The article discusses the growing importance of vector databases and engines in the data landscape, particularly for AI applications. It highlights the differences between specialized vector solutions like Pinecone and Weaviate versus traditional databases with vector capabilities, while addressing their integration into existing data engineering frameworks. Key considerations for choosing between vector engines and databases are also examined, as well as the evolving technology landscape driven by AI demands.

Celebrating two years at Weaviate, the author reflects on key insights about vector databases, emphasizing the importance of starting with traditional keyword search, understanding the nuances of vector search, and recognizing the interplay between vector databases and large language models. The article also addresses common misconceptions and offers practical advice on embedding models and search strategies.