ParadeDB adds high-performance vector and full-text search to PostgreSQL, making it a stronger choice than MySQL for AI-driven applications.
ParadeDB extends PostgreSQL with dense-vector, sparse-vector, and full-text search in one engine. MySQL needs external services (e.g., Elastic, Redis) or limited built-in FULLTEXT. Fewer moving parts means lower latency, simpler ops, and ACID guarantees.
ParadeDB stores embeddings next to transactional data. You run a single SQL statement instead of syncing to a vector store.JOINs, window functions, and permissions work out of the box—capabilities MySQL lacks without plugins.
ParadeDB uses HNSW and IVF indexes for sub-second ANN search over millions of vectors. MySQL’s FULLTEXT scales poorly and lacks vector distance operators, forcing costly work-arounds.
No. You create the extension once and keep writing standard SQL. Existing tooling—psql, Prisma, Django, Galaxy—continues to work.MySQL would require code rewrites or ORMs that support its distinct syntax.
Choose MySQL if your workload is heavy on simple OLTP with minimal search or if your team’s skill set and infra are already MySQL-centric. Otherwise, ParadeDB covers search, analytics, and transactions in one stack.
Store embeddings in a separate “embedding” column of type vector to avoid bloating row size.Index only the column you search.
Use CREATE INDEX ... USING hnsw
for read-heavy, low-latency workloads. Adjust m
and ef_search
for your recall/latency budget.
.
Yes. Keep transactional data in MySQL and replicate to PostgreSQL+ParadeDB for search. However, this adds ETL overhead compared to staying fully on Postgres.
ParadeDB works with Postgres sharding solutions like Citus and pgCat. For billions of vectors, partition by customer or hash and create per-partition HNSW indexes.
ParadeDB is Apache-2 licensed and hosted on GitHub, allowing enterprise audits and custom contributions.