Prisma ORM vs Drizzle ORM: Which to Choose in 2026

Introduction

Choosing the right ORM for a TypeScript project is one of those decisions that ripples through your entire codebase. Pick the wrong one and you’ll spend months fighting migration bugs, verbose query syntax, or runtime type errors that your ORM should have caught at compile time. Pick the right one and your database layer becomes almost invisible — clean, fast, and predictable.

In 2026, the two ORMs dominating the TypeScript ecosystem are Prisma and Drizzle. Both have matured significantly. Prisma released version 7 with major serverless improvements. Drizzle shipped its “Alternation Engine” — a massive rearchitecture with 9,000+ tests. The gap between them has narrowed, but the philosophical differences that made them distinct in 2023 remain: Prisma prioritizes developer experience and abstraction; Drizzle prioritizes SQL transparency and minimal overhead.

This guide cuts through the marketing noise. We’ll look at real benchmarks, actual bundle sizes, migration workflows, type safety guarantees, and database support — then give you an honest decision framework so you can pick the right ORM for your specific project.

Sources at a glance:

• Prisma GitHub: ~45,500 stars (github.com/prisma/prisma)
• Drizzle GitHub: ~33,400 stars, ~4.9M weekly npm downloads (npmtrends.com)
• Type-check benchmark: Prisma checks types 72% faster than Drizzle (prisma.io)
• Bundle size: Drizzle ~1.5 MB vs Prisma ~6.5 MB (techloset.com)
• Cold start gap: ~50–80ms in 2026 (Prisma 7 vs Drizzle) (dev.to)

---

Why ORMs Matter in TypeScript

TypeScript’s greatest strength is its type system — catching errors at compile time rather than runtime. But databases don’t speak TypeScript. They speak SQL. An ORM is the translation layer, and how well it preserves type safety across that boundary determines whether you’re truly safe from runtime database errors or just playing a guessing game with any types.

Beyond type safety, ORMs handle three critical concerns in modern applications:

Migration management — As your schema evolves, you need a reliable way to propagate changes across environments without data loss or downtime.

Query composition — Writing raw SQL for every operation is error-prone and hard to maintain. A good ORM gives you composable, reusable query building blocks.

Database abstraction — Ideally, your ORM lets you swap the underlying database (PostgreSQL → PlanetScale → SQLite) without rewriting your application logic.

Both Prisma and Drizzle address these concerns, but they take fundamentally different approaches — and those differences matter more as your project scales.

---

Prisma Overview: Schema-First, Developer-First

Prisma was released in 2021 and quickly became the de facto choice for Node.js/TypeScript developers who wanted a batteries-included ORM with a gentle learning curve. It has accumulated approximately 45,500 GitHub stars and millions of monthly npm downloads, making it the most widely adopted TypeScript ORM as of early 2026.

Schema-First Design

Prisma’s defining characteristic is its schema-first architecture. You define your entire data model in a declarative schema.prisma file, and Prisma generates a fully-typed client from that schema. Your database structure lives in one file, is human-readable, and serves as the single source of truth.

// schema.prisma
model User {
  id        String   @id @default(uuid())
  email     String   @unique
  name      String?
  posts     Post[]
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
}

model Post {
  id        String   @id @default(uuid())
  title     String
  content   String?
  published Boolean  @default(false)
  author    User     @relation(fields: [authorId], references: [id])
  authorId  String
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
}

This schema generates a PrismaClient with fully typed queries, mutations, and relations. If you try to access a field that doesn’t exist, TypeScript screams at you at compile time — not at runtime in production.

Prisma Client

The generated PrismaClient is a strongly-typed database client that supports all CRUD operations, relations, transactions, and aggregations. Queries are type-safe end-to-end:

import { PrismaClient } from '@prisma/client'
const prisma = new PrismaClient()

// Fully typed — TypeScript knows the return shape
const user = await prisma.user.findUnique({
  where: { email: '[email protected]' },
  include: { posts: { where: { published: true } } }
})
// user.posts[0].title is typed as string, not unknown

Prisma’s query engine sits between your application and the database driver. In Prisma 7, this engine was rewritten to drastically reduce cold start overhead for serverless deployments.

