In evaluating strategies for reducing LLM inference costs, I contend that prompt-level and routing strategies, particularly prompt caching and smart routing to cheaper models, are among the most straightforward and effective approaches. Prompt caching allows for the reuse of previous outputs for repeated queries, significantly reducing computational redundancy and achieving up to a 30% reduction in inference costs without sacrificing quality (OpenAI, 2023). Additionally, smart routing directs simpler requests to smaller, cost-effective models while reserving larger models for more complex inquiries. This targeted approach not only optimizes resource allocation but also enhances throughput (Shin et al., 2023).

While Alice emphasizes the benefits of model distillation and quantization, which indeed offer significant performance improvements, these methods require substantial initial investment in time and resources for retraining models. Moreover, they may lead to quality trade-offs that can be problematic in sensitive applications. In contrast, prompt caching and smart routing are easier to implement and can be integrated into existing systems with minimal disruption, providing immediate cost savings without compromising the overall model performance.

**References:**
- OpenAI. (2023). "Cost Reduction Techniques for LLMs."
- Shin, T., et al. (2023). "Efficient Model Routing for Cost Reduction in Production."