Written by Temitope Oyedele✏️

Drizzle and Prisma are two ORMs you’ll be recommended to use when working with data access and migrations in your project. There's an ongoing debate in the community over which of these two is superior. Yes, it's like the Samsung vs. iPhone debate.

In this article, we'll compare both ORMs to see how they rank. We'll take a look at their differences, benefits, and weaknesses to help you select the best ORM for the job.

Abstraction level

The level of abstraction in which Prisma operates is different from Drizzle. Prisma provides a high level of abstraction, helps simplify data fetching and manipulation process, and also has an API that is really easy to understand.

Prisma abstracts away the details of the underlying database. It introduces a domain-specific language (DSL), which is less complex than the underlying SQL.

With this level of abstraction, you can focus on productivity and simplifying complex database interactions.

As good as this may seem, this abstraction comes with a downside — it leaves you with less control when compared to using SQL explicitly.

Below is an example of how Prisma queries data:

const userData = await prisma.user.findMany({
  where: { isActive: true },
  select: { id: true, name: true, email: true }
});

Prisma generates queries that work for most use cases, but it can be less predictable for performance optimizations since it's abstracted from SQL.

Drizzle, on the other hand, follows an SQL-first approach. According to its documentation, “If you know SQL, then you know Drizzle.”

Drizzle offers more granular control over SQL queries and a closer relationship with the underlying database schema as it is designed to avoid the "black box" feeling of Prisma. In Drizzle, you know what SQL is being executed, which makes it easier to anticipate performance behavior.

Below is an example of how Drizzle queries data:

const userData = await drizzle.execute(`
  SELECT id, name, email 
  FROM users 
  WHERE is_active = true;
`);

Database support

In terms of database support, Prisma supports various databases, such as PostgreSQL, MySQL, SQLite, MongoDB, and SQL Server. It has an automatic migration feature that simplifies the process of how you update the database schemas. With this feature, you can quickly adapt to whatever data requirements you need without needing to manage migrations manually.

Drizzle, on the other hand, doesn't support as many databases as Prisma does but it supports all major SQL databases such as PostgreSQL, MySQL, or SQLite drivers. Its SQL-driven workflows provide a more hands-on experience and are built for developers who prefer direct SQL control over abstracted ORM layers.

Flexibility vs. speed

In terms of flexibility, Drizzle gives you complete control over your queries. You can write queries that meet the demands of your application. As a frontend developer, this level of control is what you need, especially if you're looking to fine-tune data handling to ensure minimal payload sizes and efficient data fetching.

Drizzle lets you customize every part of your query, and this is useful for complex applications with unique requirements, but as good as this sounds, it comes with a downside. Your speed is limited during development as each query is manually written and adjusted, and you need to have a deeper understanding of database interactions to acheive this.

Prisma, on the other hand, makes speed and ease of development a priority. Its auto-generated queries allow you to get started immediately without worrying about the minutiae of each query. It simplifies the data layer by abstracting away the complex query logic, which can result in significant productivity gains if you prefer frontend-focused development cycles. Now that we’ve taken a look at their differences, it’s time to compare these two ORMs.

Integration with frontend

Prisma integrates nicely with frontend frameworks like Next.js and Remix, and it’s flexible too. It works well with API routes, getServerSideProps, getStaticProps, and other server-side functions. With its built-in TypeScript support, Prisma auto-generates types from the schema end-to-end type, and safety is guaranteed, which helps catch errors during development.

Prisma also auto-generates queries and mutations, simplifying CRUD operations.

Below is an example of using Prisma's auto-generated schema in a Next.js app:

import { prisma } from '../../../lib/prisma';
import type { NextApiRequest, NextApiResponse } from 'next';

export default async function handler(req: NextApiRequest, res: NextApiResponse) {
  const { id } = req.query;

  if (req.method === 'GET' && typeof id === 'string') {
    const user = await prisma.user.findUnique({
      where: { id },
    });
    return user ? res.status(200).json(user) : res.status(404).json({ error: 'User not found' });
  }

  res.status(405).json({ error: 'Method not allowed' });
}

In this example, Prisma’s findUnique query, auto-generated from the schema, fetches a user by ID in a Next.js API route. This type-safe integration reduces manual API code and makes your frontend-backend interactions simpler and more productive.

Drizzle also supports integration with TypeScript-based frameworks like Next.js and Remix. It provides an API that mirrors SQL syntax and allows you to write type-safe queries. Drizzle generates TypeScript types based on the database schema that not only facilitates shared types between frontend and backend but also simplifies data handling. Unlike Prisma, Drizzle does not generate frontend-specific code but is optimized for use in server-side functions, API routes, and full-stack frameworks. This makes Drizzle a more manual but precise choice, ideal for those who prefer working closely with SQL. Below is an example of Drizzle integration in a Next.js API route:

import { pgTable, serial, text, varchar } from 'drizzle-orm/pg-core';
import { drizzle } from 'drizzle-orm/node-postgres';

export const users = pgTable('users', {
  id: serial('id').primaryKey(),
  name: text('name'),
  email: varchar('email', { length: 255 }).unique(),
});

type User = typeof users.$inferSelect;

import { eq } from 'drizzle-orm';
import type { NextApiRequest, NextApiResponse } from 'next';

export default async function handler(
  req: NextApiRequest, 
  res: NextApiResponse<User | { error: string }>
) {
  const { id } = req.query;

  if (req.method === 'GET' && typeof id === 'string') {
    try {
      const user = await db.select()
        .from(users)
        .where(eq(users.id, parseInt(id)))
        .limit(1);

      if (user.length > 0) {
        res.status(200).json(user[0]); 
      } else {
        res.status(404).json({ error: 'User not found' });
      }
    } catch (error) {
      res.status(500).json({ error: 'Internal Server Error' });
    }
    return;
  }

  res.status(405).json({ error: 'Method not allowed' });
}

In this code, Drizzle’s schema builder defines a user table and generates TypeScript types that can be shared across frontend and backend. This simple example demonstrates Drizzle's flexibility and how it offers control for SQL-focused development while maintaining type safety.

Query speed and performance

Prisma uses features like query batching and Prisma Accelerate to reduce database round-trips and cold starts. However, its abstraction layers may introduce slight overhead, which can impact query speed under high-frequency requests.

Drizzle, in contrast, operates without these additional abstraction layers, and that makes it fast when it comes to query execution. Drizzle would definitely be an ideal choice for complex or large-scale queries where direct database interaction is beneficial for speed.

Below is a benchmark comparison of typical frontend queries that shows how Prisma and Drizzle perform on common tasks:

Overall, Prisma is a solid choice if you need flexibility when handling complex queries, while Drizzle would be a solid choice for faster execution for simpler queries but would require more manual handling.

Type safety and developer experience

Prisma generates TypeScript types consistently across frontend and backend. You don't need manual type definitions because it eliminates the need for that and reduces data mismatches.

This approach leverages TypeScript to catch discrepancies at compile time and cutd down errors at runtime. For example, let's say you define a user model with an age field in Prisma’s schema and later rename it to birthdate. TypeScript immediately flags this and lets you know of any frontend code that still relies on the user age. This way, it tries to prevent a potential issue before it reaches production.

Drizzle, by contrast, doesn’t auto-generate types from an abstract schema. Instead, what it does is define data models in SQL-like schemas with TypeScript inferring types from these definitions. This provides you with flexibility and precision if you prefer detailed control over SQL queries without added ORM abstractions.

Even though Drizzle’s approach requires more manual updates, it still benefits from TypeScript’s compile-time checking to prevent runtime errors.

If, for example, a field is changed or removed, TypeScript flags any mismatched frontend references so you can make changes and update the codebase. This SQL-driven type inference ensures type consistency and helps maintain alignment between frontend and backend codebases.

Data fetching

Prisma supports GraphQL extensively and is very flexible as it integrates well with various GraphQL tools and frameworks. This flexibility helps avoid common issues like over-fetching and under-fetching by allowing the client to control exactly what data is returned from the server:

import { prisma } from '../../lib/prisma';

const resolvers = {
  Query: {
    user: async (_: unknown, { id }: { id: string }) => {
      return prisma.user.findUnique({
        where: { id },
        select: {
          id: true,
          name: true,
          email: true,
          posts: { select: { id: true, title: true } }
        },
      });
    },
    users: async (_: unknown, { ids }: { ids: string[] }) => {
      return prisma.user.findMany({
        where: { id: { in: ids } },
        select: { id: true, name: true, email: true }
      });
    }
  },
};

export default resolvers;

In this example, Prisma’s resolvers handle single and batch user fetching. It uses the select to retrieve only the fields needed. Prisma also supports RESTful APIs, which enable data fetching across both GraphQL and REST setups with optimizations like endpoint customization, query batching, and caching.

Drizzle, on the other hand, focuses on simplicity and RESTful setups. It allows you to create highly customizable endpoints that only return fields needed by the frontend. This gives you fine control over query structures, ideal for reducing over-fetching and under-fetching:

import { drizzle } from '../../../lib/drizzle';
import type { NextApiRequest, NextApiResponse } from 'next';
import { sql } from 'drizzle-orm';

export default async function handler(req: NextApiRequest, res: NextApiResponse) {
  const { id } = req.query;
  if (req.method === 'GET' && typeof id === 'string') {
    const user = await drizzle.query(sql`
      SELECT id, name, email FROM users WHERE id = ${id}
    `);

    if (user.length > 0) {
      res.status(200).json(user[0]);
    } else {
      res.status(404).json({ error: 'User not found' });
    }
    return;
  }
  res.status(405).json({ error: 'Method not allowed' });
}

In the example above, you can see how Drizzle allows precise SQL queries by retrieving only the necessary fields (id, name, email) in order to prevent over-fetching.

Drizzle also supports GraphQL through its drizzle-graphql package, which allows you to create a GraphQL server from a schema with minimal setup:

import { createGraphQLServer } from 'drizzle-graphql';
import { schema } from './drizzleSchema';

const server = createGraphQLServer(schema);

While Drizzle’s GraphQL setup is simple, you’ll still need to manually handle complex data fetching:

import { createGraphQLServer } from 'drizzle-graphql';
import { schema } from './drizzleSchema';

const customResolvers = {
  Query: {
    optimizedDataFetch: async (_, args, context) => {
      const result = await context.db.query('SELECT ...');
      return result;
    }
  }
};

const server = createGraphQLServer(schema, { resolvers: customResolvers });

This SQL-driven control in Drizzle provides flexibility for fine-tuning, although it requires more hands-on adjustment to achieve optimal frontend performance.

Tooling and IDE support

Prisma provides support for frontend-friendly development environments in many ways, one of which is through its official VS Code extension. This extension enhances syntax highlighting and error-checking in .prisma schema files and offers autocompletion and type safety for Prisma models.

For instance, if you need to fetch a user profile, you can benefit from autocompletion and error-checking on fields like user and id, as this extension can auto-suggest fields related to your Prisma models:

const user = await prisma.user.findUnique({ 
  where: { id: userId }, 
});

Another thing the extension does is ensure that TypeScript catches any change in the schema across both frontend and backend.

In contrast, Drizzle follows a leaner workflow without a dedicated extension, which can be challenging for anyone who is less familiar with SQL. However, Drizzle leverages TypeScript integration in VS Code, which allows autocompletion on SQL fields (e.g., users.id). This SQL-focused approach may appeal to those who prefer working directly with raw SQL:

const user = await db.select().from(users).where(users.id.equals(userId));

Drizzle’s direct approach reduces reliance on ORM-specific abstractions and provides simplicity for those wanting a straightforward interaction with the database. However, this requires more SQL knowledge, which can pose a learning curve if you’re a frontend developer who is new to backend work.

