Towards AI•1 day ago
You probably don’t need a Vector Database (Yet) for your RAG
Last Updated on February 21, 2026 by Editorial Team Author(s): Thomas Reid Originally published on Towards AI. Numpy and/or SciKit-Learn might meet all your retrieval needs Right now, off the back of Retrieval Augmented Generation (RAG), vector databases are getting a lot of attention in the AI world. Image by Nano BananaThe article discusses the emerging popularity of vector databases in the AI landscape, particularly in the context of Retrieval Augmented Generation (RAG). It argues that while these tools are crucial for large-scale enterprise applications with extensive vector data, smaller-scale implementations might not require the complexity of a dedicated vector database. Instead, NumPy and SciKit-Learn are highlighted as viable alternatives for building retrieval systems that can efficiently handle smaller data volumes, enabling fast search operations without the added latency and costs of vector databases. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI