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A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?
Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
Explanation of Options:
Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
Option B is ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.