Why it matters for your business
The vector database decides how fast and how accurately an AI assistant finds relevant content, and how well access controls carry over. Metadata filters, such as department, client or document date, are often as important as similarity scores, because they keep one client's files out of another client's answers. Many small and mid-sized deployments do not need a separate vector service; adding vector search to an existing Postgres database keeps data in one place and reduces moving parts. Dedicated services become more attractive at larger scale or with heavy query loads.
Example
An accounting firm builds an internal assistant over client engagement files. Each document chunk is stored in Postgres with pgvector and tagged with client ID, year and document type. When a staff member asks a question while working on one client, the query filters to that client's records before similarity search, so other clients' documents are never retrieved.