Best Postgres for AI apps

AI apps have a different database profile than traditional web apps. Requests burst unpredictably, inference runs at the edge, and agents increasingly need direct database access. Most managed Postgres services weren't designed with those patterns in mind.

Built-in connection pooling

Serverless inference endpoints open a new database connection per invocation. At any meaningful load, that exhausts your Postgres connection limit fast. Prisma Postgres includes connection pooling by default, so there is no PgBouncer to configure and no sidecar to run. Your connection string already points to the pooler.

Edge-native connectivity

Standard Postgres TCP drivers don't work in Cloudflare Workers, Vercel Edge Functions, or Deno Deploy. Prisma Postgres ships @prisma/ppg, a serverless driver that connects over HTTP.

See Serverless driver for setup.

pgvector for embeddings

Prisma Postgres supports the pgvector extension for storing and querying vector embeddings natively in Postgres. You can keep your embeddings alongside your application data without a separate vector store.

See Postgres extensions for how to enable it.

MCP server for agent workflows

AI agents (Claude, Cursor, or custom) can connect to Prisma Postgres via the Prisma MCP server to introspect schemas, run queries, apply migrations, and manage environments, without raw SQL access.

See Prisma MCP server for setup.

At a glance

Get started

bun create prisma@latest

Or provision from the Prisma Console and grab your connection string.

• Connect to Prisma Postgres
• Use the serverless driver at the edge
• Enable pgvector
• Set up the Prisma MCP server