Show HN: Built AI-Gateway reverse proxy to reduce LLM API costs and token burn (github.com)

A new tool called AI-Gateway has emerged, designed to significantly reduce the costs associated with using Large Language Model (LLM) APIs by implementing a semantic caching layer. This reverse proxy allows applications to store and retrieve past question responses, effectively reducing redundant API calls. For instance, repeated queries such as "What is RAG?" can lead to up to 70% savings on API bills by turning multiple identical requests into a single call. Deployment is quick, requiring as little as 30 seconds, enhancing accessibility for developers without extensive technical skills. The significance of AI-Gateway lies in its potential to democratize AI applications by minimizing operational costs, particularly for businesses that rely on repetitive queries in environments like customer support. It features advanced caching techniques, including exact, template, and semantic matches, alongside a robust architecture that ensures high availability and performance with options for auto-scaling and rate limiting. The tool can dramatically reduce API usage from millions of calls per month to under a hundred, translating to savings of nearly $500, making it an attractive solution for startups and enterprises alike engaged in AI development.