Large Language Model (LLM) technology has transformed natural language processing, especially the development of retrieval-augmented generation systems. Complementing the use of cloud API-based systems, this work investigates the practical effects that LLM-based RAG systems have when deployed locally, with a focus on scalability, system performance, and data privacy. We created a local LLM RAG prototype using Lang Chain for pipeline orchestration, (Facebook AI Similarity Search) FAISS for vector retrieval, and Ollama for local LLM deployment. Our analysis of the offline deployment approach highlights its potential to lower the privacy risks related to cloud apps. To help a company select the deployment alternatives that best fit its unique requirements, a decision-making approach has been presented. Additionally, this paper offers strategies for on-premises LLM RAG system deployment and offers helpful guidance on how to best balance performance efficiency and data privacy.