Migration System

Prisma Migrate handles schema changes through a versioned migration history. Running prisma migrate dev compares your schema.prisma against the current database state and generates a SQL migration file:

npx prisma migrate dev --name add_user_role

This produces a migration file with the exact SQL needed, which you can review before applying. Prisma Migrate is designed to be safe: it generates a shadow database to detect drift, and each migration is idempotent where possible.

Prisma Studio

Prisma ships a built-in GUI called Prisma Studio for visually browsing and editing data. It’s incredibly useful during development for quick data exploration without writing SQL or connecting an external database tool.

npx prisma studio

This opens a browser window at http://localhost:5555 with a spreadsheet-like interface for all your models.

The Tradeoffs

Prisma’s strength is its DX — the schema is clean, the generated client is ergonomic, and Studio is genuinely useful. The cost is bundle size (~6.5 MB) and runtime overhead: the Prisma engine is a separate binary that runs as a sidecar process. For serverless functions, this adds cold start latency. For large monorepos, type checking can be slow because the Prisma type system is complex and highly generic.

---

Drizzle Overview: SQL-Like, Lightweight, Type-Safe

Drizzle ORM was created by the Drizzle Team with a different philosophy: be as close to SQL as possible while still providing full TypeScript type safety. Where Prisma abstracts away SQL, Drizzle embraces it. Drizzle has grown to approximately 33,400 GitHub stars and sees ~4.9 million weekly npm downloads as of early 2026, cementing its position as the #2 TypeScript ORM.

SQL-Like Schema Definition

Drizzle uses TypeScript to define schemas — not a custom DSL. Your schema looks like TypeScript classes that map directly to SQL CREATE TABLE statements:

import { pgTable, varchar, uuid, boolean, timestamp } from 'drizzle-orm/pg-core'

export const users = pgTable('users', {
  id: uuid('id').defaultRandom().primaryKey(),
  email: varchar('email', { length: 255 }).notNull().unique(),
  name: varchar('name', { length: 255 }),
  role: varchar('role', { length: 50 }).default('user').notNull(),
  createdAt: timestamp('created_at').defaultNow().notNull(),
})

export const posts = pgTable('posts', {
  id: uuid('id').defaultRandom().primaryKey(),
  title: varchar('title', { length: 500 }).notNull(),
  content: varchar('content', { length: 10000 }),
  published: boolean('published').default(false).notNull(),
  authorId: uuid('author_id').notNull().references(() => users.id),
  createdAt: timestamp('created_at').defaultNow().notNull(),
})

The schema is TypeScript, which means your editor’s IntelliSense and type checking work natively — no custom language server required. There’s no separate code generation step; the types are inferred directly from the schema definition.

Generated Types, Zero Abstraction

Drizzle’s query builder produces raw SQL strings under the hood, with TypeScript types inferred from the schema. This means:

1. No runtime engine — Drizzle generates plain SQL sent directly to your database driver
2. Smaller bundle — Drizzle weighs approximately 1.5 MB compared to Prisma’s ~6.5 MB
3. Predictable SQL — You see exactly what SQL is generated, making debugging and optimization straightforward

import { drizzle } from 'drizzle-orm/node-postgres'
import { eq } from 'drizzle-orm'

const db = drizzle(pool)

// Type-safe query — generated SQL is readable
const user = await db.select().from(users)
  .where(eq(users.email, '[email protected]'))
  .leftJoin(posts, eq(posts.authorId, users.id))

Serverless by Design

Drizzle was built for modern JavaScript runtimes. It works natively in:

• Node.js (postgres, mysql2, better-sqlite3 drivers)
• Bun (native Bun database APIs)
• Deno
• Cloudflare Workers
• Any Edge runtime

There’s no sidecar process. Your function’s bundle includes the ORM logic, which compiles down to a thin wrapper around your database driver. Cold starts are dramatically faster.

The Tradeoffs

Drizzle’s SQL-first approach means you need to understand SQL to be productive. It’s not a crutch — it’s a tool. If you’re comfortable writing SQL, Drizzle gives you surgical control. If you want to abstract away the database, you’ll fight Drizzle rather than work with it.

The query API also requires more explicit handling of relations. Prisma’s include syntax for eager-loading relations is cleaner than Drizzle’s approach, though Drizzle’s relational API v2 (released in 2025) has significantly improved this.

---

Schema Syntax Comparison

The most visible difference between Prisma and Drizzle is how you define your data model. Let’s compare the same schema side by side.

