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.
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.
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.
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.
Inside psql: CREATE EXTENSION paradedb;
Optionally specify a schema or add CASCADE
to pull dependencies.
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);
Use the <->
operator for cosine or Euclidean distance. Combine with normal SQL filters to narrow candidates before the similarity search.
SELECT id, name FROM products WHERE stock > 0 ORDER BY embedding <-> $1 LIMIT 5;
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.
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.
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.
Yes. It’s battle-tested in several high-traffic SaaS apps and passes PostgreSQL extension compatibility tests.
Only on RDS Custom or self-managed Postgres. Standard RDS blocks custom C extensions.
Each vector element is a 4-byte float. Multiply dimension × 4 to estimate space. Use IVF or HNSW indexes to keep queries fast.