Blog/Development

Best Tech Stack for Resume Builder in 2025

Framework comparison, database analysis, and tool recommendations for building a modern resume builder SaaS.

📅 Published: 2025-12-17🔄 Last Updated: 2025-12-17✓ Manually Reviewed

TL;DR: Recommended Stack

Frontend

Next.js 15 + React 19 + TypeScript

Styling

Tailwind CSS + Shadcn/ui

Database

PostgreSQL + Prisma ORM

PDF Generation

Puppeteer (server-side)

Authentication

Clerk or NextAuth.js

Payments

Stripe

Tech Stack Overview

Building a production-ready resume builder requires careful technology selection. The right stack balances developer experience, performance, scalability, and maintainability. This guide compares options based on real-world resume builder requirements.

Key Requirements for Resume Builders

User-Facing Needs

  • Fast, responsive editor interface
  • Real-time preview updates
  • Multiple template rendering
  • PDF/DOCX export
  • Mobile-friendly design

Technical Needs

  • SEO-friendly architecture
  • Scalable PDF generation
  • Secure authentication
  • Payment processing
  • Data persistence and backups

Frontend Framework Comparison

FrameworkProsConsBest For
Next.js 15SSR, API routes, SEO, React 19Learning curve, Vercel-optimizedRecommended
React + ViteFast dev, flexible, lightweightNo SSR, separate backend neededSPAs, existing backend
Vue + NuxtGreat DX, SSR, growing ecosystemSmaller talent poolVue teams
RemixNested routes, progressive enhancementNewer, smaller communityComplex routing needs

Why Next.js 15 Wins

Next.js 15 is the optimal choice for resume builders because:

  • Server Components: Faster initial load, better SEO for template gallery pages
  • API Routes: Build backend logic alongside frontend, no separate server needed
  • Image Optimization: Automatic optimization for template thumbnails
  • React 19 Support: Latest React features including improved Suspense
  • Edge Functions: Fast PDF generation at the edge

# Create a new Next.js 15 project

npx create-next-app@latest resume-builder --typescript --tailwind --eslint --app

Backend Options

Option 1: Next.js API Routes (Recommended)

For most resume builders, Next.js API routes provide sufficient backend functionality without managing separate infrastructure:


// app/api/resume/route.ts
import { NextResponse } from 'next/server'
import { prisma } from '@/lib/prisma'
import { auth } from '@/lib/auth'

export async function POST(request: Request) {
  const session = await auth()
  if (!session) {
    return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })
  }

  const data = await request.json()

  const resume = await prisma.resume.create({
    data: {
      userId: session.user.id,
      title: data.title,
      content: data.content,
      templateId: data.templateId,
    }
  })

  return NextResponse.json(resume)
}

Option 2: Separate Backend (Express/Fastify)

Consider a separate backend if you need:

  • Microservices architecture
  • Heavy background processing
  • WebSocket connections for real-time collaboration
  • Shared API across multiple frontends

Option 3: Serverless (AWS Lambda, Vercel Functions)

Serverless works well for PDF generation workloads that have variable demand:

  • Auto-scaling based on traffic
  • Pay-per-execution pricing
  • No server management
  • Cold start latency trade-off

Database Comparison

DatabaseResume DataUsers/AuthPaymentsVerdict
PostgreSQLExcellent (JSONB)ExcellentExcellentBest Choice
MongoDBExcellentGoodNeeds careGood for flexibility
MySQLGood (JSON)ExcellentExcellentSolid alternative
SupabaseExcellentBuilt-in authExcellentGreat DX option

PostgreSQL + Prisma: The Winning Combination

Prisma provides type-safe database access with excellent developer experience:


// prisma/schema.prisma
model User {
  id            String    @id @default(cuid())
  email         String    @unique
  name          String?
  resumes       Resume[]
  subscription  Subscription?
  createdAt     DateTime  @default(now())
  updatedAt     DateTime  @updatedAt
}

model Resume {
  id          String   @id @default(cuid())
  userId      String
  user        User     @relation(fields: [userId], references: [id])
  title       String
  templateId  String
  content     Json     // Flexible resume data as JSON
  isPublic    Boolean  @default(false)
  createdAt   DateTime @default(now())
  updatedAt   DateTime @updatedAt

  @@index([userId])
}

model Template {
  id          String  @id @default(cuid())
  name        String
  slug        String  @unique
  thumbnail   String
  category    String  // professional, creative, simple, modern
  isPremium   Boolean @default(false)
  config      Json    // Template-specific configuration
}

PDF Generation Deep Dive

PDF generation is the most technically challenging aspect of resume builders. Here is a comparison of approaches:

LibraryApproachQualityPerformanceComplexity
PuppeteerHTML → PDFPixel-perfectModerateMedium
PlaywrightHTML → PDFPixel-perfectGoodMedium
react-pdfProgrammaticGoodFastHigh
pdfmakeJSON → PDFGoodFastMedium
GotenbergDocker servicePixel-perfectScalableHigh (infra)

