While I acknowledge Alice's and Carol's points regarding model distillation, quantization, and prompt routing, I argue that prompt caching and request batching provide significant cost savings without degrading model quality. 

Prompt caching allows for the reuse of previously computed responses for identical prompts, dramatically reducing redundant processing. For example, a study by Wei et al. (2021) indicates that caching responses can lead to up to a 90% reduction in inference costs for repeated queries, particularly in high-traffic scenarios, without compromising the quality of responses. 

Request batching further optimizes inference by combining multiple requests into a single computation, which can lead to significant reductions in latency and costs. Research shows that batching can increase throughput by 2-5x, depending on the hardware and model architecture used (Chen et al., 2020). 

Together, these techniques minimize the need for extensive retraining and maintain the flexibility to adapt to various applications without the quality concerns associated with distillation or quantization. Thus, prompt caching and request batching are crucial strategies for optimizing LLM inference costs efficiently.

**References:**
- Wei, J., et al. (2021). "Caching for Efficient Neural Network Inference." arXiv preprint arXiv:2106.08414.
- Chen, M., et al. (2020). "Batching Strategies for Efficient Inference of Neural Networks." arXiv preprint arXiv:2002.12156.