Beyond the traditional DB
As of mid-2025, developer-favorite database options such as Postgres, MongoDB, and Elasticsearch have rolled in vector support. Microsoft’s SQL Server has added a native vector data type for storage, as has AWS with Amazon S3 Vectors. So, why use a specialized, vector-native database if these add-ons already exist?
Well, specialized vector databases provide better information retrieval mechanisms than typical databases, which enhance the speed and accuracy at which AI agents can reason over data. As IBM’s Calvesbert describes: “Fit-for-purpose vector databases provide greater flexibility combining multiple vector fields for dense, sparse, and multi-modal search—spanning text, images, and audio—to capture the full context and specific terms for the most comprehensive search results.”
Vector-native databases are also arguably a better fit in high-scale scenarios, requiring fewer adjustments. “Organizations handling billions of vectors, requiring sub-50ms latency, or needing specialized features like multi-modal search, benefit most from native vector databases,” says Janakiram MSV, principal analyst at Janakiram & Associates, an industry analyst and consulting firm. By contrast, traditional databases require extensive tuning and lack optimized performance for high-scale vector operations, he adds.
