{"id":16959,"date":"2026-10-13T09:43:31","date_gmt":"2026-10-13T16:43:31","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=article&p=16959"},"modified":"2026-10-13T09:43:32","modified_gmt":"2026-10-13T16:43:32","slug":"do-we-really-need-specialized-vector-databases-for-vector-stores","status":"publish","type":"article","link":"https:\/\/www.pingcap.com\/ko\/article\/do-we-really-need-specialized-vector-databases-for-vector-stores\/","title":{"rendered":"Do we Really Need Specialized Vector Databases for Vector Stores?"},"content":{"rendered":"

A RAG prototype usually starts with an in-memory vector store that lives inside the application process. When the prototype heads to production, a common next step is to add a standalone vector database next to the existing application database, plus a job that copies embeddings and metadata between the two. That second system brings its own scaling, backups, access control, and sync failures. The vector store vs vector database question decides whether you take on that second system at all.<\/p>\n\n\n\n

This guide separates the two terms, lists the cases where a specialized vector database is worth it, and shows how a SQL database such as \ud2f0DB<\/a> can sit behind the same vector store interface.<\/p>\n\n\n\n

<\/span>What Is a Vector Store?<\/strong><\/span><\/h2>\n\n\n\n

A vector store is the layer that holds embeddings and returns the ones closest to a query vector. The term carries two meanings. In storage terms, it is the component that persists vectors and runs similarity search, whether that is an in-memory library, an extension inside a database, or a standalone service. In framework terms, LangChain and LlamaIndex each define a vector store interface that your RAG code calls, and many backends implement it.<\/p>\n\n\n\n

The framework meaning is the one that matters for production planning. Your application code talks to the interface, so the real decision is which backend sits behind it and what else that backend has to do.<\/p>\n\n\n\n

<\/span>Vector Store vs. Vector Database: The Difference<\/strong><\/span><\/h2>\n\n\n\n

A vector database is a full database system built around vector storage, adding durability, indexing, metadata filtering, and operational tooling to the core store. Every vector database contains a vector store, but not every vector store is a database.<\/p>\n\n\n\n

\ucc28\uc6d0<\/th>Vector store (library or in-process)<\/th>Standalone vector database<\/th>SQL database with vector support<\/th><\/tr><\/thead>
Examples<\/td>FAISS, in-memory Chroma<\/td>Pinecone, Weaviate, Qdrant, Milvus<\/td>PostgreSQL with pgvector, TiDB<\/td><\/tr>
Durability<\/td>Often none, or file snapshots<\/td>Built in<\/td>Built in<\/td><\/tr>
Metadata filtering<\/td>Basic, in application code<\/td>Vendor-specific filter syntax<\/td>Full SQL WHERE and joins<\/td><\/tr>
Transactions with app data<\/td>No<\/td>No; requires a sync job<\/td>Yes, same database<\/td><\/tr>
Scaling<\/td>Bounded by one process<\/td>Scales the vector workload<\/td>pgvector scales with its PostgreSQL instance; TiDB scales out storage nodes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Image retrieval shows the difference in practice. A library can find visually similar images for a demo, but a production catalog also needs stock, price, and region filters, which live in the application database. An e-commerce recommendation engine has the same shape: the similarity search is the easy part, and keeping it consistent with inventory is the hard part.<\/p>\n\n\n\n

For the broader comparison of vector databases against relational systems, see vector database vs traditional database<\/a>.<\/p>\n\n\n\n

<\/span>When a Specialized Vector Database Makes Sense<\/strong><\/span><\/h2>\n\n\n\n

A standalone vector database earns its operational cost when vectors are the workload rather than one feature of it. That usually means one of these:<\/p>\n\n\n\n