Prisma Schema

Prisma uses its own DSL in schema.prisma:

model User {
  id        String   @id @default(uuid())
  email     String   @unique
  name      String?
  role      Role     @default(USER)
  posts     Post[]
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
}

enum Role {
  USER
  ADMIN
}

model Post {
  id        String   @id @default(uuid())
  title     String
  content   String?
  published Boolean  @default(false)
  author    User     @relation(fields: [authorId], references: [id])
  authorId  String
  createdAt DateTime @default(now())
  updatedAt DateTime @updatedAt
}

Drizzle Schema

Drizzle uses TypeScript directly:

import { pgTable, uuid, varchar, boolean, timestamp, pgEnum } from 'drizzle-orm/pg-core'

export const roleEnum = pgEnum('role', ['USER', 'ADMIN'])

export const users = pgTable('users', {
  id: uuid('id').defaultRandom().primaryKey(),
  email: varchar('email', { length: 255 }).notNull().unique(),
  name: varchar('name', { length: 255 }),
  role: roleEnum('role').default('USER').notNull(),
  createdAt: timestamp('created_at').defaultNow().notNull(),
})

export const posts = pgTable('posts', {
  id: uuid('id').defaultRandom().primaryKey(),
  title: varchar('title', { length: 500 }).notNull(),
  content: varchar('content', { length: 10000 }),
  published: boolean('published').default(false).notNull(),
  authorId: uuid('author_id').notNull().references(() => users.id),
  createdAt: timestamp('created_at').defaultNow().notNull(),
})

Key Differences

Aspect	Prisma	Drizzle
Language	Custom DSL (schema.prisma)	Pure TypeScript
Type inference	Generated client (code gen)	Direct TypeScript inference
Enum handling	Native enum block	pgEnum / dialect-specific
Default values	@default(value)	.default(value)
Relations	Implicit via @relation	Explicit column references
IDE support	Custom language server	Native TypeScript

Prisma’s schema is more readable and concise, especially for developers who don’t want to think in SQL. Drizzle’s TypeScript schema gives you full access to TypeScript’s type system without code generation, making it easier to build complex conditional types or share schema definitions across packages.

---

Query Performance Benchmarks

Performance is the area where Drizzle’s architectural choice (no runtime engine, pure SQL) gives it a structural advantage. But Prisma 7 has significantly closed the gap.

ORM Overhead (Raw Query Speed)

Drizzle generates SQL strings directly with zero abstraction overhead. In raw query execution benchmarks, Drizzle consistently shows lower latency per operation. According to community benchmarks published in early 2026, Drizzle’s ORM overhead is approximately 1–3ms per query compared to raw pg driver performance, while Prisma’s overhead runs approximately 3–8ms due to its query engine layer.

For trivial queries (single row lookups), this difference is measurable. For complex queries with joins, aggregations, or large result sets, the database query execution time dwarfs the ORM’s overhead, making the difference negligible in practice.

Cold Start (Serverless)

This is where Prisma made its biggest improvement in version 7. Previously, Prisma’s sidecar engine process could add 200–800ms to cold starts on serverless platforms. Prisma 7’s rearchitecture brings cold starts to approximately 50–80ms above baseline — comparable to Drizzle’s ~10–30ms overhead.

Source: “Drizzle ORM vs Prisma in 2026: The Honest Comparison Nobody Is Making,” DEV Community, February 2026 (dev.to)

As the author notes: “Prisma 7’s cold start is now in the same ballpark as Drizzle, not an order of magnitude worse.”

Type Checking Speed

Here’s an area where Prisma leads decisively. Prisma’s generated types are complex but structurally sound, and the Prisma language server handles them efficiently. A benchmark published by Prisma in September 2025 found that Prisma checks queries 72% faster on average than Drizzle during TypeScript type checking.

Source: “Why Prisma ORM Checks Types Faster Than Drizzle,” Prisma Blog, September 2025 (prisma.io)

This matters in large codebases. Slower type checking means longer CI runs and a laggier editing experience, especially in monorepos with dozens of packages that all import the ORM client.

Bundle Size

ORM	Bundle Size
Drizzle	~1.5 MB
Prisma	~6.5 MB

Source: “Drizzle vs Prisma: Which TypeScript ORM is More Efficient in 2025,” Techloset, April 2025 (techloset.com)

Prisma’s larger bundle is partly due to the query engine, which is compiled in. For traditional server deployments, this is irrelevant. For edge functions and bandwidth-constrained environments, Drizzle’s smaller footprint is a genuine advantage.

