I’ve been building web applications for years, and one persistent challenge has always been keeping the frontend and backend in sync. How many times have you updated an API endpoint only to break the client because the types didn’t match? That frustration led me to discover a powerful combination that changed my development workflow: tRPC, Prisma, and Next.js. This stack delivers something remarkable—true end-to-end type safety without the overhead of code generation or complex schemas. Today, I want to share this approach with you because it transformed how I build applications, and I believe it can do the same for you.

When I first encountered tRPC, I was skeptical. Another framework? But its simplicity won me over. tRPC uses TypeScript’s type system to automatically share types between your server and client. Imagine calling a backend procedure from your frontend with full IntelliSense support and compile-time error checking. No more guessing about response shapes or manually keeping types aligned. The development experience feels almost magical. Why settle for traditional REST or GraphQL when you can have this level of integration?

Setting up the project is straightforward. I start with a new Next.js application using the TypeScript template. The key dependencies include @trpc/server and @trpc/client for the core functionality, Prisma for database operations, and Zod for validation. Here’s how I initialize the project:

npx create-next-app@latest my-trpc-app --typescript --tailwind --app
cd my-trpc-app
npm install @trpc/server @trpc/client @trpc/next @tanstack/react-query prisma @prisma/client zod

After installation, I configure Prisma to handle the database layer. Prisma’s schema language makes defining models intuitive. For instance, in a blog application, I might define User, Post, and Comment models with proper relations. Running npx prisma migrate dev creates the database tables, and npx prisma generate builds the type-safe client. Did you know that Prisma’s migration system tracks schema changes automatically, making database evolution painless?

With the database ready, I create tRPC procedures. These are essentially type-safe API endpoints. Each procedure defines its input validation using Zod and specifies the return type. Here’s a simple query to fetch posts:

import { z } from 'zod';
import { procedure, router } from '../trpc';

export const postRouter = router({
  list: procedure
    .input(z.object({ limit: z.number().default(10) }))
    .query(async ({ input, ctx }) => {
      return await ctx.db.post.findMany({
        take: input.limit,
        include: { author: true },
      });
    }),
});

On the frontend, integrating with Next.js is seamless. I set up a tRPC client that leverages React Query for caching and state management. Calling the backend procedure feels like invoking a local function, but with network awareness. For example:

const { data: posts, isLoading } = trpc.post.list.useQuery({ limit: 5 });

This code fetches posts with full type checking. If I try to pass an invalid parameter, TypeScript catches it immediately. How often have you wasted time debugging API calls that should have been caught at compile time?

Error handling is built into the framework. I use Zod to validate inputs and tRPC’s error formatting to provide clear feedback. Middleware allows me to add authentication or logging globally. For instance, I can create a protected procedure that requires user authentication:

const protectedProcedure = procedure.use(({ ctx, next }) => {
  if (!ctx.user) {
    throw new Error('Not authenticated');
  }
  return next({ ctx: { ...ctx, user: ctx.user } });
});

Performance optimizations come naturally. tRPC batches requests automatically, and React Query handles caching. I can prefetch data on the server with Next.js and hydrate it on the client, reducing load times. Deployment to platforms like Vercel is straightforward since the entire stack is designed for modern web environments.

Throughout this process, I’ve learned a few lessons. Always validate inputs rigorously. Use Prisma’s relation includes wisely to avoid N+1 queries. Test your procedures with both happy and error paths. The type safety doesn’t eliminate bugs, but it catches most common mistakes early.

I hope this guide inspires you to try this stack. The reduction in boilerplate and increase in confidence is transformative. If you found this helpful, please like and share this article. I’d love to hear about your experiences in the comments—what challenges have you faced with API development, and how might tRPC help? Let’s build more reliable software together.