While I acknowledge the merits of prompt caching and smart routing as effective strategies, I maintain that model-level optimizations, specifically distillation and quantization, provide superior cost-per-quality tradeoffs in the long run. Distillation allows for the creation of smaller models that retain much of the performance of their larger counterparts, significantly reducing inference costs. For example, DistilBERT, a smaller version of BERT, achieves over 95% of BERT’s performance while being 60% faster and requiring 40% less memory (Sanh et al., 2019). 

Quantization further enhances efficiency by reducing the precision of the model weights, leading to smaller model sizes and faster computation without substantial quality loss. Research shows that quantized models can achieve performance levels only marginally lower than their full-precision counterparts—often within 1-2% (Jacob et al., 2018). 

While prompt caching and routing strategies are effective, they do not fundamentally reduce the model size or complexity, which limits their long-term scalability. Therefore, investing in distillation and quantization not only lowers costs significantly but also enhances the overall efficiency of LLM deployment in production. 

Sources:
- Sanh, V., et al. (2019). "DistilBERT, a distilled version of BERT: smaller, faster, cheaper, and lighter."
- Jacob, B., et al. (2018). "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference."