{"id":15673,"date":"2018-05-08T22:12:55","date_gmt":"2018-05-09T02:12:55","guid":{"rendered":"https:\/\/www.sherweb.com\/blog\/?p=15673"},"modified":"2018-05-21T10:16:10","modified_gmt":"2018-05-21T14:16:10","slug":"amazon-web-services-vs-microsoft-azure","status":"publish","type":"post","link":"https:\/\/www.sherweb.com\/blog\/cloud-server\/amazon-web-services-vs-microsoft-azure\/","title":{"rendered":"Amazon Web Services vs. Microsoft Azure: Comparing Cloud Leaders"},"content":{"rendered":"

In our earlier blog posts, we\u2019ve covered many Azure features, highlighting its PaaS capabilities and touching on some of the artificial intelligence services (Cognitive Services APIs) and how the Azure platform offers a rich ecosystem for businesses to host, scale, manage and easily provision services in the cloud.<\/p>\n

In this blog post, we compare Amazon Web Services and Microsoft Azure<\/a>, and we look at the key features both computing platforms have to offer. By the time you\u2019re done reading, you\u2019ll have a better understanding of both AWS and Azure.<\/p>\n

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Click here to download our free ebook which compares AWS, Google and Microsoft Azure\u00a0<\/a><\/h3>\n

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Computing, Storage, and Databases<\/strong><\/h2>\n

Most of the resources you provision in the cloud revolves around three main areas: computing, storage, and databases.\u00a0 Both Amazon Web Services and Microsoft Azure let you provision all three resources, but let\u2019s look at these in more detail.<\/p>\n

Computing<\/strong><\/h4>\n

Computing instances are the virtual servers that host the applications that you need to run. With Amazon, these computing instances are called Elastic Compute (EC2) whereas in the Azure realm they are known as Virtual Machines (VMS).<\/p>\n

AWS and Azure do well in this category, and both support Windows and *NIX images. Azure has a slightly higher CPU count, but both offer everything you will need which places both platforms on an even keel for the \u201ccompute\u201d category.<\/p>\n

Storage<\/strong><\/h4>\n

Running services in the cloud involve processing data which must be saved at some point. AWS\u2019s storage mechanism is called Simple Storage Service (or S3), and Microsoft Azure\u2019s is called Blob Storage.<\/p>\n

Both AWS and Azure are strong in this category and take care of the basics, including features like REST API access and server-side data encryption.\u00a0 AWS has some added handy features, for example, you can auto-bill customers that use your storage service \u2013 ideal if you have a SaaS product.<\/p>\n

Databases<\/strong><\/h4>\n

Most modern software applications need a database to save information, and AWS and Azure let you provision database services, whether you need a relational database or NoSQL offering; both platforms have you covered.<\/p>\n

AWS is dubbed RDS (Relational Database Service), and Azure\u2019s offering is called SQL Server. At the time of writing, AWS offers multiple database instances such as MySQL, MariaDB, SQL Server and Aurora.<\/p>\n

While AWS may have more instance types you can provision and offers slightly more control over your DB instances; there\u2019s no getting away from it, Azure tooling and interface makes it easy to perform DB operations.<\/p>\n

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Developer Support<\/strong><\/h2>\n

This is where Azure really shines, especially for professionals that are invested in Microsoft Tooling such as Visual Studio. From Visual Studio you can connect to Azure services such as chatbots and debug remotely from your local codebase.<\/p>\n

Publishing web projects and services are done in just a few clicks under the hood, and all required Azure instances will be set up for you. Azure also integrates nicely with Active Directory which is another great bonus.<\/p>\n

AWS has welcomed the integration of open source technologies into their infrastructure whereas Microsoft has traditionally been \u201cclosed off\u201d here – this has been changing in recent years, but with this in mind, you might find that open source software plays a little easier with AWS.<\/p>\n

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Machine Learning and Artificial Intelligence <\/strong><\/h2>\n

In recent years we\u2019ve seen the democratization\u00a0 of complex machine learning algorithms (Machine Learning as a Service \u2013 MLaaS) in the form of new online products and services. No longer do you need a Ph.D. to undertake natural language processing (NLP) or to identify human emotions in a set of images.<\/p>\n

Both AWS and Azure offer a similar range of API driven services that allows you to add cognitive intelligence to your applications out of the box.\u00a0 For example, these services can be consumed via REST APIs making it simple for developers to extend your existing software products.<\/p>\n

Just some of the areas in which cognitive intelligence can be added, include, but are not limited to:<\/p>\n