Enterprises developing Retrieval-Augmented Generation (RAG) systems must decide between VectorRAG and GraphRAG architectures, each with unique technical challenges that impact accuracy, scalability, and query complexity. VectorRAG, which relies on embedding-based similarity, offers efficient broad retrieval but struggles with structured multi-hop reasoning, while GraphRAG encodes entity relationships explicitly, enhancing logical consistency but requiring complex schema design. Hybrid architectures combining the two can balance broad recall with logical precision, albeit with a 150-200ms orchestration overhead for notable accuracy gains. VectorRAG faces challenges with re-embedding for real-time updates and polysemy in entity disambiguation, whereas GraphRAG supports incremental updates and uses surrounding context to resolve ambiguity, though at a higher memory cost. GraphRAG's strengths in relationship traversal make it particularly beneficial for industries like finance, healthcare, and supply chain, despite its higher implementation complexity. The choice between architectures should be guided by industry-specific needs and performance metrics like retrieval precision, recall@k, and latency, rather than solely theoretical advantages.