GigaOm Radar for AIOpsv1.0

1. Summary

This report explores the nascent AIOps landscape and looks at current, emerging, and future solutions and approaches that will impact enterprises that leverage complex cloud computing architectures, such as multi-cloud. It also provides insight into the emerging providers in this dynamic market space.

There are two major categories of AIOps players. One consists of traditional, on-premises operations tools that have had an AI engine bolted on to enable more proactive and predictive ops automation. These are typically provided by brand name players, such as IBM, CA, and EMC/Dell. The second consists of tools purposely built for AIOps, such as those provided by startups that have emerged in the last several years (Moogsoft, for instance).

As discussed in the AIOps Key Criteria report, it can be difficult to pin down exactly what is meant by an AIOps tool, Upon reviewing the tools listed below, what is apparent is that when it comes to AIOps, there is more than one way to skin a cat. A few solutions have deep AI functions built in, while others are equipped with medium or low functionality. Some products leverage agents to monitor remote systems. while others are agent-less. And some vendors provide static methods of ops monitoring interfaces and can seem overly complex, while others offer streamlined ops monitoring interfaces that can border on being overly simple.

So, how do you choose?

As with any significant IT decision, understanding your requirements is key. You need visibility into your ops environment, including things like the mix of on-premises and cloud-based systems, databases, storage, networking, and the like, and you need to know how the ops team can leverage these AIOPs tools to maximize uptime. Ultimately, IT decision-makers must pick the best tool or tools for the mix of technologies they operate, leveraging core features, such as AI integration and self-healing, that they feel are critical.