AI

Zenoss Recognized in 2022 Gartner® Market Guide for AIOps Platforms

Zenoss Inc., a leader in AI-driven full-stack monitoring, today announced it was named a Representative Vendor in the 2022 Gartner Market Guide for AIOps Platforms [1]. According to the report: “AIOps platforms enable decision making across design, deploy, execute and operate activities by automated contextualization of large, varied volumes of operational data. I&O leaders should use AIOps platforms to analyze and share application life cycle insights, making digital business observable.”

A Gartner Market Guide defines an emerging market and explains what clients can expect it to do in the short term, outlining the attributes of Representative Vendor offerings and how those offerings are likely to evolve.

Among the key findings in the document, Gartner states: “Enterprises are replacing some traditional monitoring tool categories by embedding them within AIOps platforms. For example, virtual network monitoring, observability and infrastructure as a service (IaaS) monitoring are being done entirely within AIOps platforms, especially if the enterprise has its entire IT footprint in the cloud.”

Zenoss Cloud is the first SaaS-based intelligent IT operations management platform that collects all machine data, uniquely enabling the emergence of context for preventing service disruptions in complex, modern IT environments. Zenoss Cloud leverages the most powerful machine learning and real-time analytics of streaming data to deliver AIOps, full-stack monitoring, and observability, giving customers the ability to reduce the risk of modernizing their businesses.

“Modern IT environments can no longer be managed with traditional monitoring tools,” said Ani Gujrathi, chief technology officer at Zenoss. “The scale and complexity of these environments requires AIOps combined with unified monitoring and observability — and Zenoss Cloud delivers all of this in a single solution.”

[1] Gartner, Inc. Market Guide for AIOps Platforms by Pankaj Prasad, Padraig Byrne, and Gregg Siegfried, 2022.

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