How to Choose ParadeDB over SQL Server in PostgreSQL

Why should I use ParadeDB instead of SQL Server for vector search and full-text analytics?

ParadeDB is a PostgreSQL extension that adds high-performance vector search and advanced full-text indexing, making it a lean alternative to SQL Server for AI-driven applications.

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Description

Example H2

Example H3

Why replace SQL Server with ParadeDB?

ParadeDB runs inside PostgreSQL, so you avoid a second RDBMS, reduce licensing costs, and keep your stack in one place. Vector similarity and full-text features live next to your relational data, eliminating ETL overhead.

Does ParadeDB scale better for AI workloads?

Yes. ParadeDB’s IVFFlat and HNSW index types handle millions of embeddings with millisecond latency. SQL Server’s vector capabilities are still preview and require memory-intensive columnstore tables.

Can ParadeDB match SQL Server’s full-text search?

ParadeDB extends PostgreSQL’s mature text search with language dictionaries, ranking, and phrase queries. It stores GIN/GIST or HNSW indexes alongside JSONB or standard columns, cutting cross-system joins.

How do I install ParadeDB?

Inside psql: CREATE EXTENSION paradedb; Optionally specify a schema or add CASCADE to pull dependencies.

What is the basic syntax for a vector index?

Create a new vector column, then index it: ALTER TABLE products ADD COLUMN embedding vector(1536); CREATE INDEX products_embedding_idx ON products USING paradedb_ivf_ops (embedding);

How do I query vectors and relational data together?

Use the <-> operator for cosine or Euclidean distance. Combine with normal SQL filters to narrow candidates before the similarity search.

Example: recommend similar products

SELECT id, name FROM products WHERE stock > 0 ORDER BY embedding <-> $1 LIMIT 5;

Best practices for ParadeDB migration

1) Start with read-only replicas to benchmark. 2) Batch-load vectors, then build indexes. 3) Tune paradedb.ivf_list_size for recall vs. speed. 4) Keep embeddings in a fixed dimension.

Common mistakes to avoid

Skipping ANALYZE: PostgreSQL needs fresh statistics for the planner to choose the ParadeDB index. Run ANALYZE after large loads.

Using different dimensions: All rows in a vector column must share the same length. Store padding or re-generate embeddings before insert.

When should I stay on SQL Server?

If you’re locked into SSIS packages or rely heavily on CLR procedures, the porting cost may outweigh ParadeDB’s benefits. Otherwise, PostgreSQL + ParadeDB offers lower TCO and faster AI iteration.

Why How to Choose ParadeDB over SQL Server in PostgreSQL is important

How to Choose ParadeDB over SQL Server in PostgreSQL Example Usage


--Find the 3 most similar products under $500 still in stock
SELECT id, name, price
FROM Products
WHERE price < 500 AND stock > 0
ORDER BY embedding <-> $1   --$1 = user embedding
LIMIT 3;

How to Choose ParadeDB over SQL Server in PostgreSQL Syntax


--Install extension (once per database)
CREATE EXTENSION IF NOT EXISTS paradedb [WITH SCHEMA custom_schema] [CASCADE];

--Add a vector column to Products
ALTER TABLE Products ADD COLUMN embedding vector(1536);

--Build an IVF (Inverted File) index for ANN search
CREATE INDEX products_embedding_idx
ON Products USING paradedb_ivf_ops (embedding)
WITH (lists = 100, pq_segments = 16);

--Full-text + vector query example
SELECT p.id, p.name, p.price
FROM Products AS p
WHERE to_tsvector('english', p.name) @@ plainto_tsquery('laptop')
ORDER BY p.embedding <-> $1  --$1 is a 1536-dim array
LIMIT 10;

Common Mistakes

Forgetting to run ANALYZE after bulk inserts means the PostgreSQL planner may skip the ParadeDB index, leading to full table scans.
Mixing embeddings with different dimensions raises an error. Ensure every row uses the same vector length before inserting.