In 2019, many clients are struggling with the concept of multi-cloud: whether to be invested to a single cloud or to diversify into multi-cloud support. At the end of the day, should the enterprise leverage Best of Breed or Best integrated to realize optimal results?
Having a single cloud service provider (CSP) in many ways simplifies adoption of cloud. Each cloud is complex to master with their own nomenclature, tools and methods, there are several foundational elements that are necessary to successfully leverage cloud maturity. Yet, the answer is not as obvious as it seems, as some CSP specific best of breed features that can legitimately create an advantage.
Today I will focus on the first of a three-part series designed to explore the pros and cons of Multi-Cloud best of breed vs. the concentration of single cloud commitment. In short, we'll look at what it takes to deploy to a cloud in scale, the pros and cons of single cloud investment, and the pros and cons of multi-cloud investment.
Before we get too far into various clouds, we should discuss how an enterprise views cloud types as they look at business transformation.
Today, hybrid enterprise infrastructure portfolio has various elements:
This series will focus on the elements to deploy a Public and On-Premises Private Cloud as they both are developed and customized to the enterprise's tool and service management requirements.
In order to develop the level of maturity to be successful, a minimum level of financial, functionality, time commitment and resourcing investment are required to support at least these foundational services.
Now you might be reading this going, but I thought the cloud was turnkey? Doesn't each cloud provider have standards for these items?
In short, yes and no. While you can certainly leverage guides like the comprehensive "well architected framework" that Amazon built and tools to support these approaches like StackDriver (Google) or CloudTrail (Amazon), how you implement them into your standards is where the proverbial rubber meets the road. This is where the costs begin to impact plans.
Before talking about multi-cloud vs. single cloud, we should compare and contrast the three biggest growing players in US domestic Public Cloud services, especially in terms of market share.
Amazon: Amazon Web Services (AWS) is the 800-pound gorilla in the Cloud Services space. With over 30% market share and the most comprehensive suite of services, AWS is Gartner's magic quadrant leader for cloud services, supporting big data, IaaS, PaaS, virtual desktop, machine learning and even a machine learning driven racing league. If amazon were to be faulted, it is not in how few services offered, but rather how many, with several overlapping in functionality. Yet, that gives AWS an unparalleled ability to solve for a wide variety of use cases. As we work with clients, it is rare to find an enterprise client who doesn't actually have some AWS presence.
Azure: Azure is built on Microsoft's Hyper-V, Windows Core, and Microsoft's .net services. While they support Linux and Open Source capabilities, Azure's growth has been powered by aggressive Microsoft incentives, the dominance of Office 365 services (Exchange, SharePoint, Office, and Teams) and a relatively simple migration path for Windows IaaS services. For many Microsoft-centric shops, Azure represents a clean way to "get out of the data center business".
Google: While AWS is focused on migrating open source services and Microsoft is built on Windows Core services, Google is focused on data centric tools. Google's cloud in many ways is the opposite of Amazon in terms of breadth...while Amazon may have multiple tiers and file systems aplenty, Google consistently keeps their portfolio simple and clean with clear delineation for what services are strong for which use cases. Additionally, Google offers the ability to completely outsource and let google manage identity and access management (IAM), one of the most complicated elements to deploying Amazon in scale. Google also tends to be very aggressive in their pricing. Google's tools for machine learning (ML) and Artificial Intelligence (AI) have the maturity to even outsource the process of teaching industry specific AI models using APIs. Unlike AWS, Google does not have one size fits all approach to cloud services... either the solution fits...or it doesn't.
Be sure to check out the next installment on the Mulit-cloud vs. Single Cloud debate:
Originally Published at Above The Cloud