The advent of the public cloud marked a significant turning point. Launched in 2006, Amazon Web Services (AWS) began offering on-demand EC2 servers, and by 2008, anyone with a credit card could virtually set up a server in an instant. The ability to seamlessly scale up server capacity as demand increased was a game changer. Startups could begin with modest infrastructure and then expand as they became more profitable, thus minimizing initial investments and reducing the cost of innovation.
In 2008, Google followed suit with the Google App Engine (GAE), pioneering one of the first platforms as a service (PaaS). With GAE, developers could write a web application in PHP or Python and deploy it on Google’s public cloud, all without the need to manage server infrastructure. Despite GAE’s potential, it presented challenges for developers like me, accustomed to working with traditional applications and relational databases, due to its unfamiliar restrictions.
As the 2010s unfolded and cloud computing surged in popularity, companies with pricey on-premises data centers began eyeing their digital native competitors with envy. Companies like Netflix, Airbnb, and Slack. These newer entities, born in the cloud and deploying software on platforms like AWS, Google Cloud, and Microsoft Azure, were rapidly releasing competitive products without bearing the burdensome costs of maintaining a data center. They were also leveraging additional on-demand cloud services, including machine learning and AI, which offered unprecedented capabilities.
Established companies, rooted in traditional data center operations, found the allure of the cloud irresistible for several reasons, as shown in Figure 1-1.
Figure 1-1. Go faster, save money, and do more
These were typically:
Go faster
Enhance developer productivity by leveraging the cloud’s on-demand, scalable resources and a wide range of prebuilt services. This allows developers to focus on core application logic instead of infrastructure management.
Save money
Decrease infrastructure or operational costs by shifting from capital expenditure (CapEx) for hardware and maintenance to operational expenditure (OpEx) for on-demand services, improving cash flow and reducing upfront investments.
Do more
Access resources and services that are impractical in an on-premises setup, such as vast scalable storage options, powerful data analytics tools, machine learning platforms, and advanced AI services.
The critical misstep these organizations often make is migrating to the cloud without understanding its unique nature, the added complexity, and how it necessitates changes in software development practices. As a result, rather than enhancing efficiency and reducing costs, the cloud can sometimes introduce additional complications and expenses, thus slowing progress and increasing expenditure. Therefore, the frequently promised benefit of “run your mess for less” rarely materializes, underscoring the importance of a well-informed and strategic approach to cloud migration.