SRE.ai this week revealed plans to build a DevOps platform for deploying custom software across multiple software-as-a-service (SaaS) application platforms.
Fresh off raising $7.2 million in funding, SRE.ai CEO Raj Kadiyala said as the volume of code developed using low-code/no-code and, more recently, artificial intelligence (AI) tools increases, there needs to be a more automated approach to deploying that software based on best DevOps processes that also enables software development teams to monitor and observe those applications.
At the core of that effort is a platform that will leverage artificial intelligence (AI) technologies to automate the deployment of these applications that are already being developed at a pace that will overwhelm many existing DevOps workflows.
At the same time, organizations are also looking to centralize the management of custom extensions to SaaS application platforms versus having to build and maintain separate niche DevOps platforms for each SaaS application environment. Much of the software development effort that occurs across those platforms is similar enough to automate using a single platform as part of an effort to reduce the overall amount of tooling sprawl, said Kadiyala.
In effect, SRE.ai acts as a translation layer between business-critical platforms, developer workflows, and IT governance to automate DevOps workflows at scale in a way that is easily accessible to any developer regardless of their software engineering expertise, he added.
It’s still early days so far as usage of AI to automate the backend of software development workflows is concerned, but as more applications are built using AI agents and vibecoding tools, it may only be a matter of time before the teams currently tasked with deploying custom software on SaaS application platforms are overwhelmed.
In fact, many of the individuals currently deploying that software are administrators with little software engineering experience. As a result, many of those applications are not thoroughly tested for usability and scalability before they are deployed. More challenging still, much of that software is likely to contain known vulnerabilities that cybercriminals are likely to discover and easily exploit.
It’s not clear how much custom software is now being deployed on SaaS applications versus applications that are built from the ground up using best DevOps practices, but increasingly professional developers and software engineers are being asked to fix applications that were originally built by so-called citizen developers. Hopefully, as DevOps workflows continue to evolve those teams will work more collaboratively together as applications are built versus continually looking to professional developers to fix issues after an application has been deployed.
The next major challenge, of course, will be getting teams currently building custom SaaS applications to give up bespoke tools in favor of a more integrated platform. However, the one thing that everyone involved in the building of those applications can agree on is that nobody wants to be asked to fix an application in the wee hours of the morning when most of these issues seem to inevitably arise, in the absence of any AI tools to help them, they are not likely to understand how it was actually constructed in the first place.
