AWS vs. Microsoft Azure vs. Google Cloud

While collectively, these three cloud providers dominate the space, their approach to cloud computing is dictated strongly by their background. Amazon has immense know-how when it comes to collating and aggregating massive amounts of data, Google’s heritage stems from an analytical background, and Microsoft’s strength comes from computing. This comparison will underline their strengths and weaknesses. As we proceed, tie these activities to your business objectives to find the right fit. Also, it’s important to note that your best fit may not turn out to be a single cloud provider.

Features

  • Features are an interesting factor to compare. While all three cover data analytics & visualization, most believe Azure is the most progressive in this area. In reality, all three have their way of categorizing the different elements, so start with projects requested, and then work with partners to figure out the best solutions. Unfortunately, some organizations are so committed to AWS for example, that they fail to recognize possibly more economical, and efficient alternatives.

  • AWS, Microsoft Azure, and Google Cloud Platform offer mostly similar basic capabilities around flexible compute, networking and storage. All three share typical elements of a public cloud: autoscaling, self-service, and instant provisioning, plus compliance, security, and features of identity management. They've all invested a lot in their cloud services, and have big parent companies to continue doing so. This has given us analytics offerings that are more mature. For instance, with Hadoop clusters, support is provided by Azure (HDInsight), AWS (Elastic MapReduce) and Google (Dataproc).

  • AWS still offers the widest range of services across storage, compute, analytics, database, mobile, networking, management tools, developer tools, security, IoT and enterprise applications. However, it has been around for the longest.

  • All three vendors have included machine learning tools and some features aimed at cutting-edge technology like serverless computing (Functions with Google and Azure, Lambda for AWS) and the Internet of Things (IoT). Customers can tap into any cloud system to 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.

  • Amazon uses different codes and names to break down their cloud products. From compute, storage, database, migration to game development and more. When it comes to AWS solutions, there is equally a significant degree of categorization. These solutions cover websites, backup and recovery, archiving, disaster recovery, DevOps and big data.

  • Azure provides an enormous array of features, but they add value by delivering specific capabilities based on the number of users. Take their Enterprise Agreements [EA]. From committing usage to Azure, these enable large organizations to earn benefits. The pros include more competitive pricing and flexible billing. 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.

  • Google Cloud platform, on the other hand, is throwing itself into Enterprise Computing. Google has three critical points behind their solutions, highlighting: Future-Proof Infrastructure, Seriously Powerful Data & Analytics and Serverless, Just Code. Google offers a Cloud Machine Learning Engine, that, thanks to its open source TensorFlow deep learning library, assists machine learning engineers build models. Google also has a whole host of off-the-shelf APIs on offer, for things like translation, natural language processing, and computer vision.

  • With all of them providing support for Docker services, the recent interest around containers is addressed too.

Compute, databases, storage, and networking

  • For compute, the EC2 instances are AWS's primary offering. It can be customized with a massive number of options. It also offers related services like the EC2 Container service, for app deployment, the Elastic Beanstalk, AWS Lambda, and Autoscaling.

  • Whereas, Virtual Machines (VMs) is what Azure's offering is centered around. Along with other tools like the Azure Autoscaling service, Cloud Services, and Resource Manager to assist with deploy applications on the cloud.

  • The scalable Compute Engine of Google delivers Virtual Machines in Google's data centers. They come with persistent disk storage, are quick to boot, promise consistent performance and are highly customizable based on the requirements of the customer.

  • All support relational databases, from Redshift, Amazon Relational Database Service, Azure SQL Database, Google Cloud SQL, along with Google Bigtable, Amazon DynamoDB and NoSQL databases with Azure DocumentDB.

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

  • The offerings of Microsoft include its Azure Blob block storage, core Azure Storage service, as well as Queue, Table and File storage. Offers Import Export, Site Recovery and Azure Backup too.

  • With connectivity to on-premise systems and automated server load balancing, all three typically offer amazing networking capabilities.

Pricing

  • One of the major benefits of most cloud providers is their competitive pricing strategies. They are all fighting to take workloads and migrate them to the cloud, given the recurring revenue they receive. This type of commoditization has forced a very open, online pricing model. Now you are even able to pay per minute.

