Drizzle vs Prisma for AI SaaS — a practical 2026 breakdown.

Drizzle — schema definition

// schema.ts — TypeScript is the schema
import { pgTable, text, timestamp, uuid } from "drizzle-orm/pg-core";

export const users = pgTable("users", {
  id: uuid("id").primaryKey().defaultRandom(),
  email: text("email").notNull().unique(),
  createdAt: timestamp("created_at").defaultNow(),
});

// Types inferred automatically — no generation step
type User = typeof users.$inferSelect;

Prisma — schema definition

// schema.prisma — separate DSL file
model User {
  id        String   @id @default(uuid())
  email     String   @unique
  createdAt DateTime @default(now())
}

// Then run: npx prisma generate
// Types come from generated PrismaClient

Drizzle — migration

# Push schema changes immediately (dev)
bun drizzle-kit push

# Or generate a migration file (prod)
bun drizzle-kit generate
bun drizzle-kit migrate

Prisma — migration

# Create + apply a migration
npx prisma migrate dev --name add_users

# Deploy to production
npx prisma migrate deploy

# View database in browser
npx prisma studio

Drizzle — query

// SQL-like, explicit joins
const results = await db
  .select()
  .from(users)
  .where(eq(users.email, "hi@example.com"))
  .limit(1);

// Raw SQL when you need it
const count = await db.execute(
  sql`SELECT COUNT(*) FROM users`
);

Prisma — query

// Object-relational style, intuitive
const user = await prisma.user.findUnique({
  where: { email: "hi@example.com" },
});

// Relations loaded elegantly
const posts = await prisma.user.findUnique({
  where: { id },
  include: { posts: true },
});