AWS vs Azure vs Google: What's the best cloud platform for the enterprise?

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It's the defining cloud battle of our time: AWS vs Microsoft Azure vs Google Cloud Platform. Who can win the IaaS enterprise market? ComputerworldUK takes a look at the merits of the big three vendors

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The adoption of cloud computing has quickly become a key driving force for businesses today, as applications are moved out of on-premise data centres in a bid to cut costs and increase agility.

Early concerns over security and data sovereignty have largely been addressed by the big three public cloud vendors: Amazon Web Service (AWS), Microsoft Azure and Google Cloud Platform, with only the most heavily regulated businesses lagging behind in terms of adoption. 

This has fueled a crowded infrastructure-as-a service (IaaS) market, worth a total of $25 billion in 2016, according to Gartner's most recent statistics, which project the market will reach $45 billion by 2018.

IaaS is a model where a third-party provider hosts and maintains core infrastructure, including hardware, software, servers and storage on behalf of a customer. This typically includes the hosting of applications in a highly scalable environment, where customers are only charged for the infrastructure they use.

AWS has dominated the market since it started offering cloud services in 2006. A Synergy Research report from February 2017 puts AWS' market share at 40 percent, with Microsoft, Google and IBM, which they have grouped together, at 23 percent combined. 

But despite AWS’s dominance, Microsoft has quickly gained ground under the leadership of "cloud first" CEO Satya Nadella, building a huge global cloud network of its own. Then there is the internet giant Google, which has been busy building out its public cloud services and IaaS business under the Google Cloud Platform (GCP).

So what separates the big three cloud providers? And how can you start to decide which IaaS platform is best-suited to your organisation?

Read next: Five key legal considerations when negotiating cloud contracts

Features and services

Selecting one cloud over the others will come down to the wants and needs of each individual customer and the workloads they are running. It is often the case that organisations will use multiple providers within different parts of their operations, or for different use cases, called a multi-cloud approach.

However, there are a number of differentiating factors that separate the approaches of the three firms, which can help end-users consider which is right for them.

AWSMicrosoft Azure and Google Cloud Platform offer largely similar basic capabilities around flexible compute, storage and networking. They all share the common elements of a public cloud: self-service and instant provisioning, autoscaling, plus security, compliance and identity management features.

All three are investing heavily in their cloud services, and have sizable parent companies to do so. This has resulted in more mature analytics offerings. For example, support for Hadoop clusters are provided by AWS (Elastic Map Reduce), Azure (HDInsight) and Google (Dataproc).

AWS still offers the largest range of services with nearly 100 across compute, storage, database, analytics, networking, mobile, developer tools, management tools, IoT, security and enterprise applications. But it has been around the longest.

All three vendors have added machine learning tools and a number of features targeted at cutting edge technology areas like the Internet of Things (IoT) and serverless computing (Lambda for AWS, Functions with Azure and Google), while customers can tap either cloud to variously build a mobile app or even create a high performance computing environment depending on their needs.

Naturally, all three vendors are strong in machine learning as they can draw on deep wells of internal expertise.

AWS launched the Amazon Machine Learning service in April 2015 to help developers create machine learning models. Then in 2016 it announced three new machine learning services for image recognition (AWS Rekognition), text to speech deep learning models (Polly) and the engine that powers Alexa (Lex).

Read next: AWS announces three new AI and machine learning services for customers: Amazon AI availability and pricing

Google offers a Cloud Machine Learning Engine, which helps machine learning engineers build models based on its open source TensorFlow deep learning library. Google also offers a whole host of off-the-shelf APIs for things like natural language processing, translation and computer vision.

Microsoft's Azure Machine Learning Studio allows specialist developers to write, test and deploy algorithms, as well as a marketplace for off-the-shelf APIs.

The recent buzz around containers is catered for too, with all three providers supporting Docker services.

All three providers take a pretty open approach to partnerships, allowing customers to run various apps and services in their cloud environments.

Google, for example, has announced a range of key partnerships with established vendors like SAP, Pivotal and Rackspace. Read next: Google courts enterprise customers with SAP, Pivotal and Rackspace partnerships

For UK customers worried about data sovereignty, AWS launched its UK region in December 2016, with Microsoft and Google quickly following suit.

Compute, storage, databases and networking

For compute, AWS’ main offering is its EC2 instances, which can be tailored with a large number of options. It also provides related services such as Elastic Beanstalk for app deployment, the EC2 Container service, AWS Lambda and Autoscaling.