Migrations

Both Drizzle and Prisma support migrations by generating SQL migration files from model specifications executed via a CLI, but their approaches to managing migrations differ.

Prisma follows a declarative, schema-first approach. It uses schema.prisma as the central database structure and generates SQL migration files based on schema changes to ensure database consistency.

This automation helps maintain API stability, as Prisma updates TypeScript types whenever the schema changes, and this will flag any code misalignments on your frontend during compile time.

In the case of Drizzle, migrations are written directly in TypeScript, which provide you with more hands-on control over each migration step.

However, Drizzle requires you to manually update your frontend types whenever a schema change occurs because type adjustments are not automatically propagated. This can affect API stability and also add an unplanned responsibility of you having to keep your frontend types aligned with backend schema updates.

Security and validation

Prisma and Drizzle each handle validation and security differently. Prisma defines a comprehensive schema that serves as a contract between the frontend and the backend. For instance, when you specify rules, such as a unique or formatted email field, Prisma enforces these constraints automatically:

model User {
  id        Int      @id @default(autoincrement())
  email     String   @unique
  name      String   @db.VarChar(255)
  age       Int      @db.Int
}

This schema-driven validation reduces custom code and ensures a consistent data validation throughout the application. Prisma also includes built-in security measures like automated query sanitization, which protects against SQL injection, and is most especially beneficial for those who are less familiar with SQL security practices.

Drizzle on the other hand allows you to implement validation rules manually and also provides flexibility when defining custom validation logic. Although this approach may require more initial setup, it provides direct control over validation.

The Drizzle error handling is a direct representation of database-level issues. It tends to give an insight into the constraints on the data. However, interpreting these errors for user-friendly frontend messages can require additional work. Below is a comparison table summarizing everything we discussed:

Use cases

Drizzle

• Performance-focused applications: Supports apps needing maximum speed and minimal overhead, such as real-time dashboards, trading platforms, or high-traffic APIs
• Type-safe development: Prevents data-related bugs in large TypeScript projects, critical for financial or healthcare systems
• Infrastructure scaling: Fits microservices and serverless applications in cloud environments with variable loads and quick startups
• SQL-centric projects: Suits teams using complex SQL queries for data warehousing or analytics platforms
• Modern web framework integration: Efficiently integrates with Next.js, SvelteKit, and other modern frameworks for server components and API routes
• Real-time applications: Offers fine-grained control over queries for low-latency, data-intensive applications like live visualizations or collaborative tools

Prisma

• Rapid prototyping: Simplifies database setup for MVPs and startups
• Full-stack development: Keeps frontend and backend in sync with auto-generated types and clients
• Complex data relationships: Manages interconnected data for apps like social networks or CMSs
• Database abstraction: Enables switching databases or supporting multiple databases with ease
• Real-time applications: Supports data synchronization for apps needing live updates, such as collaborative tools or social media platforms

When to choose Prisma

You can choose Prisma when:

• You need to build and validate your idea quickly
• Your application success depends more on user experience than database performance
• Your team's SQL expertise is limited
• Time-to-market is your priority

When to choose Drizzle

Choose Drizzle when:

• Database performance is critical to your application's success
• You're building a database-intensive application that needs to scale
• Your team has strong SQL expertise
• You need fine-grained control over query optimization

Conclusion

In this article, we took a look at the differences between Prisma and Drizzle and compared them side-by-side to see how they rank in various aspects.

Prisma and Drizzle use different strategies, each with its own set of advantages and disadvantages. Depending on your project requirements and development objectives, one may be a better fit than the other. Understanding these ORMs can help you know which is right for the job.

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