Beyond traditional DB
As of mid-2025, database options useful for developers such as Postgres, Mongodb, Elasticsearch provide vector support. Microsoft's SQL Server has added native vector data types for storage, similar to AWS using Amazon S3 vectors. So, if these add-ons already exist, why use a specialized vector native database?
Well, special vector databases provide better information retrieval mechanisms than typical databases, increasing the speed and accuracy of AI agents being able to infer on data. Calvesbert at IBM explains: “A suitable vector database provides greater flexibility by combining multiple vector fields of dense, sparse, and multimodal searches (spenning text, images, audio) to capture the full context and specific terms of the most comprehensive search results.”
Vector-Native databases are likely to be suitable for high-end scenarios, so they do have less tweaks. “It requires organizations that deal with billions of vectors that require sub-50 millisecond latency, or special features such as multimodal search, and benefits from native vector databases,” said Janakiram MSV, principal analyst at industry analyst and consulting firm Janakiram & Associates. In contrast, traditional databases require extensive tuning and lack optimized performance for high-scale vector manipulation, he adds.
