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What are the cost considerations when buying AIOps tools?
AIOps tools can reduce overhead for IT staff, but first, enterprises must decide how they will use the tool to know which features to budget for.
As AIOps tools add functionality for tasks outside of IT operations, more organizations will look to incorporate them into their tool sets.
But how much should a company expect to pay when buying an AIOps tool? The answer varies, depending on these three factors.
1. Breadth of functionality
AIOps is a broad term that can mean different things to different vendors. Some self-described AIOps tools are conventional application performance monitoring (APM) suites that use machine learning algorithms to help interpret problems. Many APM platforms already did this before AIOps came on the scene as a buzzword; in these cases, AIOps is simply a label vendors attached to existing tool sets. Other tools apply AI to additional tasks, such as automatic problem remediation or proactive issue prediction.
When buying AIOps tools, expect to pay more for platforms that provide more than basic data analytics. Tools that only offer classic APM features should cost less than those that apply AI for more advanced functionality.
2. Range of supported use cases
Initially, AIOps tools catered to the needs of IT operations teams exclusively, enabling faster and more efficient ways to manage workflows -- again, like APM.
Increasingly, however, AIOps expands to meet other needs, such as determining the full effect of an IT disruption on the business. This capability enables an IT team to triage incidents accordingly. AIOps can also facilitate tasks that span IT and the business, such as cost and capacity planning.
AIOps tools specific to IT ops teams don't necessarily cost more, or less, than tools that help with business management and other tasks. However, pricing can vary to reflect the value an AIOps tool provides across an organization.
3. Data-agnostic vs. domain-centric
Some AIOps tools can work with any type of data. These are called data-agnostic tools, and they apply to a wide variety of use cases -- from APM to the analysis of business or marketing data.
Other, domain-centric tools work only with certain types of data and in certain use cases. AIOps platforms that cater to APM are a classic example of this type of tool. Other domain-centric AIOps tools might focus on security operations or cloud capacity management, for example.
In general, data-agnostic AIOps tools that support a variety of use cases cost more than those that work only within a specific domain. But data-agnostic tools might not offer all of the dedicated features and functionality that a team needs when operating within a specific domain. Thus, if your organization has a narrow focus and finite needs, consider buying AIOps tools that are domain-specific -- they'll offer the most value, even at a lower price tag.
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