Optimize AI Costs: 10 Best Practices for Enterprises

Enterprises are facing significantly rising costs when developing, deploying, and operating generative artificial intelligence models. This trend is amplified by the shift toward AI agents, often due to poor architecture, limited operational maturity, and weak governance. Here's the thing: Information technology leaders can adopt 10 best practices to optimize these costs. This allows them to achieve quicker business value and operational efficiency. One key practice is being objective about model accuracy, performance, and cost tradeoffs. This means balancing these factors when selecting the right model. A tailored approach can deliver better performance and lower inference costs. What's interesting is that most API providers charge for input and output tokens separately, or based on the number of characters. Normalizing these pricing models helps with direct comparisons. Another practice involves creating an AI model sandbox for safe experimentation, promoting model choice and price transparency. IT leaders should also balance upfront and operational costs when augmenting and customizing models. They should consider augmentation and customizations sequentially, only moving to more advanced approaches if simpler ones don't meet quality requirements. The bottom line: Understanding and implementing these practices can help organizations control their AI spending and improve overall efficiency.