Choosing a Database for Enterprise RAG: PostgreSQL, Azure SQL or a Vector Database

When you choose a database for an enterprise RAG (Retrieval-Augmented Generation) system, the real question isn’t “which vector database?” but “where does the enterprise data live, and who can access what?” For most organizations, the best start is to add vector search to the PostgreSQL (pgvector), SQL Server or Azure SQL database where the data already lives. A separate vector database or search engine should come into play only with very large scale, many data sources or advanced search needs.

This guide is for IT managers and architects planning to connect enterprise documents and databases to AI assistants. We compare the technology options by scale, security, data protection and operating cost.

The Options at a Glance

The Layers of a RAG System

The database is only one layer of a RAG architecture. To make the right decision, you need to see the whole flow:

Decision Criteria

1. Where does the data live?

If most of the data is already in a relational database (ERP, CRM, product catalog, support tickets), keeping the vectors in the same database largely removes synchronization, consistency and permission problems. If the sources are very varied (file servers, email, wiki pages, several databases), a central search layer may fit better.

2. Scale

3. Access control

This is the most critical and most often neglected requirement of enterprise RAG. Users must see only chunks of content they’re authorized for. In relational databases, Row-Level Security can apply this filter directly to the search query. If you use a separate vector system, the permissions in the source systems must be carried over to it accurately and kept current.

4. Data protection and data location

In organizations with strict data location requirements, running the language model and embedding model in-house and keeping every RAG layer in their own data center is an increasingly common option. In that case PostgreSQL or SQL Server 2025 running in-house is a natural database choice.

5. Operating cost

Every new system means setup, backups, monitoring, security patches, permission management and expertise. Using your existing database platform lets the operating processes your team already knows apply to RAG too.

Example: Vector Search with pgvector

In PostgreSQL, the pgvector extension stores vectors in the same table as business data and runs fast approximate search with an HNSW index. The example below uses three-dimensional vectors to show the idea:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE doc_chunks (
    id         bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    department text NOT NULL,
    content    text NOT NULL,
    embedding  vector(3) NOT NULL
);

INSERT INTO doc_chunks (department, content, embedding) VALUES
  ('it',      'Backup policy',          '[0.9, 0.1, 0.0]'),
  ('it',      'Restore procedure',      '[0.8, 0.2, 0.1]'),
  ('finance', 'Budget approval process', '[0.0, 0.2, 0.9]');

CREATE INDEX ON doc_chunks USING hnsw (embedding vector_cosine_ops);

-- Nearest chunks by cosine distance, only within the user's department
SELECT content, embedding <=> '[0.85, 0.15, 0.05]' AS distance
FROM doc_chunks
WHERE department = 'it'
ORDER BY embedding <=> '[0.85, 0.15, 0.05]'
LIMIT 2;

The department filter in the example stands for a simple access control. In real systems, enforce this filter at database level with a Row-Level Security policy. We show the SQL Server and Azure SQL equivalent in our vector search in Azure SQL post.

Decision Tree

1. Do you have a data location requirement? If so, limit yourself to options where every layer (the model included) can run in a suitable location.
2. Is the data mostly in one relational database? If so, start with that database’s vector support.
3. Does the chunk count exceed millions? If so, use a vector index and measure performance.
4. Do you need many different sources and advanced full-text search? If so, consider a search layer with vector support.
5. Do measurements show the relational database isn’t enough? Only then move to a dedicated vector database.

Enterprise RAG with Aryasoft

With our experience in SQL Server, Azure SQL and PostgreSQL, Aryasoft designs the data layer of your RAG projects: which platform the data stays on, how vector search runs, how access control carries into search and how personal data is protected. The biggest risk we see in the field is RAG applications fed with unmasked data producing answers that cross permission boundaries, so we make security part of the architecture from day one. For details, see our data analytics and AI services, and our data governance post for the governance side.

Frequently Asked Questions

Sources

The technical details in this guide were checked against the following sources in November 2026.

1. pgvector
2. PostgreSQL documentation: Row Security Policies
3. Microsoft Learn: Vector Search and Vector Index
4. Microsoft Learn: Row-level security
5. Microsoft Learn: Retrieval Augmented Generation overview