Next.js 15 + TypeScript + PostgreSQL: The Best MVP Stack in 2025 | Autonow

The Perfect MVP Stack 2025: Next.js 15 + TypeScript + PostgreSQL

"Choosing the wrong stack is like building a house on sand — you don't see the problem until you've invested too much to turn back."

In 2025-2026, the tech stack debate has been settled. State of JS 2024, the Stack Overflow Developer Survey, and thousands of battle-tested startups all agree: Next.js + TypeScript + PostgreSQL is the dominant trio.

But knowing what isn't enough. This article explains why — from the technical perspective of a team that ships MVPs weekly, not from framework marketing.

1. Next.js 15: Dissolving the Frontend/Backend Boundary

App Router — The Real Revolution

Next.js 15 with App Router isn't just new routing. It's a completely different mental model: React Server Components (RSC) as the default.

In the old Pages Router, every component ran on the client. In App Router:

app/
├── layout.tsx          ← Server Component (HTML shell)
├── page.tsx            ← Server Component by default
├── dashboard/
│   ├── page.tsx        ← Server Component
│   └── ClientChart.tsx ← 'use client' only when interactivity needed
└── api/
    └── webhook/
        └── route.ts    ← API Route handler

Real-world RSC benefits:

// app/dashboard/page.tsx — Server Component
// No useState, useEffect, loading states, or client error handling needed
// Database query runs server-side — data never exposed to client
import { db } from '@/lib/db'

export default async function DashboardPage() {
  // Direct database query — no API route needed
  const stats = await db.query.metrics.findMany({
    where: (m, { gte }) => gte(m.createdAt, new Date(Date.now() - 86400000)),
    orderBy: (m, { desc }) => desc(m.value),
    limit: 10,
  })

  return (
    <div>
      <h1>Dashboard</h1>
      {stats.map(s => <MetricCard key={s.id} metric={s} />)}
    </div>
  )
}

Compared to Pages Router:

No getServerSideProps
No /api/metrics endpoint
No useState + useEffect to fetch data
Smaller bundle size — database client never ships to the browser

Server Actions — The Real Game Changer

Server Actions fundamentally change how you write forms and mutations:

// app/actions/create-project.ts
'use server'

import { db } from '@/lib/db'
import { revalidatePath } from 'next/cache'
import { redirect } from 'next/navigation'
import { z } from 'zod'

const CreateProjectSchema = z.object({
  name: z.string().min(1).max(100),
  description: z.string().optional(),
})

export async function createProject(formData: FormData) {
  const validated = CreateProjectSchema.safeParse({
    name: formData.get('name'),
    description: formData.get('description'),
  })

  if (!validated.success) {
    return { error: validated.error.flatten().fieldErrors }
  }

  const project = await db.insert(projects).values(validated.data).returning()
  revalidatePath('/dashboard')
  ()
}


   () {
   (
    
  )
}

Why Server Actions are revolutionary:

No fetch('/api/...') in components
No separate API route handlers
TypeScript types flow automatically from server to client
Progressive enhancement: works even with JS disabled
CSRF protection built-in

PPR — Partial Prerendering (Stable in Next.js 15)

PPR is a hybrid rendering model: static parts render instantly (0ms), dynamic parts stream in afterwards.

// app/product/[id]/page.tsx
import { Suspense } from 'react'

export default async function ProductPage({ params }) {
  return (
    <div>
      {/* Static: renders immediately, cached forever */}
      <ProductHero productId={params.id} />

      {/* Dynamic: streams in after data is ready */}
      <Suspense fallback={<ReviewsSkeleton />}>
        <ProductReviews productId={params.id} />
      </Suspense>

      <Suspense fallback={<PricingSkeleton />}>
        <DynamicPricing productId={params.id} />
      </Suspense>
    </div>
  )
}

Result: Users see product information instantly (from static cache) while reviews and pricing load dynamically. Exceptional perceived performance without compromising on dynamic data.

Turbopack — 5x Faster Dev Experience

Next.js 15 ships Turbopack stable (Rust-based bundler):

Cold start: from 4-8s down to 0.7-1.2s
HMR (Hot Module Replacement): from 200-500ms down to 20-50ms
On codebases with 1000+ modules, the difference is felt immediately

2. TypeScript: Type-Safety Is Infrastructure, Not a Luxury

Why a 5-Person Team Needs TypeScript More Than a 1-Person Team

Common myth: "TypeScript is for big teams; startups don't need it."

