From model routing and semantic caching to quantization and intelligent batching, these are the techniques we use to reduce inference costs by up to 70%.
Inference costs can scale rapidly when a language model is used in high-volume production. Multiple strategies exist to optimize these costs without sacrificing quality: model routing directs simple queries to economical models and reserves expensive ones for complex tasks, semantic caching avoids re-processing similar queries, and quantization reduces model size while maintaining quality. By combining these techniques, we've achieved cost reductions of up to 70% in production projects.