Table of Contents
The importance of AIOps has increased in response to the rapid adoption of cloud and edge computing and the rising complexity these environments create. Intelligent tools act as a force multiplier for ops teams, helping them adapt to escalating demand even in the absence of budget and staff increases. AIOps also helps address the operational challenges of having cloud-based applications and data that must continue to operate with existing systems, such as mainframes, x86 clusters still crowding data centers, and increasingly complicated networks.
AIOps tools are growing in several directions. Most vendors in the traditional operations tools space have incorporated an AI engine and rebranded their tool with AIOps. Additionally, there is a cohort of startups that have developed purpose-built AIOps tools.
The development of hybrid AIOps tools follows the normalization of the market, leading to vendors combining technologies. Some vendors are buying their way into the AIOps space via acquisitions. In this scenario, vendors integrate traditional operational tools with AI technology, while new upstarts address a niche or add new features to the AIOps landscape. Finally, there are also larger cloud providers dipping their toes into the market. These providers are building tools that manage their native services and cross-cloud tools that manage multiple cloud platforms.
All these tools are data-oriented. They gather data from as many sources as possible, using their connectors and integration or even leveraging other instrumentation to connect with systems. Combining software has confused the AIOps market, as some tools focus only on data analysis and not how it’s collected. Others focus on collection and analysis but may not support complete awareness of the state of the enterprise
If that’s not confusing enough, we’ve also found AIOps tools take different approaches to how AIOps works. Approaches to the remediation of issues, integration with other cloud systems, security, governance, and even cost accountability make vendor selection more complex. The confusion multiplies when the term “AI” is used to describe a rules-based heuristic system with human supervision.
In contrast, others have a core AI module with true neural capabilities. This difference can determine whether a system can ingest a new data set with minimal human intervention or if it requires substantial effort to add new data to the system.
As we close in on the measure of a good AIOps tool, the “it depends” factor becomes important to understand. The complexity of the answer depends on the types of systems you’re looking to monitor and observe, the data storage in place, expectations (including supporting a customer experience), applications employed, and other operational systems such as security and governance. Thus, it’s less about selecting the best AIOps tool, and more about selecting a tool or tools that will meet your overall cloud and non-cloud operational needs in the near- and long-term.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.