Meanwhile, Azure's compute offering is centred around its Virtual Machines (VMs), with other tools such as Cloud Services and Resource Manager to help deploy applications on the cloud, and its Azure Autoscaling service.

Google's scalable Compute Engine delivers VMs in Google's data centres. They are quick to boot, come with persistent disk storage, promise consistent performance and are highly customisable depending on the needs of the customer.

All three cloud providers support relational databases - that's Azure SQL Database, Amazon Relational Database Service, Redshift and Google Cloud SQL) - as well as NoSQL databases with Azure DocumentDB, Amazon DynamoDB and Google Bigtable.

Read next: Best cloud-based relational database options for the enterprise

AWS storage includes its Simple Storage (S3), Elastic Block Storage (EBS), Elastic File System (EFS), Import/Export large volume data transfer service, Glacier archive backup and Storage Gateway, which integrates with on-premise environments.

Microsoft’s offerings include its core Azure Storage service, Azure Blob block storage, as well as Table, Queue and File storage. It also offers Site Recovery, Import Export and Azure Backup.

All three typically offer excellent networking capabilities with automated server load balancing and connectivity to on-premise systems.

Pricing

Pricing can be a huge attraction for those considering a move to the cloud, and with good reason: there has been a continued downward trend on prices for some time now as the big providers compete. See also: Making sense of public cloud pricing: What does the cloud ‘pricing war’ mean for CIOs in 2016?

In general terms prices are roughly comparable, especially since AWS shifted from by-the-hour to by-the-second pricing for its EC2 and EBS services in the Autumn of 2017, bringing it into line with Azure and Google.

However, making a clear comparison can be tough as all three offer slightly different pricing models, discounts and make frequent price cuts.

AWS provides a price calculator here, Microsoft here and Google here.

All vendors offer free introductory tiers before beginning to charge customers, and typically offer credits to attract innovative startups onto their platforms.

Customers

A high-profile user base may not be the main reason for choosing your cloud provider, but it can help more cautious organisations understand how the public cloud is benefiting others in their sector.

This is clearly a strong point of AWS. It has increasingly taken on large customer deals. For example, although the US Central Intelligence Agency eventually signed a contract with IBM, it awarded AWS a contract to build its private cloud in a one-off deal in 2013, which could be seen as a symbolic moment for potential buyers.

A longstanding AWS customer is Netflix, which eventually decided to shut all of its data centres in a final move to the cloud in 2016. But aside from web pioneers, AWS has been truly successful in convincing more traditional businesses to move to the cloud.

Other major customers include: AstraZeneca, NewsCorp, AirBnB, Aon, Channel 4, Financial Times, Dow Jones, Kurt Geiger, Lonely Planet, Nasdaq, Nike, Nisa Retail, Pfizer, and the Royal Opera House. A full list of AWS customers can be seen here.

Read next: Wall Street regulator Finra goes ‘all in’ on the public cloud with AWS

Microsoft perhaps has less high profile Azure users, with most of the messaging from the vendor appearing to be around its widely used software-as-a-service (SaaS) tools. But the Redmond firm has also notched up some notable customer wins such as Pearson, Ford, NBC News and Easyjet, to name but a few.

In a bid to turn this around Microsoft cut around ten percent of its global sales force in July 2017, as part of a broad reorganisation to focus on selling its cloud services under the Azure brand.

The new selling strategy at the company was revealed in a leaked email, which was obtained by the Wall Street Journal. In it, Judson Althoff, executive vice president for worldwide commercial business, outlined how Microsoft wants to focus on targeting businesses instead of specific industries or market segments. He said he wanted to increase the “technical depth and better align sales and services to solution areas” at the company.

Google is in a similar position, but has notched up some key wins in recent years. UK bank HSBC has opted for Google Cloud for its analytics and machine learning capabilities. However, HSBC is taking a clear multi-cloud approach, partnering with all three providers for different workloads.

Read next: HSBC turns to Google Cloud for analytics and machine learning capabilities

Snapchat parent company Snap also spends a great deal with Google for IaaS, but it also spends with AWS. During the social network's IPO process it was revealed that the company is committed to a $2 billion five-year deal with Google for cloud services, as well as a $1 billion deal with AWS over five years.

Home Depot and Disney were also named as Cloud Platform customers during Google's 2017 Cloud Next conference.

