GraphRAG vs. Vector RAG: The Architecture Decision Teams Make Too Late
Most teams discover they need GraphRAG six months too late — after they've already explained to users why the AI got the relationship wrong, why it confused two entities that share similar embeddings, or why it confidently cited a document that contradicts the actual answer. Vector RAG is genuinely good at what it does. The problem is that teams treat it as good at everything, and keep piling on retrieval hacks when the underlying architecture has hit a mathematical ceiling.
Fewer than 15% of enterprises have deployed graph-based retrieval in production as of 2025. This is not because the technology is immature. It's because the failure signals for vector-only RAG are subtle: the system runs, the LLM responds, and only careful inspection reveals that the retrieved context was plausible but wrong.
