What comes first – observability or AIOps? Can you achieve observability without AIOps? Do you need AIOps if you already have an observability solution in place?
These are all questions that any team considering AIOps will want to answer in order to determine the real-world value that AIOps tools stand to offer. In this article, we untangle these issues by discussing the relationship between observability and AIOps, as well as explaining how – despite being distinct concepts – they go inextricably hand-in-hand.
Over the past few years, observability has emerged as a leading buzzword among IT teams responsible for monitoring and managing applications. While there’s some debate about what, exactly, observability means and how it’s different from monitoring, the general consensus is that monitoring is about merely watching systems and collecting data, while observability focuses on using that data to answer important questions about the state of a system.
The conversation surrounding observability has pushed teams to rethink the way they approach monitoring. Rather than endeavoring to know simply what is happening within an application, which is the purpose of monitoring, engineers have shifted to a strategy that focuses on explaining why applications behave the way they do.
AIOps, meanwhile, is the use of AI and machine learning to help address challenges faced by IT teams. AIOps can help engineers do things like find the root cause of complex application performance problems or automatically remediate infrastructure failures.
Like observability, AIOps has helped to usher in new modes of thinking about IT operations in recent years. Although conversations about automation and machine learning certainly predate AIOps, AIOps has helped IT teams realize just how much they can gain by leveraging AI to automate some of the most tedious and time-consuming aspects of their own workflows.
AIOps and observability are closely related concepts. Since they both focus, at least in part, on helping IT teams understand what is happening within complex systems, the terms can appear to overlap. (Indeed, some analysts go so far as to claim that vendors purposefully conflate the terms.)
Nonetheless, at the end of the day, AIOps and observability are distinct concepts and technological categories in several important respects:
To put this another way, observability and AIOps serve different purposes and work in different ways. You can have one without the other.
That said, just because you can achieve observability without AIOps, and you can use AIOps to pursue goals other than observability, doesn’t mean you should.
When paired together, AIOps and observability tools double down on the value that each type of solution offers on its own. Observability can provide engineers with insights that AIOps tools may not reveal, such as mapping application behavior to specific parts of the codebase. Likewise, AIOps offers value that observability doesn’t, like the ability to respond automatically to problems that are surfaced from application performance data.
In this respect, AIOps extends observability and helps to translate it into action. By using AIOps and observability tools at the same time, teams gain maximum visibility into every stage of the software delivery chain, as well as a deep understanding of issues and their impact.
To think in terms of “AIOps vs. observability” is to miss the point of both concepts. While AIOps and observability work in somewhat different ways and support different needs, they vitally reinforce one another. You can implement one type of solution without the other, but in most cases, you shouldn’t.