Production-Ready Puppeteer Setup


// lib/pdf-generator.ts
import puppeteer, { Browser } from 'puppeteer'

let browserInstance: Browser | null = null

async function getBrowser(): Promise<Browser> {
  if (!browserInstance) {
    browserInstance = await puppeteer.launch({
      headless: 'new',
      args: [
        '--no-sandbox',
        '--disable-setuid-sandbox',
        '--disable-dev-shm-usage',
        '--disable-gpu'
      ]
    })
  }
  return browserInstance
}

export async function generatePDF(html: string): Promise<Buffer> {
  const browser = await getBrowser()
  const page = await browser.newPage()

  try {
    await page.setContent(html, {
      waitUntil: 'networkidle0',
      timeout: 30000
    })

    // Wait for fonts to load
    await page.evaluateHandle('document.fonts.ready')

    const pdf = await page.pdf({
      format: 'A4',
      printBackground: true,
      margin: {
        top: '0.4in',
        right: '0.4in',
        bottom: '0.4in',
        left: '0.4in'
      }
    })

    return Buffer.from(pdf)
  } finally {
    await page.close()
  }
}

// Cleanup on process exit
process.on('SIGINT', async () => {
  if (browserInstance) {
    await browserInstance.close()
  }
})

Authentication Solutions

Clerk (Recommended)

  • Pre-built UI components
  • OAuth providers included
  • Webhook integrations
  • $25/month for 5K MAU
clerk.com →

NextAuth.js (Free)

  • Open source, free
  • 50+ OAuth providers
  • Database adapters
  • Full control, more setup
next-auth.js.org →

Payment Integration

Stripe is the standard choice for SaaS payments. Key features needed:

  • Checkout Sessions: Hosted payment page (fastest implementation)
  • Customer Portal: Self-service subscription management
  • Webhooks: Handle subscription events (created, canceled, updated)
  • Price Tables: Embeddable pricing components

// app/api/checkout/route.ts
import { NextResponse } from 'next/server'
import Stripe from 'stripe'

const stripe = new Stripe(process.env.STRIPE_SECRET_KEY!)

export async function POST(request: Request) {
  const { priceId, userId } = await request.json()

  const session = await stripe.checkout.sessions.create({
    mode: 'subscription',
    payment_method_types: ['card'],
    line_items: [{ price: priceId, quantity: 1 }],
    success_url: `${process.env.NEXT_PUBLIC_URL}/dashboard?success=true`,
    cancel_url: `${process.env.NEXT_PUBLIC_URL}/pricing?canceled=true`,
    metadata: { userId }
  })

  return NextResponse.json({ url: session.url })
}

AI Integration Options

ProviderBest ForCost
Google GeminiResume parsing (vision), content generation$0.075/1M tokens
OpenAI GPT-4High-quality text generation$30/1M input tokens
ClaudeLong-form content, analysis$15/1M input tokens
Local LLMsCost-sensitive, privacy-focusedHardware only

For resume parsing with document upload, Google Gemini Vision API offers the best balance of capability and cost. See our AI models comparison for detailed benchmarks.

Deployment & Hosting

PlatformBest ForStarting Cost
VercelNext.js, zero-config deploymentFree tier, $20/mo Pro
RailwayFull-stack with database$5/mo + usage
RenderContainers, background workers$7/mo per service
AWSEnterprise, full controlVariable, ~$50/mo start

Get This Stack Pre-Built

Skip months of setup with production-ready source code. Includes Next.js 15, React 19, 60+ templates, AI parser, PDF generation, authentication, and Stripe integration. Deploy to Vercel in 15 minutes.

View Source Code PackageComplete stack, ready to customize

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Frequently Asked Questions

Is Next.js or React better for a resume builder?

Next.js is recommended for resume builders because it provides server-side rendering (important for SEO), API routes (backend functionality), and optimized performance out of the box. Plain React requires additional setup for these features.

What database should I use for a resume builder?

PostgreSQL is the best choice for resume builders. It handles structured data well (user accounts, subscriptions) while also supporting JSON columns for flexible resume data. MongoDB works but offers less data integrity for transactional features.

How do I generate PDFs in a resume builder?

The two main approaches are: 1) Puppeteer/Playwright for HTML-to-PDF conversion (pixel-perfect but resource-intensive), or 2) Libraries like react-pdf for programmatic generation (lighter but less flexible). For production, Puppeteer with browser pooling is recommended.

Should I use TypeScript for a resume builder?

Yes, absolutely. TypeScript catches type errors during development, provides better IDE support, and makes refactoring safer. Resume data structures are complex, and TypeScript helps ensure consistency across your codebase.

What hosting is best for a resume builder SaaS?

Vercel is ideal for Next.js resume builders due to zero-configuration deployment, edge functions, and automatic scaling. For more control, AWS/GCP with containerized deployments work well. Expect $50-200/month for moderate traffic.

PR

Written by Pattanaik Ramswarup

AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset

I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.

✓ 10+ Years in ML/AI✓ 77K Dataset Creator✓ Open Source Contributor

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