The Sequence Opinion #537: The Rise and Fall of Vector Databases in the AI Era
Once regarded as a super hot category, now its becoming increasingly commoditized.
Hello readers, today we are going to discuss a really controversial thesis: how vector DBs become one of the most hyped trends in AI just to fall out of favor in a few months.
In this new gen AI era, few technologies have experienced a surge in interest and scrutiny quite like vector databases. Designed to store and retrieve high-dimensional vector embeddings—numerical representations of text, images, and other unstructured data—vector databases promised to underpin the next generation of intelligent applications. Their relevance soared following the release of ChatGPT in late 2022, when developers scrambled to build AI-native systems powered by retrieval-augmented generation (RAG) and semantic search.
This essay examines the meteoric rise and subsequent repositioning of vector databases. We delve into the emergence of open-source and commercial offerings, their technical strengths and limitations, and the influence of traditional database vendors entering the space. Finally, we contrast the trajectory of vector databases with the lasting success of the NoSQL movement to better understand why vector databases, despite their value, struggled to sustain their standalone identity.