Choosing a Tech Stack for Your Startup in 2026 (Opinionated, With Reasons)
The stack matters far less than people argue and far more than people admit. Here is the short list and the reasoning behind each pick.
Choosing a stack is a hiring decision, a hosting decision, and a speed decision disguised as a technical one. Optimise for how fast a small team can change things safely.
Language: TypeScript everywhere
One language across client and server means shared types, shared validation, and one hiring pool. Python earns its place for heavy data and ML work — call it as a service rather than splitting your product code across two languages.
Framework: a full-stack React framework
Server-side rendering, file-based routing, and typed server functions in one project. The specific choice matters less than picking one with a real deployment story and not fighting it. Avoid gluing a separate SPA to a separate API server unless you have a genuine reason — you have just doubled your deployment surface for no user benefit.
Database: Postgres, and stop deliberating
Relational integrity, JSONB when you need flexibility, full-text search built in, vector search via pgvector, and every hosting provider supports it. Reach for something else only when you have a measured need Postgres provably cannot meet.
Auth: managed, not hand-rolled
Sessions, refresh rotation, OAuth providers, MFA, and password reset are a solved problem with sharp edges. Use a managed provider and keep authorisation — roles and permissions — in your own database where you can query it.
Hosting: edge-first, with escape hatches
Deploy to a platform that handles previews, rollbacks, and CDN by default. Know the runtime limits before you build: native binaries, long-running processes, and heavy filesystem work do not run at the edge, so plan a worker or container for those from the start rather than discovering it during launch week.
Payments
Stripe unless you need merchant-of-record handling for global tax, in which case Paddle or Lemon Squeezy saves you a compliance function. Model subscriptions in your own database and treat webhooks as the source of truth for state changes.
AI layer
Route through a single gateway rather than calling providers directly. You get one place for keys, budgets, logging, and model swaps — which is the difference between a model migration taking an hour and taking a sprint.
What to defer
- Kubernetes. Almost certainly not yet.
- Microservices. A modular monolith is faster to change with fewer than ten engineers.
- A custom design system. Use a component library and theme it.
- Multi-region. Until you have paying customers complaining about latency.
The actual rule
Pick boring, well-documented tools your team already knows, and spend your novelty budget on the one thing that makes your product different. Every other novel choice is a tax you pay weekly.
FAQ
Frequently asked questions
Is Postgres really enough for an AI product?+
For most, yes — pgvector handles millions of embeddings comfortably. Move to a dedicated vector store when benchmarks, not blog posts, tell you to.
Should I use a monorepo?+
If you have a web app, a marketing site, and shared types, yes. For a single app it adds tooling overhead without payoff.
How much does the stack affect fundraising?+
Almost not at all. Investors ask about traction. Engineers you are recruiting ask about the stack — that is the real constraint.
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