The Verdict on Performance

In 2026: Drizzle wins raw ORM overhead. Prisma wins type-checking speed. Cold starts are now roughly comparable. For most applications, performance differences won’t be the deciding factor — DX and ecosystem fit will be.

---

Migration Workflow

Migrations are where ORMs live or die in production. A bad migration system causes data loss, long deployment windows, and sleepless incident calls. Both Prisma and Drizzle have mature migration systems, but they differ in philosophy.

Prisma Migrate

Prisma Migrate uses a declarative approach. You define your desired schema state, and Prisma computes the diff:

npx prisma migrate dev --name add_user_role

This generates a SQL migration file:

-- Generated by Prisma Migrate
CREATE TABLE "Role" (
    "id" UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    "name" TEXT NOT NULL
);

ALTER TABLE "User" ADD COLUMN "roleId" UUID REFERENCES "Role"(id);

The generated SQL is readable, so you can audit it before applying. Prisma uses a _prisma_migrations table to track which migrations have been applied, and it supports baseline migrations for existing databases.

Prisma also has a Shadow Database — a temporary database created automatically to validate migrations against the actual schema before applying them to your real database. This catches issues like circular dependencies or ambiguous column changes before they hit production.

Drizzle Kit

Drizzle takes a differential approach. You define your schema in TypeScript, and drizzle-kit generates migrations by comparing your schema against a live database connection:

npx drizzle-kit generate:pg
npx drizzle-kit push:pg   # For quick prototyping
npx drizzle-kit migrate    # For production

Drizzle Kit 1.0 (and the newer “Alternation Engine” branch with 9,000+ tests) improved migration reliability significantly. The generated SQL is minimal and explicit:

CREATE TABLE IF NOT EXISTS "role" (
    "id" uuid PRIMARY KEY DEFAULT gen_random_uuid(),
    "name" text NOT NULL
);

ALTER TABLE "users" ADD COLUMN IF NOT EXISTS "role_id" uuid REFERENCES "role"(id);

Drizzle’s push command (drizzle-kit push:pg) is popular for rapid prototyping — it applies schema changes directly without generating migration files, similar to how Prisma’s db push works.

Which Migration System is Better?

For prototyping: Both are roughly equivalent. Prisma’s db push and Drizzle’s push:pg are both fast, schema-synchronizing tools that skip migration history for speed.

For production with data at risk: Prisma Migrate’s shadow database and drift detection give you better safety guarantees. Drizzle’s differential approach is transparent but requires more manual oversight.

For teams with strict SQL review processes: Drizzle generates cleaner, more explicit SQL that SQL-focused DBAs can review easily. Prisma’s generated SQL is also clean but goes through an abstraction layer first.

---

Type Safety and Developer Experience

Type safety is the core promise of using an ORM in TypeScript, but “type safety” means different things in practice.

Prisma’s Approach: Generated, Layered Types

Prisma generates a complete type hierarchy from your schema:

// The generated Prisma namespace gives you:
type User = Prisma.UserGetPayload<{ include: { posts: true } }>
type UserCreateInput = Prisma.UserCreateArgs['data']
type UserWhereUniqueInput = Prisma.UserFindUniqueArgs['where']

The types are deep and precise. Prisma’s $extends API lets you add custom methods to the client with full type inference. The trade-off is that these types are generated — if you have a complex schema with many relations and optional fields, TypeScript needs to resolve deeply nested generic types, which can slow down type checking.

Prisma’s official benchmark claims 72% faster type checking than Drizzle, which is counterintuitive given how complex Prisma’s generated types are. The explanation is that Prisma’s types are structurally optimized for resolution, while Drizzle’s approach of inferring types directly from schema definitions creates more complex TypeScript type inference scenarios in large queries.

Source: “Why Prisma ORM Checks Types Faster Than Drizzle,” Prisma Blog (prisma.io)

Drizzle’s Approach: Inferred, Direct Types

Drizzle’s types are inferred directly from your schema definition:

import { users, posts } from './schema'

// The type of `users` is statically known from the pgTable definition
type User = typeof users.$inferSelect
type NewUser = typeof users.$inferInsert

This approach feels natural to TypeScript developers — the schema is the type, the type is the schema. There’s no code generation step to run, so schema changes are immediately reflected in types across your codebase.

