Continuous Availability: How It’s Changed, and Why It’s Critical
Scott Kelly | March 24, 2022

Remember when Slack went down in early January? The three-hour outage, set off by AWS capacity issues, cost the company an untold amount of money. And the effects rippled across the enterprise. The outage devalued the company’s stock and seemed to send all 142,000 of its customers to Twitter to gripe.

This high-profile outage is just the most recent of many outages highlighting the critical nature of continuous availability. And there’s only one answer to the problem.

AIOps (artificial intelligence for IT Operations) is essential for improving operational performance and system resiliency. But these AIOps tools must adapt as our digital world evolves — and not all vendors are keeping up.

The origin of continuous availability

Gartner invented the term “continuous availability” in 2012 as the business world started relying more heavily on IT infrastructures. Seeing how detrimental outages were to companies, Gartner coined the term to describe an approach that “focuses on eliminating the downtime required for standard maintenance tasks, but more importantly, removing any disruption to IT operations regardless of events that may occur.”

Of course, a lot has changed in the last decade.

Infrastructure environments grew increasingly large and complicated, and microservices and ephemeral architectures created even more layered and fragile IT systems. And as businesses rolled out innovations and updates at increasing velocity, it became much harder for them to guarantee uptime in these complex ecosystems.

Of course, over the past 10 years, society also has increased dependence on digital apps and services and decreased its tolerance for downtime. Customer-facing service outages put companies’ reputations at risk, compromising customer loyalty. Even internal outages pose risks with lost productivity and opportunities. It’s clear that continuous availability is becoming even more critical to business continuity and revenue growth as time goes on.

AIOps to the rescue

In 2016, Gartner once again coined a term to describe the evolving IT landscape. “AIOps” describes a solution that “combines big data and machine learning (ML) to automate IT operations processes, including event correlation, anomaly detection and causality determination.”

In other words, AIOps increases application availability and reduces downtime by detecting performance-affecting incidents, determining the reason behind the problem and helping engineering teams fix the issue. The platform automates this workflow for rapid response at scale.

A holistic AIOps solution is essential to managing uptime in today’s unwieldy IT ecosystems and providing continuous availability for the end-user. But advanced AIOps platforms are going even further.

Don’t just fix outages – prevent them

AIOps was born in the world of event data, traditionally monitoring infrastructures by focusing on changes in topology. But there’s a seismic shift brewing as more businesses migrate their IT assets to the cloud and operate like software companies to satiate consumers’ appetites for bigger and better digital apps. These digital-first companies need to shift from monitoring their infrastructures to monitoring their applications. After all, apps now determine the user experience.

Enterprises simply can’t afford to spot an incident after an app has crashed. Incidents must be detected faster and earlier in the incident lifecycle, and resolution must occur before impacting customers, partners or internal stakeholders.

This is exactly what next-generation AIOps tools do.

Advanced AIOps platforms converge all data — metrics, traces, logs, changes, and events — for rapid, accurate reporting and analysis. Unlike old, rules-based technologies, this method can operate on partial evidence and detect problems before they become critical. AIOps also uses ML to dissect incidents, understanding how to catch problems earlier in the incident lifecycle and identifying patterns that drive continuous availability.

Continuous availability — now and into the future

Surprisingly few vendors provide true AIOps solutions (just check their patent portfolios!), and even fewer offer the level of early incident detection that Moogsoft does.

Moogsoft was in the AIOps game before Gartner even invented the term and saw from the get-go that an event-only world was limited. So we added metrics, changes and logs to the mix to look across the entire IT ecosystem. With this approach, we routinely capture incidents before they impact the end-user.

Our patented technology (humble brag: we have more than 70 patents) sets us apart. We provide:

Early detection: metric and event data identify anomalies early in the lifecycle
Automated collaboration: automated workflows route, remediate and auto close incidents
Pattern identification: AI and ML technologies understand patterns and prevent them from happening again

Moogsoft’s depth of experience and patented technology make it the only AIOps vendor poised to provide global enterprises with continuous availability now and into the future.

Sound too good to be true? Sign up for a free trial of Moogsoft’s AIOps platform to try it for yourself! We’re so confident in our product that we won’t even ask you for a credit card.

Moogsoft is the AI-driven observability leader that provides intelligent monitoring solutions for smart DevOps. Moogsoft delivers the most advanced cloud-native, self-service platform for software engineers, developers and operators to instantly see everything, know what’s wrong and fix things faster.
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About the author


Scott Kelly

Scott is a Director of Product Marketing at Moogsoft. He has spent the bulk of his career in enterprise tech, with the last 7+ years focused on cloud-native technologies. Prior to Moogsoft, Scott worked in product marketing at VMware, Pivotal, and Dynatrace. As a problem solver and marketer, Scott is always looking for ways to reduce the friction in business and life. And, if you have a couple of hours, ask him about CrossFit.

All Posts by Scott Kelly

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