Almost every application today needs to talk to a database. While there have been many hype cycles around non-relational databases, we always seem to return to our old and boring friend: SQL. “Boring” in this context is a good thing. Databases like PostgreSQL and MySQL are proven technologies and are suitable for most applications. Sometimes boring is exactly what you need.

Because SQL databases are everywhere, the question inevitably comes up: what is the best TypeScript ORM in 2026? In this article, I compare Drizzle, Prisma, and TypeORM, and explain why Drizzle is my top pick today.

TL;DR (Best TypeScript ORM in 2026)

If you want the short answer: Drizzle. It stays close to SQL, handles complex queries without fighting abstractions, and keeps query behavior obvious. Using Drizzle forces you to learn SQL, not framework-specific APIs that become useless knowledge the moment you switch to a project that uses a different tool.

Why Use an ORM?

Here are the main benefits ORMs provide over raw SQL:

  1. Type safety across queries and results
  2. Guardrails against SQL injection
  3. A consistent API for common operations
  4. Migrations and schema management

I have nothing against raw SQL, but the benefits of using an ORM are clearly there. Whether those benefits outweigh the costs is a separate debate. Since most teams end up using an ORM anyway, the more practical question is: which one is worth your time?

The 80/20 Problem

A few years ago, I watched Matteo Collina’s talk, “I Would Never Use an ORM”. It is definitely worth a watch, and it made me reconsider some of the opinions I had at the time. These days, I agree with almost everything Matteo says in that video.

Matteo’s argument draws on the Pareto principle: 80% of most projects is the easy part and takes about 20% of the effort. This is where ORMs shine. But the remaining 20%, the complex part that requires queries with many joins, aggregates, and subqueries, is where ORMs break down. The hard part is exactly where you would want the most help. Classic.

I completely agree with this take. Simple queries like SELECT * FROM users are easy everywhere. Choosing to use an ORM to make this part even simpler does not make much sense to me.

The real test is whether an ORM can handle the hard 20%. I believe there is at least one that can.

That ORM is Drizzle.

Drizzle Handles Complex Queries with Ease

Drizzle does not replace SQL with a separate query language. It exposes SQL directly through a typed, composable API. When the easy 80% ends, you keep writing queries instead of fighting abstractions.

Here is a complex query written in Drizzle. It builds a leaderboard of top authors from active departments who have published at least 3 posts in the last year:

import { eq, and, gte, gt, count, avg, sum, max, desc } from "drizzle-orm";

const oneYearAgo = new Date();
oneYearAgo.setFullYear(oneYearAgo.getFullYear() - 1);

const result = await db
  .select({
    authorId: users.id,
    authorName: users.name,
    email: users.email,
    department: departments.name,
    postsPublished: count(posts.id),
    avgLikes: avg(posts.likes),
    totalComments: sum(posts.commentCount),
    mostRecentPost: max(posts.publishedAt)
  })
  .from(users)
  .innerJoin(departments, eq(users.departmentId, departments.id))
  .innerJoin(posts, eq(posts.authorId, users.id))
  .where(
    and(
      eq(departments.active, true),
      eq(posts.status, "published"),
      gte(posts.publishedAt, oneYearAgo)
    )
  )
  .groupBy(users.id, users.name, users.email, departments.name)
  .having(gt(count(posts.id), 3))
  .orderBy(desc(avg(posts.likes)), desc(sum(posts.commentCount)))
  .limit(10);

This query joins three tables, filters by multiple conditions, groups results, applies a HAVING clause to exclude low-volume authors, and orders by engagement metrics. The hard 20% that normally forces you to step away from your ORM and go to raw SQL is not a problem here. The query reads like SQL because it is SQL.

SQL-Like API

The main advantage Drizzle has over other ORMs is its SQL-like API. If you know SQL, you know Drizzle. This becomes clear even with basic insert operations.

TypeORM

import { DataSource } from "typeorm";

const userRepository = dataSource.getRepository(User);

await userRepository.save({
  firstName: "John",
  lastName: "Doe",
  age: 35
});

There is no save keyword in SQL. Is this an INSERT or UPDATE? Turns out it is both: TypeORM’s save method inserts if the entity is new and updates if it already exists. This implicit behavior can be convenient but also confusing.

Prisma

import { PrismaClient } from "@prisma/client";

const prisma = new PrismaClient();

await prisma.user.create({
  data: {
    firstName: "John",
    lastName: "Doe",
    age: 35
  }
});

The word create hints at an INSERT, but the nested data object does not resemble SQL syntax.

Drizzle

import { users } from "./schema";
import { db } from "./db";

await db.insert(users).values({
  firstName: "John",
  lastName: "Doe",
  age: 35
});

If you have any SQL experience, this feels natural. The mental model matches the database. What you write is what you get.

This matters because using Drizzle teaches you SQL, not a framework-specific API. When you switch jobs or projects, that knowledge transfers. You are learning the database, not the abstraction layer.

Schema Definitions

Schema definition is another area where Drizzle stays close to SQL. Prisma uses its own schema language that abstracts away database-specific details. Drizzle is explicit about the dialect, which makes migrations more predictable.

Prisma

model User {
  id        Int      @id @default(autoincrement())
  email     String   @unique
  createdAt DateTime @default(now())
}

Prisma uses its own schema language that requires learning new syntax.

TypeORM

import { Entity, PrimaryGeneratedColumn, Column, CreateDateColumn } from "typeorm";

@Entity()
export class User {
  @PrimaryGeneratedColumn()
  id: number;

  @Column({ unique: true })
  email: string;

  @CreateDateColumn()
  createdAt: Date;
}

TypeORM uses decorators, which are familiar to developers coming from frameworks like NestJS, but the actual SQL types are hidden behind abstractions.

Drizzle (PostgreSQL)

import { pgTable, serial, varchar, timestamp } from "drizzle-orm/pg-core";

export const users = pgTable("users", {
  id: serial().primaryKey(),
  email: varchar({ length: 255 }).notNull().unique(),
  createdAt: timestamp({ withTimezone: true }).defaultNow().notNull()
});

When you read that Drizzle schema, you immediately see the target SQL types: serial, varchar(255), timestamp with time zone. The migration output becomes predictable because the schema maps directly to SQL.

Performance

Drizzle is lightweight. It does not ship with a separate query engine, and it does not generate extra queries behind your back. One query in your code means one query sent to the database. No magic.

While ORMs like Prisma and TypeORM offer powerful and convenient APIs, understanding the exact queries your application is running can be far more complex than with Drizzle.

The Verdict

When choosing an ORM, it’s worth asking whether it actually helps with the complicated 20%, or whether it forces you back to raw SQL when things get tough. Drizzle does not abandon you. Its SQL-like design makes it possible to express very complex queries without dropping down to a different tool.

There is another angle that matters in the long run: transferable knowledge. Time spent learning Prisma’s schema language or TypeORM’s decorator patterns is time invested in abstractions that lose their value the moment you switch projects. Time spent with Drizzle is time spent learning SQL. That knowledge travels with you.

If you know SQL (which you should), and you want to understand what your program is doing (which you also should), then Drizzle is a great choice.

Sometimes the new thing really is better.