Public Cloud War: AWS vs Azure vs Google

AWS vs Azure vs Google

The competition is heating up in the public cloud space as vendors regularly drop prices and offer new features. In this article, we will shine a light on the competition between the three giants of the cloud: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft’s Azure.  While AWS has a significant head start on the others, Google and Microsoft are far from out of the race. As of today, March 23rd, 2016  Google is planning 12 new cloud data centers in next 18 months. They’ve both got the power, money, technology, and marketing to attract individual and enterprise customers. Let’s compare these three big players by service category: compute, storage, networking, and pricing structure.

public cloud

AWS vs Azure vs Google: cloud players

 

AWS vs Azure vs Google: Compute

AWS’s EC2 (Elastic Compute Cloud) provides Amazon’s core compute service, allowing users to configure virtual machines using either pre-configured or custom AMIs (machine images). You select the size, power, memory capacity, and number of VMs and choose from among different regions and availability zones within which to launch. EC2 also allows load balancing (ELB) and auto scaling. ELB distributes loads across instances for better performance, and auto-scaling allow users to automatically scale available EC2 capacity up or down.

In 2012, Google introduced their computing cloud service: Google Compute Engine (GCE). Google Compute Engine lets users launch virtual machines, much like AWS, into regions and availability groups. However, GCE didn’t become available for everyone until 2013. Since then Google has added its own enhancements, like load balancing, extended support for Operating Systems, live migration of VMs, faster persistent disks, and instances with more cores.

Also in 2012, Microsoft introduced their compute service as a preview, but didn’t make it generally available until May 2013.  Azure users choose a VHD (Virtual Hard Disk), which is equivalent to Amazon’s AMI, to create a VM. A VHD can be either predefined by Microsoft, by third parties, or be user-defined. With each VM, you need to specify the number of cores and amount of memory.

Table1 shows Big Three compute options:

Instance Families Instances types Regions Zones
AWS 7 38 Yes Yes
GCE 4 18 Yes Yes
Azure 4 33 Yes

Table1: AWS vs Azure vs Google: Compute

AWS vs Azure vs Google: Storage and databases

AWS provides ephemeral (temporary) storage that is allocated once an instance is started and is destroyed when the instance is terminated. It provides Block Storage that is equivalent to hard disks, in that it can either be attached to any instance or kept separate. AWS also offers object storage with their S3 Service, and archiving services with Glacier. AWS fully supports relational and NoSQL databases and Big Data.

Google’s Cloud Platform similarly provides both temporary storage and persistent disks. For Object storage, GCP has Google Cloud Storage. GCP supports relational DBs through Google Cloud SQL. Technologies pioneered by Google, like Big Query, Big Table, and Hadoop, are naturally fully supported. Google’s Nearline offers archiving as cheap as Glacier, but with virtually no latency on recovery.

Azure uses temporary storage (D drive) and Page Blobs (Microsoft’s Block Storage option) for VM-based volumes. Block Blobs and Files serve for Object Storage. Azure supports both relational and NoSQL databases, and Big Data, through Windows Azure Table and HDInsight.

Table2 shows a comparison of the three clouds in storage and DBs. 

Ephemeral (Temporary) Block Storage Object Storage Relational DB Archiving NoSQL and Big Data
AWS Yes EBS S3 RDS Glacier DynamoDB, EMR, Kinesis, Redshift
GCP Yes Persistent disks Google Cloud Storage Google Cloud SQL  Nearline Cloud Datastore, Big Query, Hadoop
Azure Temporary Storage – D Drive Page Blobs Block Blobs and Files Relational DBs Windows Azure Table, HDInsight

Table 2: AWS vs Azure vs Google: Storage and databases

AWS vs Azure vs Google: Networking

Amazon’s Virtual Private Clouds (VPCs) and Azure’s Virtual Network (VNET) allow users to group VMs into isolated networks in the cloud. Using VPCs and VNETs, users can define a  network topology, create subnets, route tables, private IP address ranges, and network gateways. There’s not much to choose between AWS vs Azure on this: they both have solutions to extend your on-premise data center into the public (or hybrid) cloud. Each Google Compute Engine instance belongs to a single network, which defines the address range and gateway address for all instances connected to it. Firewall rules can be applied to an instance, and it can receive a public IP address.

AWS is unique in providing Route 53, a DNS web service.

Table 3 compares the three clouds from a networking point of view.

Virtual network Public IP Hybrid Cloud DNS Firewall/ACL
AWS VPC Yes Yes Route 53 Yes
GCP subnet Yes Yes
Azure VNet Yes Yes Yes

Table 3:  AWS vs Azure vs Google: Networking

AWS vs Azure vs Google: Pricing Structure

AWS charges customers by rounding up the number of hours used, so the minimum use is one hour. AWS instances can be purchased using any one of three models:

  • on demand – customers pay for what they use without any upfront cost
  • reserved – customers reserve instances for 1 or 3 years with an upfront cost that is based on the utilization
  • spot – customers bid for the extra capacity available

GCP charges for instances by rounding up the number of minutes used, with a minimum of 10 minutes. Google recently announced new sustained-use pricing for compute services that will offer a simpler and more flexible approach to AWS’s reserved instances. Sustained-use pricing will discount the on-demand baseline hourly rate automatically as a particular instance is used for a larger percentage of the month.

Azure charges customers by rounding up the number of minutes used for on demand. Azure also offers short-term commitments with discounts.

Table 4 shows the comparison in Pricing and Models between the three public clouds.

Pricing Models
AWS Per hour – rounded up On demand, reserved, spot
GCP Per minute – rounded up (minimum 10 minutes) On demand – sustained use
Azure Per minute – rounded up commitments (pre-paid or monthly) On demand – short term commitments (pre-paid or monthly)

Table 4: AWS vs Azure vs Google: Pricing and Models

All this isn’t to say that there aren’t many other ways to compare the three giants, like support levels, management, security and access. However, this is a pretty good start. Cloud Academy remains vendor-neutral and offers learning paths, courses, quizzes, and hands-on labs for these competing services. Cloud Academy offers a free 7-day trial so readers may evaluate the content and quality of their learning resources.

Feedback means everything to us. We listen and react to your comments in shaping our offerings. Fairly recently, we added Learning Paths to our professional educational offerings. Learning paths guide students through a personalized learning experience. Each path is constructed as a specific track that brings a student’s knowledge to the next level, step by step.

The public cloud war drags on.  As cloud computing is still in an early, maturing stage, no one can predict exactly how things will change in the near future. But what we can say, is that prices will continue dropping and attractive features will continue appearing. Cloud computing is here to stay and the way we all use computers will follow along with it.

Motasem Aldiab

Motasem Aldiab

Motasem Aldiab is a professor, consultant, trainer, and developer. Dr. Aldiab has got his PhD in Computer Engineering from QUB in 2008. He is a certified trainer for the Cloud School and SOA School. He has been training and offering consultations for years in Java, SOA, and Cloud Computing, and leading workshops and training session (virtual or instructor led).

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