The global RAG market was valued at $1,042.7 million in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 44.7% through 2030. RAG combines two processes: retrieving relevant information from external sources and using generative AI to create responses tailored to specific queries. Building, running, and scaling a RAG system comes with costs, including embedding, data storage and retrieval, LLM inference, infrastructure, and operational expenses. The Zilliz RAG Cost Calculator is a free tool that provides a clear cost breakdown, customizable parameters, scenario simulation, user-friendly design, support for multiple embedding models, and limitations such as focus on text-based data, limited scope, and excluding other costs like system maintenance. Understanding key cost factors, including cloud infrastructure, model usage, data volume and scaling, latency requirements, operational costs, and strategies for optimization can help plan effectively and make the most of investment in RAG-based solutions. Optimizing storage, reducing inference costs, efficient queries, right infrastructure, and hybrid approaches can significantly lower costs while maintaining efficiency, scalability, and performance. The Zilliz Cloud offers tailored optimizations for vector operations, potentially saving up to 50x on RAG costs.