Postgres for AI search and vector workloads.
VectorDB combines managed PostgreSQL, native vector search, real-time analytics, and multi-region infrastructure for teams building modern AI applications.
Production cluster
rag-prod-us-east
select title, summary
from documents
order by embedding <-> vector($1)
limit 5;Retrieval trace
Embedding job
1.8M rowsSynced across three regions with read latency under 21ms.
Trusted by developers shipping AI products worldwide
VectorDB gave us one place for customer data, embeddings, and production retrieval analytics. Our RAG stack got simpler and faster in the same migration.
We moved from a stitched-together search system to managed Postgres with vector indexes, replication, and real-time visibility our infra team can actually trust.
Everything AI teams expect from a modern database.
One managed platform for transactional data, embeddings, search, analytics, replication, and global deployment.
Vector Search
High-performance approximate nearest neighbor search directly beside your transactional data.
AI Embeddings
Generate, sync, and version embeddings from documents, events, and user content.
Horizontal Scaling
Scale reads, writes, storage, and vector indexes independently as products grow.
Real-time Replication
Stream changes to applications, warehouses, and AI pipelines with low latency.
SQL-first API
Use standard Postgres, familiar SQL, and type-safe SDKs without proprietary lock-in.
Multi-region Deployment
Place data near users with regional replicas, failover, and policy-aware routing.
RAG Optimization
Tune chunking, metadata filters, reranking, and retrieval metrics for AI answers.
Automatic Backups
Point-in-time recovery, snapshots, audit trails, and enterprise-grade retention.
Easy to use with SQL, SDKs, and your favorite ORM.
Run vector generation, hybrid search, and retrieval pipelines from the tools your team already ships with.
CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE documents (
id uuid PRIMARY KEY,
title text NOT NULL,
body text NOT NULL,
embedding vector(1536)
);
CREATE INDEX documents_embedding_idx
ON documents USING hnsw (embedding vector_cosine_ops);Built for semantic search, RAG pipelines, and hybrid retrieval.
VectorDB keeps source data, generated embeddings, metadata filters, and retrieval analytics together so AI systems stay fresh and observable.
Rank results by meaning, not just keywords, with Postgres-native vector indexes.
Blend BM25, vector similarity, metadata filters, and business ranking in one query.
Trace retrieval quality, token usage, latency, and feedback across production flows.
Automatically regenerate vectors as documents, permissions, and models change.
Search preview
Multiple late-stage support escalations and a 42 percent seat contraction risk.
Contract language flags renewal review, indemnity changes, and unresolved SLA terms.
Usage growth is strong, but payment history indicates procurement delay risk.
Documents, events, tickets, and records stream into managed Postgres.
Vectors are generated, indexed, versioned, and replicated automatically.
Applications query fresh context with SQL, SDKs, or the REST API.
Start small. Scale without re-architecting.
Transparent plans for prototypes, production teams, and enterprise AI platforms.