  • Beginning with Amazon Web Services, the pricing of various cloud offerings is clearly jotted down from Enterprise to SMB. According to storage, their three-tier model is very friendly if all you're looking for is to put some data in the cloud. But sadly when it comes to storing fifty to five hundred terabytes of data, the percentage price difference isn’t that large.

  • Now Amazon may be great for large databases, but when it comes to putting apps in the cloud, Microsoft is better equipped. It too has a breakdown of its pricing. You’ll find Azure’s pricing more aggressive in some areas than Google and Amazon, because of the plan to lead certain segments of the cloud.

  • Google’s pricing model attempts to go head-to-head with its core competitors while emphasizing its desire to bill based on exact usage.

  • Generally, prices are comparable roughly, especially since AWS shifted to by-the-second from by-the-hour, pricing for its EBS and EC2 services in the Autumn of 2017, bringing it in line with Google and Azure. But making an outright comparison is difficult as all three offer slightly different discounts, pricing models and make frequent price cuts.

Your reasoning for picking one service over another will differ from another user. However, there are particular aspects of competing clouds that offer benefits in certain circumstances. That can always be compared. So, let's advocate for and against each now.

Why AWS?

  • The depth and breadth of the AWS offering is seen as a plus for AWS. AWS has built out its suite of cloud services since 2006, gaining a good head start on the competition. All its offerings are built to be enterprise-friendly so that they appeal to CIOs as well as its core audience of developers. Another advantage of the AWS cloud is its flexibility and openness.

  • However, its hybrid cloud strategy is an area AWS falls short in. AWS, unlike Microsoft, has proven to be dismissive about the advantages of on-premise private clouds. Most organizations prefer to keep sensitive data within their own data centers, like those in the financial sector, using public clouds for other purposes.

  • The scale of its offering can become another downside of AWS. While many see this as an attraction in many senses, it can be difficult at times to navigate the huge number of features that are on offer. Some see AWS as being a complex vendor to manage.

Why Azure?

  • Microsoft already has a firm footing within many organizations. This is seen as a significant pull for Azure, where Microsoft can smoothly play a part in assisting those companies transition to the cloud. Naturally, Azure links well with primary Microsoft on-premise systems like the System Center, Windows Server and Active Directory.

  • Additionally, 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. AWS isn't immune to downtime, though.

  • Azure can be somewhat restrictive in comparison to AWS since AWS provides users with enough options for supporting other platforms. Azure might not be the best option if you want to run anything other than Windows Server. However, Microsoft has been open to embracing open source platforms lately if a little slowly.

Why Google Cloud?

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

  • It has focused its go-to-market strategy on proving itself on innovative, smaller projects at large organizations, rather than on becoming a strategic cloud partner. If it plans to attract more traditional enterprises, then supporting pre-cloud businesses, increasing the breadth of its partnerships and IT processes will need to become focus areas.

  • The company is certainly backing machine learning tools. The organization’s popular TensorFlow framework and internal AI expertise are selling points in what is set to turn into a key battleground.

  • By launching innovative features in the machine learning space, with its BigQuery analytics engine, and the Cloud Spanner distributed database, it has proved itself to be more than an AWS copycat.

  • Also, it's worth noting that compared to the other two, Google has the smallest footprint of global instances.

Conclusion

AWS continues to lead the way, in very broad terms, regarding maturity and offering the widest range of functionality. However, the gap is certainly closing. Its expansive list of services and tools, along with its enterprise-friendly features make it an attractive proposition for big organizations. While its massive, continuously growing infrastructure offers economies of scale that allow aggressive price cuts.

Now it appears, Microsoft has begun to bridge that gap between the two. With its plans to strengthen ties with its on-premise software and ongoing investment in building out the Azure cloud platform, it will continue to bridge that gap. Microsoft Azure will continue to be a strong proposition for organizations already heavily invested heavily in Microsoft regarding technology and developer skills, of which there are undoubtedly many. With Google offering a slightly different proposition, it has made inroads with certain users. Even so, to become a viable enterprise choice, it has a lot of work to do. It might carve a niche for itself in advanced use cases based on machine learning and big data, but whether Google is willing to relinquish the key IaaS market to its largest competitors is another subject.

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