Choosing the Right Tech Stack for Your MVP
Eight tech stack decisions every MVP needs: with recommended defaults, when to deviate, and the AI-specific additions that most stack guides miss.
8 Stack Decisions for Your MVP
Next.js 14+ (App Router)
Why this default
Full-stack React with built-in routing, server components, API routes, and zero-config Vercel deployment. Supports streaming for AI features natively.
When to deviate
Use Vite + React for purely client-side apps with a separate API. Use SvelteKit if your team has strong Svelte experience. Don't use Create React App: it's deprecated.
TypeScript throughout
Why this default
Catches 70%+ of bugs at compile time. IDE autocompletion dramatically speeds development. Non-negotiable for any production codebase beyond one person.
When to deviate
Never deviate from TypeScript for production web apps. If your team insists on JavaScript, this is a hiring/culture problem, not a technical decision.
Tailwind CSS + shadcn/ui
Why this default
Tailwind for utility-first styling with zero CSS specificity issues. shadcn/ui for accessible, customisable components built on Radix primitives: no separate component library dependency.
When to deviate
Use CSS Modules for teams that prefer semantic class names. Use MUI or Chakra if you have significant existing design system work there. Don't use plain CSS: specificity at scale is unmanageable.
Clerk
Why this default
Handles email, social OAuth, magic links, MFA, and organisation management out of the box. Production-ready in one afternoon. $25/month vs. weeks of custom auth development.
When to deviate
Use Supabase Auth if you're already on Supabase and want a single vendor. Use Auth.js for teams that need full auth control and are willing to manage it. Build custom auth only for very specific compliance requirements that off-the-shelf can't meet.
Supabase (managed Postgres)
Why this default
Postgres (the world's most capable open-source database), managed with zero-config backups, row-level security, realtime subscriptions, and a generous free tier. Also provides auth, storage, and vector search (pgvector).
When to deviate
Use PlanetScale (MySQL) if your team has MySQL expertise. Use Neon for serverless Postgres with branch-per-preview. Use a self-managed Postgres (Railway, Render) for more cost control at scale.
Vercel AI SDK + OpenAI GPT-4o
Why this default
Vercel AI SDK handles streaming, multi-turn conversation, tool use, and provider switching with a unified interface. GPT-4o for capability; switch to GPT-4o-mini for cost-sensitive flows.
When to deviate
Use Anthropic Claude 3.5 Sonnet for reasoning-heavy tasks and long-context documents. Use Llama 3 (self-hosted) for data that can't leave your infrastructure. Use LangChain / LangGraph for agent orchestration beyond basic tool use.
Vercel
Why this default
Zero-config deployment from GitHub push, global CDN, automatic preview deployments per PR, and native integration with Next.js. Free tier is generous for MVPs.
When to deviate
Use Railway or Render for longer-running processes (background workers, WebSockets) that Vercel's serverless model doesn't support. Use AWS / GCP / Azure for enterprise clients with infrastructure requirements or AI workloads needing GPU.
Sentry + Vercel Analytics + Langfuse
Why this default
Sentry for error tracking and performance. Vercel Analytics for Core Web Vitals and traffic. Langfuse for LLM cost monitoring, prompt versioning, and output quality tracking.
When to deviate
Add PostHog for product analytics (user behaviour, funnels, A/B tests). Add Datadog or Grafana for infrastructure monitoring in more complex deployments.
Frequently Asked Questions
What is the best tech stack for an AI MVP in 2026?
The default AI MVP stack in 2026: Next.js 14 (App Router), TypeScript, Tailwind CSS + shadcn/ui, Clerk for auth, Supabase for Postgres, Vercel AI SDK for LLM streaming, OpenAI or Anthropic API for the LLM, Langfuse for LLM observability, and Vercel for deployment. This stack is well-supported, has extensive AI-specific tooling, and deploys globally in minutes. It works for 90%+ of AI MVPs: deviate only when you have a specific reason to.
Should I use a monorepo or separate repositories for my MVP?
Single repository for the MVP. Monorepos (Turborepo, Nx) make sense when you have multiple deployable applications sharing code: a web app, mobile app, and API that all use the same types. For an MVP, you have one application, and the monorepo overhead (build configuration, workspace management) slows you down with no benefit. Start with a single Next.js repo; split into a monorepo when you genuinely have multiple apps that share code.
Do I need a separate backend for my Next.js MVP?
No, Next.js Route Handlers replace a separate Express or FastAPI backend for most MVP use cases. API routes live in the same codebase, share TypeScript types with the frontend, and deploy together on Vercel. Add a separate backend when: your AI processing is CPU/GPU intensive (use FastAPI in a container); you need background job workers that can't run as serverless functions; or your team has an existing backend they're extending.
How important is tech stack choice for startup success?
Less important than execution, but not irrelevant. The right stack lets a small team move fast without scaling-related rewrites. The wrong stack creates friction that slows feature delivery and onboarding. The main risks: over-engineering the stack for a scale you haven't reached (premature Kubernetes, microservices), under-engineering for AI features (no streaming, no cost monitoring), or choosing niche technology that limits your hiring pool. The defaults recommended here are deliberately mainstream: there's always someone who can work with them.
When should I switch stacks?
Almost never switch stacks mid-product. A partial rewrite is more expensive than continuing with an imperfect stack. The exception: your current stack has a hard technical blocker for a feature that's core to your value proposition (e.g., Python-only ML libraries that can't be accessed from a Node.js backend). In that case: add a separate Python service for the specific capability rather than rewriting the entire stack.
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