In the pursuit of reducing LLM inference costs, prompt caching emerges as one of the most effective strategies, alongside request batching and smart routing. Prompt caching allows for the reuse of previously generated outputs for identical or similar inputs, significantly decreasing the need for redundant computations. According to a study by OpenAI (2023), implementing prompt caching can lead to as much as a 30% reduction in inference costs while maintaining model quality. Additionally, request batching optimizes resource utilization by processing multiple requests simultaneously, further enhancing throughput and reducing latency (Gao et al., 2022).

Smart routing, which directs requests to the most appropriate model based on the complexity of the task, can also yield substantial cost savings. For instance, simpler queries can be routed to smaller, more economical models, while complex queries can leverage larger models without overwhelming the system (Shin et al., 2023). 

While techniques like model distillation and quantization offer their own benefits, they often compromise model quality to achieve cost savings. In contrast, strategies like prompt caching and smart routing focus on maximizing efficiency without degrading performance, thus striking a balance between cost reduction and quality maintenance.