Reality: Startups need TypeScript more because:

No bandwidth to write comprehensive documentation
Constant context switching — you forget your own code after 2 weeks
Onboarding new developers without proper docs
Refactoring without type safety is gambling

// Without TypeScript — you're guessing everything
function processOrder(order) {
  // What is order.items? Is order.userId a string or number?
  // What are the valid status values?
  // Must read database schema, read code at every call site
  return order.items.reduce((sum, item) => sum + item.price * item.qty, 0)
}

// With TypeScript — self-documenting
interface OrderItem {
  productId: string
  name: string
  price: number        // cents, not dollars
  qty: number
  discount?: number    // 0-1 percentage
}

type OrderStatus = 'pending' | 'processing' | 'shipped' | 'delivered' | 'cancelled'

interface Order {
  id: string
  userId: string
  items: OrderItem[]
  status: OrderStatus
  createdAt: Date
  shippedAt?: Date
}

function processOrder(order: Order): number {
   order..(
     sum + item. * item. * ( - (item. ?? )),
    
  )
}

Strict Mode — Not Optional

Production-ready tsconfig.json:

{
  "compilerOptions": {
    "target": "ES2022",
    "lib": ["dom", "dom.iterable", "ES2022"],
    "allowJs": true,
    "skipLibCheck": true,
    "strict": true,
    "noUncheckedIndexedAccess": true,
    "exactOptionalPropertyTypes": true,
    "noImplicitReturns": true,
    "noFallthroughCasesInSwitch": true,
    "forceConsistentCasingInFileNames": true,
    "noEmit": true,
    "esModuleInterop": true,

strict: true enables 6 checks simultaneously:

strictNullChecks: Can't use undefined without a null check
strictFunctionTypes: Function parameter types must match strictly
strictBindCallApply: Type-check .bind(), .call(), .apply()
noImplicitAny: TypeScript can't silently infer any
strictPropertyInitialization: Class properties must be initialized
noImplicitThis: this must have an explicit type

Practical Utility Types for MVPs

// From database schema
type User = {
  id: string
  email: string
  passwordHash: string
  createdAt: Date
  updatedAt: Date
}

// Never expose passwordHash externally
type PublicUser = Omit<User, 'passwordHash'>

// Creating new user — id, createdAt, updatedAt are auto-generated
type CreateUserInput = Omit<User, 'id' | 'createdAt' | 'updatedAt'>

// Updating user — all fields optional
type UpdateUserInput = Partial<Omit<User, 'id' | 'createdAt'>>

// Type-safe API response wrapper
type ApiResponse<T> =
  | { success: true; data: T }
  | { success: false; error: string; code: number }

Zod + TypeScript: One Schema, Two Benefits

import { z } from 'zod'

// Define once — use for both validation and TypeScript types
const CreateProjectSchema = z.object({
  name: z.string().min(1, 'Name is required').max(100),
  description: z.string().max(500).optional(),
  budget: z.number().min(0).max(1_000_000_000),
  deadline: z.string().datetime().optional(),
  tags: z.array(z.string()).max(10).default([]),
})

// TypeScript type automatically inferred from schema
type CreateProjectInput = z.infer<typeof CreateProjectSchema>

// Use in Server Action — input already validated
export async function createProject(input: CreateProjectInput) {
  // TypeScript knows the exact shape
}

3. PostgreSQL: Unmatched After 35 Years — And Getting Stronger

Why Not MongoDB, DynamoDB, or PlanetScale?

After migrating multiple projects, here's the honest comparison:

Criteria	PostgreSQL	MongoDB	DynamoDB
ACID transactions	Full	Partial (single doc)	Limited
Relational queries	Native JOIN	Aggregation pipeline (verbose)	Scan heavy
JSON support	JSONB (indexed)	Native	AttributeValue
Full-text search	Built-in	Atlas Search ($$)	OpenSearch needed
Vector similarity	pgvector	Atlas Vector Search ($$)	External needed
Schema flexibility	JSONB columns	Schemaless	Schemaless
Query language	SQL (universal)	MQL (proprietary)	PartiQL (limited)
Serverless options	Neon, Supabase	Atlas Serverless	Native
Cost at scale	Linear	Expensive at scale	Expensive at scale

PostgreSQL wins on almost every dimension that matters for a startup that needs complex queries in the future.