The downside: as your queries become more complex (subqueries, window functions, complex joins), Drizzle’s inferred types can become very verbose in error messages. TypeScript’s type system has to resolve the full chain of inference, which can lead to cryptic error messages that take time to debug.

Query API Ergonomics

Prisma:

// Relations are intuitive and declarative
const users = await prisma.user.findMany({
  where: { published: true },
  include: {
    posts: true,
    profile: true,
  },
})

Drizzle (classic query builder):

// Relations require explicit joins
const users = await db.select({
  user: users,
  post: posts,
})
.from(users)
.leftJoin(posts, eq(posts.authorId, users.id))
.where(eq(users.published, true))

Drizzle Relational API v2 (2025):

// Improved relation handling
const users = await db.query.users.findMany({
  with: {
    posts: true,
    profile: true,
  },
  where: eq(users.published, true),
})

The new relational API brings Drizzle much closer to Prisma’s ergonomics for relation-heavy queries.

---

Database Support

Both ORMs support the major databases, but there are important nuances around managed and serverless database services.

PostgreSQL

Both Prisma and Drizzle were built with PostgreSQL as the primary target. Full feature parity on PostgreSQL for both ORMs.

MySQL / MariaDB

Prisma has full MySQL and MariaDB support. Drizzle supports MySQL and MariaDB through its drizzle-orm/mysql-core and drizzle-orm/mariadb-core packages.

SQLite

Prisma supports SQLite for development and simple use cases, though some features (like enums in older Prisma versions) don’t map cleanly to SQLite’s type system. Drizzle has excellent SQLite support, and because Drizzle uses standard SQL, there’s less abstraction leakage.

PlanetScale (MySQL-compatible serverless)

This is where the choice gets interesting. PlanetScale is a MySQL-compatible serverless database that doesn’t support foreign key constraints (it uses Vitess under the hood, which has different constraints). Prisma has explicit PlanetScale support and a dedicated guide. Drizzle supports PlanetScale through its MySQL dialect.

Important for Drizzle: Because PlanetScale doesn’t support foreign keys, you’ll need to manage relations manually in Drizzle — either in application code or using Drizzle’s relations() helper without actual FK constraints. This is doable but requires more care.

For PlanetScale specifically, Prisma has the edge due to first-party documentation and tested compatibility.

CockroachDB

Prisma has CockroachDB support. Drizzle supports CockroachDB through its PostgreSQL compatibility layer.

Summary Table

Database	Prisma	Drizzle
PostgreSQL	✅ Full	✅ Full
MySQL	✅ Full	✅ Full
SQLite	✅ (dev only)	✅ Full
PlanetScale	✅ First-class	⚠️ Compatible (manual FK)
CockroachDB	✅ Full	✅ Via PostgreSQL dialect
MongoDB	✅ Native	❌ No

---

When to Choose Prisma vs Drizzle: Decision Matrix

Neither ORM is universally better. The right choice depends on your project context, team, and constraints. Here’s an honest decision framework.

Choose Prisma if:

• Your team is backend-novice — Prisma’s DX is unparalleled for developers who are more comfortable with TypeScript than SQL
• You need the best visual tooling — Prisma Studio is genuinely useful for data exploration during development
• You’re on PlanetScale — First-class support and tested compatibility
• You value schema readability — The schema.prisma file is clean, minimal, and easy to review in pull requests
• You want minimal boilerplate — prisma generate handles everything; you rarely touch the ORM’s internals
• Your project started with a SaaS backend — If you’re building a product and the database is a means to an end, Prisma’s abstraction lets you move faster

Choose Drizzle if:

• SQL is your native language — If your team thinks in SQL queries and wants to see exactly what’s sent to the database, Drizzle is a better fit
• Bundle size matters — Edge functions, Cloudflare Workers, and bandwidth-constrained environments benefit from Drizzle’s ~1.5 MB footprint
• You’re building a library — Drizzle’s pure TypeScript schema doesn’t require a code generation step, making it easier to publish as a library
• You need predictable query output — Drizzle’s SQL is readable and debuggable; Prisma’s query engine can produce surprising SQL in edge cases
• You’re on Bun or Deno — Drizzle has first-class support for modern JavaScript runtimes beyond Node.js
• You need fine-grained SQL control — Window functions, CTEs, complex CTAS, and niche SQL features are easier to express in Drizzle’s query builder

The Gray Area: Mixed Approaches

In 2026, many production teams use both — Prisma for the main application layer (where DX matters) and Drizzle for specific performance-critical queries (where SQL control matters). This isn’t elegant, but it’s pragmatic.

