Platform as a Service (PaaS) emerged. As a leading force in the ever-evolving quest to simplify software development. PaaS dates back to 2006 with Force.com, followed by Heroku, AWS Elastic Beanstalk, and DotCloud, which later turned to Docker.
While the PaaS sector is very demanding A market worth $170 billion Given their involvement in the cloud industry, companies still struggle with manual deployment and workload lifecycle management today. So why isn’t Platform as a Service being more widely adopted?
Providing a PaaS experience across all workloads
PaaS platforms can be more diverse, and I’m not talking about language and framework compatibility. While PaaS is often defined as a one-stop-shop for deploying any application, there is a catch. By applications, what is usually referred to here are 12-factor applications.
However, many workloads don’t exactly fit the mold of typical web applications; They come with unique requirements, such as batch processing tasks, high-performance computing (HPC) workloads, GPU-intensive tasks, data-centric applications, or even quantum computing workloads.
I won’t go into all the benefits that PaaS offers. However, companies need to manage all their workloads in the easiest way possible, and abstracting their deployment and management is the ideal solution.
A transformation is needed. First, companies adopting the PaaS model must realize that there will not be a one-size-fits-all workload solution. In a recent speech on the topic, former Google engineer Kelsey Hightower emphasized this idea that a single, all-encompassing PaaS Still unlikely.
Businesses adopting the PaaS model must realize that there will not be a one-size-fits-all solution for workloads.
As used Workload API To designate a tool that provides that seamless “this is my app, run it for me” experience. I like the term “workload API” because it clearly states the intent: to run a specific workload. Compared to Platform as a Service (PaaS), which needs to be more precise and leads to this confusion that PaaS is the silver bullet to make anything work. I will use this term for the rest of the article.
The second change for companies that want to provide a seamless deployment and management experience for all their workloads is to consider that each workload must have its own workload API. For example, Amazon lambda Can be used for batch jobs, So he sends For the front end, Vertex Artificial Intelligence For machine learning models, and Coryvi For web applications.
Now, let’s explore how to choose workload APIs.