How Developers Can Cut AI Inference Costs by 50% Using Quantization and Caching

High inference costs from large language model APIs are squeezing margins for SaaS developers, with expenses potentially reaching $75,000 per month at scale. A technical approach combining model quantization, semantic caching, and smart routing can reportedly halve these costs without noticeable drops in output quality. Quantization reduces model weight precision from 32-bit to 8-bit or 4-bit integers, shrinking memory use by up to 75% while preserving response quality. Semantic caching, implemented via tools like Redis and sentence embeddings, identifies near-duplicate queries and serves stored responses, cutting costs by an estimated 30–60%. Developers can self-implement these strategies rather than relying on premium optimized services, according to the author's production experience.