JSONB — As Flexible as NoSQL, as Powerful as Relational

-- Users table with JSONB metadata
CREATE TABLE users (
  id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email       TEXT UNIQUE NOT NULL,
  metadata    JSONB DEFAULT '{}',
  created_at  TIMESTAMPTZ DEFAULT NOW()
);

-- GIN index on JSONB — fast queries even without knowing structure upfront
CREATE INDEX idx_users_metadata ON users USING GIN (metadata);

-- Insert with nested JSON
INSERT INTO users (email, metadata) VALUES (
  '[email protected]',
  '{"plan": "pro", "features": ["ai", "analytics"], "company": {"name": "ACME", "size": 50}}'
);

-- Query into nested JSON
SELECT * FROM users
WHERE metadata->>'plan' = 'pro'
  AND metadata->'company'->>'size' > '20';

-- Update nested field atomically — no fetch-then-update needed
UPDATE users
SET metadata = jsonb_set(metadata, '{company,size}', '100')
WHERE id = 'some-uuid';

-- Array membership check
SELECT * FROM users
WHERE metadata->'features' ? 'ai';

JSONB lets you ship fast (no schema migrations every time requirements change) while retaining relational joins and ACID guarantees.

pgvector — PostgreSQL Becomes a Vector Database

-- Install extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Documents table with vector embeddings
CREATE TABLE documents (
  id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content     TEXT NOT NULL,
  embedding   VECTOR(1536),  -- OpenAI text-embedding-3-small dimension
  metadata    JSONB DEFAULT '{}',
  created_at  TIMESTAMPTZ DEFAULT NOW()
);

-- HNSW index for approximate nearest neighbor search
-- Faster than ivfflat at query time, slower to build
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

-- Semantic similarity search (RAG)
SELECT
  content,
  metadata,
  1 - (embedding <=> $1::vector) AS similarity
FROM documents
WHERE 1 - (embedding <=> $1::vector) > 0.7  -- similarity threshold
ORDER BY embedding <=> $1::vector
LIMIT 5;

The implication: you don't need Pinecone, Weaviate, or Qdrant. PostgreSQL + pgvector handles most RAG use cases for startups — one less service to manage, one less bill to pay.

Window Functions and CTEs — Power Analytics

-- Running revenue total by day
SELECT
  DATE_TRUNC('day', created_at) AS day,
  SUM(amount) AS daily_revenue,
  SUM(SUM(amount)) OVER (ORDER BY DATE_TRUNC('day', created_at)) AS cumulative_revenue
FROM payments
WHERE status = 'completed'
GROUP BY 1
ORDER BY 1;

-- Top users per month (ranked)
WITH monthly_activity AS (
  SELECT
    user_id,
    DATE_TRUNC('month', created_at) AS month,
    COUNT(*) AS action_count
  FROM user_events
  GROUP BY 1, 2
),
ranked AS (
  SELECT *,
    RANK() OVER (PARTITION BY month ORDER BY action_count DESC) AS rank
  FROM monthly_activity
)
SELECT * FROM ranked WHERE rank <= 10;

4. ORM Battle: Drizzle vs Prisma — Drizzle Wins Clearly

Prisma: Familiar but With Real Trade-offs

Prisma is the most popular ORM for Node.js. But there are real-world problems:

Separate schema language (not TypeScript):

// schema.prisma — its own language, not TypeScript
model User {
  id        String   @id @default(cuid())
  email     String   @unique
  posts     Post[]
  createdAt DateTime @default(now())
}

Prisma's actual problems:

Prisma Client generates code into /node_modules — slow cold starts (critical for serverless/edge)
n+1 problem if include isn't used correctly
Large bundle size (~4MB unpacked)
Edge Runtime support is limited
Separate schema language = an extra "language" to learn

Drizzle: TypeScript First, SQL Second

// schema.ts — Pure TypeScript, no proprietary DSL
import { pgTable, uuid, text, timestamp, jsonb, vector } from 'drizzle-orm/pg-core'

export const users = pgTable('users', {
  id: uuid('id').primaryKey().defaultRandom(),
  email: text('email').unique().notNull(),
  metadata: jsonb('metadata').$type<{
    plan: 'free' | 'pro' | 'enterprise'
    features: string[]
  }>().default({}),
  createdAt: timestamp('created_at').defaultNow().notNull(),
})

export const documents = pgTable('documents', {
  id: uuid('id').primaryKey().defaultRandom(),
  content: text('content').notNull(),
  embedding: vector('embedding', { dimensions: 1536 }),
  userId: uuid('user_id').references(() => users., { :  }),
  : ().().(),
})


   =  users.
   =  users.

