Traditional + Vectors

Major databases like PostgreSQL, MongoDB, Elasticsearch, and Redis now support vector search natively or via extensions. Leverage your existing infrastructure and expertise — no new database to manage, no data migration required.

7 traditional databases with vector support

MongoDB Atlas Vector Search

Vector search built into the #1 document database

TraditionalDocument DB

Elasticsearch

Distributed search engine with vector capabilities

TraditionalSearch Engine

OpenSearch

Community-driven fork of Elasticsearch with vector search

Open SourceAWS

Redis Vector

Sub-millisecond vector search in memory

TraditionalIn-Memory

Supabase Vector

pgvector on Supabase — vectors in your Postgres

Open SourcePostgreSQLBaaS

SingleStore

Unified transactional-analytical DB with vector search

TraditionalNewSQL

Kinetica

GPU-accelerated database with vector search

TraditionalGPU

Why add vectors to your existing database?

Traditional databases with vector extensions let you add semantic search and AI capabilities without introducing a new database into your stack. Leverage your existing expertise, tooling, and infrastructure while gaining vector search functionality.

Key advantages:

✓No new database to learn or manage
✓Use existing backups, monitoring, and tooling
✓Combine relational queries with vector search
✓Reduced operational complexity
✓Leverage existing team expertise

Common use cases:

→Adding AI search to existing applications
→Teams already running PostgreSQL, Redis, or Elasticsearch
→Hybrid queries combining structured + vector data
→Incremental AI adoption without infrastructure changes