Knowledge graph vs vector database: how to choose your AI foundation

What this post added

This post defines knowledge graphs and vector databases, comparing their strengths and weaknesses for AI applications. It explains how knowledge graphs excel at structure, relationships, explainability, and governance, while vector databases are strong in semantic search across unstructured content and fast prototyping. The post advocates for a hybrid approach, suggesting that combining both provides a more comprehensive foundation for enterprise AI by leveraging the explicit structure of graphs with the semantic understanding of vectors. It outlines specific workflow scenarios where each approach is beneficial and how they can work together.