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Past the normal DB
As of mid-2025, developer-favorite database choices equivalent to Postgres, MongoDB, and Elasticsearch have rolled in vector assist. Microsoft’s SQL Server has added a local vector knowledge sort for storage, as has AWS with Amazon S3 Vectors. So, why use a specialised, vector-native database if these add-ons exist already?
Properly, specialised vector databases present higher data retrieval mechanisms than typical databases, which improve the velocity and accuracy at which AI brokers can motive over knowledge. As IBM’s Calvesbert describes: “Match-for-purpose vector databases present larger flexibility combining a number of vector fields for dense, sparse, and multi-modal search—spanning textual content, pictures, and audio—to seize the complete context and particular phrases for probably the most complete search outcomes.”
Vector-native databases are additionally arguably a greater slot in high-scale eventualities, requiring fewer changes. “Organizations dealing with billions of vectors, requiring sub-50ms latency, or needing specialised options like multi-modal search, profit most from native vector databases,” says Janakiram MSV, principal analyst at Janakiram & Associates, an business analyst and consulting agency. In contrast, conventional databases require in depth tuning and lack optimized efficiency for high-scale vector operations, he provides.