Drizzle queries — type-safe and close to SQL:

import { db } from '@/lib/db'
import { users, documents } from '@/lib/schema'
import { eq, and, sql } from 'drizzle-orm'

// Simple query
const user = await db.query.users.findFirst({
  where: eq(users.email, '[email protected]'),
})
// TypeScript knows: user is User | undefined

// Join with aggregation
const userWithDocs = await db
  .select({
    id: users.id,
    email: users.email,
    docCount: sql<number>`count(${documents.id})::int`,
  })
  .from(users)
  .leftJoin(documents, eq(users.id, documents.userId))
  .where(eq(users.id, userId))
  .groupBy(users.id, users.email)

// Semantic search with pgvector
const similarDocs = await db
  .select()
  .from(documents)
  .where(
    and(
      eq(documents.userId, userId),
      sql`1 - (${documents.embedding} <=> ${embedding}::vector) > 0.7`
    )
  )
  .(sql)
  .()

Drizzle vs Prisma comparison:

Criteria	Drizzle	Prisma
Bundle size	~100KB	~4MB
Cold start (serverless)	20-50ms	200-500ms
Edge Runtime	Full support	Limited
Type safety	SQL-level inference	Schema-level
Raw SQL escape hatch	`sql` tagged template	`$queryRaw`
Migrations	Auto-generated TypeScript	Auto-generated SQL
Schema language	TypeScript	Prisma DSL
Mode	Both ORM and Query Builder	ORM primarily
pgvector support	Native	Extension needed

Verdict: For Next.js 15 + Vercel Edge Functions + pgvector use cases, Drizzle is the clear choice.

Database Connection with Neon Serverless

// lib/db.ts
import { neon } from '@neondatabase/serverless'
import { drizzle } from 'drizzle-orm/neon-http'
import * as schema from './schema'

// HTTP-based connection — works in Edge Runtime
const sql = neon(process.env.DATABASE_URL!)
export const db = drizzle(sql, { schema })

// lib/db-pool.ts — for long-running Node.js processes
import { Pool } from '@neondatabase/serverless'
import { drizzle } from 'drizzle-orm/neon-serverless'

const pool = new Pool({ connectionString: process.env.DATABASE_URL })
export const dbPool = drizzle(pool, { schema })

5. shadcn/ui + Tailwind CSS v4 — The Perfect UI Layer

shadcn/ui: The Copy Philosophy

shadcn/ui isn't a component library in the traditional sense. Instead of npm install shadcn-ui and getting a black box, you copy source code directly into your project:

npx shadcn@latest init
npx shadcn@latest add button card dialog form input table

Result: Component code lives in components/ui/ — fully owned by you:

components/
├── ui/
│   ├── button.tsx      ← Source code you own
│   ├── card.tsx
│   ├── dialog.tsx
│   ├── form.tsx
│   └── table.tsx
└── features/
    ├── ProjectCard.tsx  ← Composed from ui components
    └── UserAvatar.tsx

Why this philosophy wins:

Not locked into library update cycles
Can modify any component for business requirements
TypeScript types are clear because you read the source
Bundle only includes components you actually use
Radix UI primitives underneath = accessibility built-in

Tailwind CSS v4 — Key Changes

Tailwind v4 (2025) changes significantly from v3:

/* tailwind.css — v4 config in CSS, not JavaScript */
@import "tailwindcss";

@theme {
  --color-primary: oklch(0.6 0.2 250);
  --color-primary-foreground: oklch(0.98 0 0);

  --font-sans: "Inter Variable", sans-serif;
  --font-mono: "JetBrains Mono", monospace;

  --radius-sm: 0.375rem;
  --radius-md: 0.5rem;
  --radius-lg: 0.75rem;
}

/* Custom utilities */
@utility flex-center {
  display: flex;
  align-items: center;
  justify-content: center;
}

v4 changes you need to know:

Config moves to CSS @theme — no more tailwind.config.ts
CSS variables replace hardcoded values — easier dark mode
@utility directive for custom utilities
Vite plugin replaces PostCSS for better performance
Native oklch() color space support

6. Auth: Supabase Auth vs Auth.js — Choose by Use Case

Supabase Auth — Simple, Integrated, Sufficient

When to choose Supabase Auth:

Already using Supabase for the database (zero friction)
Need email/password + social logins fast
Want Row Level Security (RLS) integrated with auth
Small team, no DevOps capacity

