Vector databases are specialised systems designed to handle the complexities of unstructured data by storing information as high-dimensional numerical vectors or embeddings. This technology contrasts with traditional databases, excelling in similarity searches based on semantic meaning rather than exact matches. The synergy between vector databases and large language models (LLMs) is explored, highlighting how vector databases enhance LLM capabilities in tasks like semantic search and recommendation systems through efficient retrieval of relevant contextual information. Challenges such as scalability and indexing are discussed alongside best practices for integrating these databases into machine learning workflows, and a comparison of popular vector database technologies provides an overview of the current landscape and future trends in this evolving field. Finally, the importance of addressing security and privacy considerations within LLM applications leveraging vector databases is underscored.