Read next: Majority of new core banking projects will be in the public cloud 'by 2020', says Temenos

AWS pros and cons

As mentioned before, the reasons for picking one vendor over another will differ for each customer. But there are aspects of the competing clouds that will offer benefits in certain circumstances.

The breadth and depth of the AWS offering is seen as a plus for AWS.

Read next: The history of AWS: A timeline of 12 defining moments from 2002 to now

AWS had a head start on the competition, building out its suite of cloud services since 2006. All of these are built to be enterprise-friendly so that they will appeal to CIOs as well as its core audience of developers.

The vendor ranks highly on platform configuration options, monitoring and policy features, security and reliability. Its partner ecosystem and general product strategy are also seen as market leading, and its AWS Marketplace has a large number of third-party software services.

Another of the benefits of the AWS cloud is its openness and flexibility. For example, Transport for London - which also relies on Azure in other parts of its operations - has used AWS to meet spikes in demand for its online services such as its Journey Planner tool.

However, one area AWS falls short to some degree is with its hybrid cloud strategy. Unlike Microsoft, AWS has tended to be dismissive of the benefits of on-premise private clouds. Many organisations prefer to keep sensitive data within their own data centres - such as those in the financial sector - using public clouds for other purposes. 

At the same time, this clearly has not deterred many customers from using AWS as part of their cloud strategy, regardless of whether they plan to move all systems to the cloud or not.

Another downside to AWS is the scale of its offering. While this is an attraction in many senses, it can be difficult at times to navigate the large numbers of features that are on offer, and some see AWS as being a complex vendor to manage.

Azure pros and cons

The big pull for Azure is where Microsoft already has a strong footing within an organisation and can easily play a role in helping those companies transition to the cloud. Azure naturally links well with key Microsoft on-premise systems such as Windows Server, System Center and Active Directory.

In addition, while both AWS and Azure have PaaS capabilities, this is a particular strength of Microsoft’s.

One of the downsides, however, has been a series of outages over the years. Gartner analyst Lydia Leong has recommended considering disaster recovery capabilities away from Azure for critical applications hosted in the cloud. AWS isn't immune to downtime, though, suffering a major S3 outage of its own in March 2017.

As part of its 2017 IaaS global Magic Quadrant, Gartner states that its clients have had issues with "technical support, documentation, training and breadth of the ISV partner ecosystem" - but the company has been steadily working on these areas.

Whereas AWS provides users with many options for supporting other platforms, Azure can be somewhat restrictive in comparison. If you want to run anything other than Windows Server then Azure might not be the best solution, but Microsoft has been willing to embrace open source platforms, if a little slowly. For example, the company has been busy extending its support for Linux operating systems in 2017.

Google Cloud Platform pros and cons

Google has a good track record with innovative cloud-native companies and has a good standing in the open source community, but has traditionally struggled to break into the enterprise market.

Its go-to-market strategy has been focused on proving itself on smaller, innovative projects at large organisations, rather than becoming a strategic cloud partner. Increasing the breadth of its partnerships and supporting pre-cloud businesses and IT processes will need to become focus areas if it wants to attract more traditional enterprises.

The company is certainly betting big on its machine learning tools, with the company's internal AI expertise and popular TensorFlow framework as selling points in what is set to become a key battleground.

Read next: How Google plans to bring AI and machine learning to the enterprise

It has also proved itself more than an AWS copycat, launching innovative features in the machine learning space as well as its BigQuery analytics engine, and the Cloud Spanner distributed database.

It is also worth noting that Google has the smallest footprint of global instances of the big three.

Verdict

In very broad terms, AWS continues to lead the way in terms of offering the widest range of functionality and maturity. It continues to be the clear market leader, but the gap is closing.

Its expansive list of tools and services, along with its enterprise-friendly features make it a strong proposition for large organisations. Meanwhile its huge and continuously growing infrastructure provides economies of scale that enable aggressive price cuts.

But it appears that Microsoft has started to bridge the gap between the two, and will continue to do so with its ongoing investment in building out the Azure cloud platform and further plans to strengthen ties with its on-premise software.

For organisations already heavily invested heavily in Microsoft in terms of technology and developer skills - of which there are undoubtedly many - Microsoft Azure will continue to be a strong proposition.

Then there is Google, which offers a slightly different proposition. It is making good progress with certain customers but has much more work to do to prove itself a viable enterprise option.

It may end up carving a niche out for itself in advanced use cases around big data and machine learning, but whether it is ready to cede the core IaaS market to its two biggest rivals is another matter.

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