Published: 06 June 2024
Generative AI costs can rapidly escalate due to poor architectural decisions, lack of operational know-how and inadequate governance. IT leaders can adopt these 10 best practices for optimizing costs, enabling them to achieve quicker business value and operational efficiency.
Generative AI (GenAI) model inference costs can rapidly scale as use cases expand and are further exacerbated by poor architectural choices, lack of monitoring tools and inadequate prompt engineering practices.
The sheer newness of GenAI offerings, and the diverse pricing and deployment models, make it hard for IT leaders to do an apples-to-apples comparison to determine the best choice ahead of them.
Most large enterprises will engage with multiple GenAI models providers, however, failing to make these models’ price, performance and accuracy transparent to developers and users may result in erroneous choices and escalating costs.
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Analysts:
Arun Chandrasekaran, Leinar Ramos, Alberto Pietrobon, Justin Tung
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