10 best practices for optimizing generative and agentic AI costs
As enterprises scale initiatives, the cost of developing, deploying and operating generative artificial intelligence models rises significantly. The shift toward AI agents can further increase costs becausse of poor architecture, limited operational maturity and weak governance. Information technology leaders can adopt these 10 best practices for optimizing costs, enabling them to achieve quicker business value [...
Key Takeaways
- SiliconANGLE UPDATED 14:31 EDT / JUNE 14 2026 AI 10 best practices for optimizing generative and agentic AI costs GUEST COLUMN by Arun Chandrasekaran As enterprises scale initiatives, the cost of developing, deploying and operating generative artificial intelligence models rises significantly.
Information technology leaders can adopt these 10 best practices for optimizing costs, enabling them to achieve quicker business value and operational efficiency: 1.
- Additionally, most application programming interface providers charge for input and output tokens separately, while some charge based on the number of characters.
Normalizing these pricing models for a given use case enables an apples-to-apples comparison.
- Balance upfront and operational costs in model augmentation and customization When customizing gen AI models, IT leaders must balance upfront investments, such as prompt engineering, retrieval-augmented generation and fine-tuning, with ongoing inference costs.
Running costs can be optimized by effective context engineering or even by efficiently fine-tuning a model on a specific dataset through instruction tuning or continued pretraining.
- IT leaders must be aware of the potential tradeoffs, as the list of cost drivers for self-hosting is extensive.
The most underestimated cost is the specialized talent required to operate gen AI at scale.
- IT leaders will need to evaluate the real productivity impact of AI features, negotiate transparent cost attribution and avoid enterprise-wide upgrades without proven return on investment.
Stats & Key Facts
- #SiliconANGLE UPDATED 14:31 EDT / JUNE 14 2026 AI 10 best practices for optimizing generative and agentic AI costs GUEST COLUMN by Arun Chandrasekaran As enterprises scale initiatives, the cost of developing, deploying and operating generative artificial intelligence models rises significantly.
SiliconANGLE UPDATED 14:31 EDT / JUNE 14 2026 AI 10 best practices for optimizing generative and agentic AI costs GUEST COLUMN by Arun Chandrasekaran As enterprises scale initiatives, the cost of developing, deploying and operating generative artificial intelligence models rises significantly. The shift toward AI agents can further increase costs becausse of poor architecture, limited operational maturity and weak governance. Information technology leaders can adopt these 10 best practices for optimizing costs, enabling them to achieve quicker business value and operational efficiency: 1.
Be objective about model accuracy, performance and cost tradeoffs For IT leaders, selecting the right model requires balancing accuracy, performance and cost. IT leaders must be objective on the tradeoffs among accuracy, performance and costs. A tailored approach can deliver better performance and lower inference costs.
Additionally, most application programming interface providers charge for input and output tokens separately, while some charge based on the number of characters. Normalizing these pricing models for a given use case enables an apples-to-apples comparison. Lastly, IT leaders should run extended pilots to vet their total cost of ownership assumptions and uncover any surprises or hidden costs.
For more details please read the original article at SiliconANGLE AI.
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