// lib/supabase/server.ts
import { createServerClient } from '@supabase/ssr'
import { cookies } from 'next/headers'

export async function createClient() {
  const cookieStore = await cookies()
  return createServerClient(
    process.env.NEXT_PUBLIC_SUPABASE_URL!,
    process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!,
    {
      cookies: {
        getAll: () => cookieStore.getAll(),
        setAll: (cookiesToSet) => {
          cookiesToSet.forEach(({ name, value, options }) =>
            cookieStore.set(name, value, options)
          )
        },
      },
    }
  )
}

// app/auth/login/actions.ts
'use server'

export async function login(formData: FormData) {
  const supabase = await createClient()
  const { error } = await supabase.auth.signInWithPassword({
    email: formData.get('email') as string,
    password: formData.get()  ,
  })
   (error)  { : error. }
  ()
}

Auth.js (NextAuth v5) — When You Need More Control

When to choose Auth.js:

Need complex custom session logic
Need to integrate with an existing user database
Need Enterprise SSO (SAML, LDAP)
Don't want vendor lock-in to Supabase

// auth.ts — Auth.js v5 config
import NextAuth from 'next-auth'
import { DrizzleAdapter } from '@auth/drizzle-adapter'
import Google from 'next-auth/providers/google'
import GitHub from 'next-auth/providers/github'
import Credentials from 'next-auth/providers/credentials'
import { db } from '@/lib/db'

export const { handlers, signIn, signOut, auth } = NextAuth({
  adapter: DrizzleAdapter(db),
  providers: [
    Google,
    GitHub,
    Credentials({
      async authorize(credentials) {
        const user = await db.query.users.findFirst({
          where: eq(users.email, credentials.email as string)
        })
        if (!user) return null
        const valid = await bcrypt.compare(
          credentials.password as string,
          user.passwordHash
        )
         valid ? user : 
      }
    })
  ],
  : {
     () {
      session.. = user.
       session
    }
  },
})

Side-by-side comparison:

Criteria	Supabase Auth	Auth.js v5
Setup time	15 minutes	45-60 minutes
Social providers	20+	80+
Magic link	Built-in	Plugin needed
MFA / TOTP	Built-in	Manual
Custom session	Limited	Full control
Database agnostic	No (Supabase)	Yes
RLS integration	Native	Manual
Enterprise SSO	Dashboard config	Custom provider
Self-hosted	Supabase self-host	Yes (any DB)

7. Production-Ready Boilerplate — Real Configuration

Project Structure

├── app/
│   ├── (auth)/
│   │   ├── login/
│   │   │   ├── page.tsx
│   │   │   └── actions.ts
│   │   └── register/
│   ├── (dashboard)/
│   │   ├── layout.tsx     ← Protected layout
│   │   ├── page.tsx
│   │   └── settings/
│   ├── api/
│   │   ├── webhooks/
│   │   │   └── stripe/route.ts
│   │   └── ai/
│   │       └── chat/route.ts
│   ├── globals.css
│   ├── layout.tsx         ← Root layout
│   └── page.tsx           ← Landing page
├── components/
│   ├── ui/                ← shadcn/ui components
│   └── features/          ← Business logic components
├── lib/
│   ├── db.ts              ← Drizzle client
│   ├── schema.ts          ← Database schema
│   ├── auth.ts            ← Auth config
│   └── utils.ts           ← cn(), formatDate(), etc.
├── middleware.ts           ← Auth protection
├── drizzle.config.ts
├── next.config.ts
└── tailwind.css

Essential Environment Variables

# .env.local
DATABASE_URL="postgresql://..."
NEXT_PUBLIC_SUPABASE_URL="https://xxx.supabase.co"
NEXT_PUBLIC_SUPABASE_ANON_KEY="eyJ..."
SUPABASE_SERVICE_ROLE_KEY="eyJ..."

# Stripe
STRIPE_SECRET_KEY="sk_test_..."
STRIPE_WEBHOOK_SECRET="whsec_..."
NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY="pk_test_..."

# Email
RESEND_API_KEY="re_..."
FROM_EMAIL="[email protected]"

# AI
OPENAI_API_KEY="sk-..."
ANTHROPIC_API_KEY="sk-ant-..."

# App
NEXT_PUBLIC_APP_URL="http://localhost:3000"

Drizzle Migrations Workflow

# Generate migration from schema changes
npx drizzle-kit generate

# Apply migrations to database
npx drizzle-kit migrate

# Visual database browser
npx drizzle-kit studio

8. AI-Ready Architecture: RAG Pipelines and AI Agents

Complete RAG Pipeline with pgvector

// lib/rag.ts — Full RAG implementation
import { openai } from '@ai-sdk/openai'
import { embed, generateText } from 'ai'
import { db } from './db'
import { documents } from './schema'
import { sql, and, eq } from 'drizzle-orm'

