Directory Services allow for defining, enforcing, and managing user access policies.
  • AWS has audit-friendly service features for PCI, ISO, HIPAA, SOC and other compliance standards.


Google Cloud security model includes:

  • All the data stored on persistent disks and is encrypted under 256-bit AES and each encryption key is also encrypted with a set of regularly changed master keys. By default.
  • Commitment to enterprise security certifications (SSAE16, ISO 27017, ISO 27018, PCI, and HIPAA compliance).
  • Only authenticated and authorized requests from other components that coming to Google storage stack are required.
  • Google Cloud Identity and Access Management (Cloud IAM) was launched in September 2017 to provide predefined roles that give granular access to specific Google Cloud Platform resources and prevent unwanted access to other resources.



Bottom line is, individuals or businesses, mostly startups that are looking to grow their business quickly would be likely to choose Google Cloud as their preferred cloud vendor. But, if you’re looking for a cloud vendor with the most experience in the cloud space and the one with an extensive catalog of services and offerings with a global recognition, AWS is the right choice for you. Well, both are the two big names in the cloud sphere with years of experience, the thing that differentiates the two is their approach to cloud computing. At the end of the day, it’s all get down to your need and requirements.