Rivestack — Managed PostgreSQL for AI with pgvector

2 min read Original article ↗

Spin up a production-ready database with pgvector, autoscaling, and backups—without the ops or the price tag.

⚡ 2,000 QPS vector search • <4ms latency • NVMe-backed

Transparent pricing. No surprises.

A three-step flow to launch AI-ready Postgres—without babysitting or big bills.

Launch Your Database

Pick a region and spend cap; we provision Postgres with pgvector, encryption, backups, and metrics in minutes.

Connect & Load Data

Plug in via psql, Prisma, SQLAlchemy, or LangChain; import your data and embeddings fast, with built-in loaders and quickstart examples.

Scale & Stay Safe

Autoscaling and read replicas keep performance steady under load; snapshots and point-in-time recovery guard everything.

Provision Now. Query in minutes

postgresql://appuser@db-7q9m2p.eu.rivestack.io/primary

✦ Goalng ✦ Java ✦ Rust ✦ Python ✦ Next.js  

Works with your stack.
Integrate it into your language or framework within minutes and unlock a simpler developer workflow.

package main

import (
	"database/sql"
	"log"
	"os"

	_ "github.com/jackc/pgx/v5/stdlib"
)

func main() {
	db, err := sql.Open("pgx", os.Getenv("DATABASE_URL"))
	if err != nil { log.Fatal(err) }
	defer db.Close()

	
	_, _ = db.Exec(`CREATE EXTENSION IF NOT EXISTS vector;
	                CREATE TABLE docs (id bigserial PRIMARY KEY, t text, e vector(3));`)

	
	_, _ = db.Exec(`INSERT INTO docs (t, e) VALUES ($1, $2::vector)`,
		"reset password", "[0.12,0.05,0.88]")

	
	var match string
	if err := db.QueryRow(`SELECT t FROM docs ORDER BY e <-> $1::vector LIMIT 1`,
		"[0.10,0.07,0.80]").Scan(&match); err != nil {
		log.Fatal(err)
	}
	log.Println("Nearest match:", match

Vector search in plain English

Find similar items instantly. No complex queries needed, just connect and search.

Choose a plan that fits your needs, with everything you need to stay organized and productive.

Got questions? We’ve got answers. Here’s everything you need to know before getting started.

Run 2,000 vector queries per second at <4ms latency.

Transparent pricing. No surprises.