// Step 1: Ingest document — embed and store
export async function ingestDocument(
  content: string,
  metadata: Record<string, unknown>,
  userId: string
) {
  const { embedding } = await embed({
    model: openai.embedding('text-embedding-3-small'),
    value: content,
  })

  await db.insert(documents).values({ content, embedding, metadata, userId })
}

// Step 2: Semantic search
export async function semanticSearch(query: string, userId: string, limit = 5) {
  const { embedding: queryEmbedding } = await embed({
    model: openai.embedding('text-embedding-3-small'),
    : query,
  })

   db
    .({
      : documents.,
      : documents.,
      : sql<>,
    })
    .(documents)
    .(
      (
        (documents., userId),
        sql
      )
    )
    .(sql)
    .(limit)
}


   () {
   relevantDocs =  (question, userId)
   context = relevantDocs.( d.).()

   { text } =  ({
    : (),
    : ,
    : [
      { : , :  }
    ]
  })

   { : text, : relevantDocs }
}

AI Agent with Tool Calling

// app/api/ai/agent/route.ts
import { openai } from '@ai-sdk/openai'
import { streamText, tool } from 'ai'
import { z } from 'zod'

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

  const result = streamText({
    model: openai('gpt-4o'),
    messages,
    tools: {
      searchDocuments: tool({
        description: 'Search user documents using semantic similarity',
        parameters: z.object({ query: z.string() }),
        execute: async ({ query }) => semanticSearch(query, userId),
      }),
      createTask: tool({
        description: 'Create a new task for the user',
        parameters: z.object({
          title: z.string(),
          description: z.string().optional(),
          dueDate: z.string().optional(),
        }),
        execute: async (input) => {
          return db.insert(tasks).({ ...input, userId }).()
        },
      }),
      : ({
        : ,
        : z.({
          : z.([, , ]),
        }),
        :  ({ period }) => {
          
        },
      }),
    },
    : ,
  })

   result.()
}

Streaming Chat UI

// app/chat/page.tsx
'use client'

import { useChat } from 'ai/react'

export default function ChatPage() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: '/api/ai/agent',
  })

  return (
    <div className="flex flex-col h-screen max-w-2xl mx-auto p-4">
      <div className="flex-1 overflow-y-auto space-y-4">
        {messages.map(message => (
          <div
            key={message.id}
            className={`p-3 rounded-lg ${
              message.role === 'user'
                ? 'bg-primary text-primary-foreground ml-8'
                : 'bg-muted mr-8'
            }`}
          >
            {message.content}
          </div>
        ))}
      </div>

      <form onSubmit={handleSubmit} className="mt-4 flex gap-2">
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Ask your AI assistant..."
          className=
        />
        
          Send
        
      
    
  )
}

9. Production Checklist — Before You Go Live

// next.config.ts — Production hardening
import type { NextConfig } from 'next'

const nextConfig: NextConfig = {
  images: {
    formats: ['image/avif', 'image/webp'],
    remotePatterns: [
      { protocol: 'https', hostname: 'cdn.yourdomain.com' },
    ],
  },
  async headers() {
    return [
      {
        source: '/(.*)',
        headers: [
          { key: 'X-Frame-Options', value: 'DENY' },
          { key: 'X-Content-Type-Options', value: 'nosniff' },
          { key: 'Referrer-Policy', value: 'strict-origin-when-cross-origin' },
        ],
      },
    ]
  },
}

export default nextConfig

Pre-launch checklist:

Conclusion: Why This Stack Still Wins in 2026

There's no perfect stack. But there is a stack that is good enough for most use cases, with the largest ecosystem, the easiest-to-hire talent pool, and the best documentation.

Next.js + TypeScript + PostgreSQL checks all three boxes — and with pgvector, Vercel AI SDK, and Drizzle added, it becomes an AI-native stack without any additional external services.

For startups that need to move fast:

Next.js 15 eliminates the frontend/backend boundary
TypeScript turns your codebase into self-writing documentation
PostgreSQL + pgvector is the only database you need for both OLTP and AI workloads
Drizzle maintains type safety from the database all the way to the UI
shadcn/ui ships production UI in hours, not days

This isn't the stack of 2020 or 2022. This is the stack of 2026 and the years ahead — designed for an AI-first, edge-first, developer-experience-first world.

Want to ship your MVP with this stack in 7 days? See Autonow's MVP service or read more about the real cost of building an MVP.

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