Decision Framework Table

Criterion	Prisma Wins	Drizzle Wins	Tie
Type safety (raw safety)	✅	✅
Type checking speed	✅ (72% faster)
Query performance		✅
Bundle size		✅ (1.5MB vs 6.5MB)
Cold start		✅ (marginally)
Schema readability	✅
SQL transparency		✅
Migration safety	✅
PlanetScale support	✅
Bun/Deno/Edge support		✅
Visual tooling	✅
Learning curve	✅
Complex relation handling		✅ (v2)

---

Real-World Adoption

Both ORMs have proven themselves in production. Here’s what the ecosystem looks like in 2026.

Prisma

Prisma’s head start (released 2021) and larger community have led to broader adoption. Companies using Prisma in production include:

• Large-scale startups using it with PostgreSQL and MySQL
• SaaS products that benefit from Prisma’s migration safety
• E-commerce platforms where the data model changes frequently

Prisma’s community is large, with extensive documentation, thousands of Stack Overflow answers, and an active Discord. If you run into an issue, someone has almost certainly solved it before.

Drizzle

Drizzle’s growth has been rapid since its 2022 launch. Companies and projects using Drizzle in production include:

• API-first products that need fine-grained query control
• Serverless-heavy architectures on Vercel, Netlify, and Cloudflare Workers
• Microservice backends where each service owns its database and the ORM must be lightweight

Source: “Drizzle ORM: The Lightweight ORM Developers Are Switching To,” Startupik, March 2026 (startupik.com)

Drizzle’s documentation at orm.drizzle.team has improved dramatically, and the community Discord is active and responsive. The Drizzle team ships updates frequently — the Alternation Engine PR (#4439) with 9,000+ tests represents a major investment in the project’s long-term quality.

Community and Ecosystem

Metric	Prisma	Drizzle
GitHub Stars	~45,500	~33,400
npm Weekly Downloads	Millions	~4.9M
Stack Overflow answers	Extensive	Growing
Discord community	Large, established	Active, rapidly growing
Official documentation	Excellent	Good, improving

Sources: GitHub stars from respective repositories; npm data from npmtrends.com (npmtrends.com)

Long-Term Sustainability

Both are open-source projects with healthy funding (Prisma is VC-backed; Drizzle is community and sponsor-supported). Neither shows signs of abandonment. The TypeScript ORM space is mature enough that both projects are likely to remain active for years.

---

Conclusion: The Honest Answer

There is no universally correct answer. In 2026, both Prisma and Drizzle are production-ready, well-maintained, and capable ORMs that serve different philosophies.

• Choose Prisma if developer experience, visual tooling, schema readability, and migration safety are your top priorities. It’s the right choice for most teams building SaaS products, e-commerce backends, and applications where the database is infrastructure, not the product itself.
• Choose Drizzle if SQL transparency, bundle size, performance, and serverless optimization are your top priorities. It’s the right choice for API-first products, edge deployments, and teams that think in SQL.

If you’re starting a new project today, try both. Prisma’s schema.prisma and Drizzle’s schema definitions both take about an hour to set up for a basic CRUD application. After that hour, you’ll have a strong gut feeling about which one fits your brain better.

The TypeScript ORM ecosystem is healthier than ever. Either choice is defensible — and both are a significant upgrade from writing raw SQL strings with untyped any parameters.

---

Quick Reference

Feature	Prisma	Drizzle
GitHub Stars	~45,500	~33,400
npm Downloads	Millions/mo	~4.9M/week
Bundle Size	~6.5 MB	~1.5 MB
Type-Check Speed	✅ 72% faster
Schema Language	Custom DSL	Pure TypeScript
Code Generation	Required	None (inferred)
SQL Transparency	Partial	Full
Migration System	Prisma Migrate	Drizzle Kit
Visual Tooling	Prisma Studio	None built-in
PlanetScale	First-class	Compatible
Bun/Deno/Edge	Partial	First-class
PostgreSQL	✅	✅
MySQL	✅	✅
SQLite	✅	✅
MongoDB	✅	❌
Best For	DX-focused teams	SQL-focused teams