The Tech Stack Decision

Your technology stack is one of the most consequential early decisions for a startup. It affects development speed, hiring difficulty, scalability, and operational costs. The good news: there are many excellent choices. The bad news: analysis paralysis can delay your launch by months. At Nexis Limited, we help startups make this decision based on a practical framework, not trends.

The Decision Framework

1. Team Expertise Comes First

The most important factor is what your team knows well. A Python expert builds faster in Django than learning Rust, regardless of theoretical performance advantages. The best tech stack is the one your team can ship with. Hire for skills you already have unless there is a compelling technical reason to adopt something new.

2. Speed to Market Matters Most

Startups fail because they run out of runway, not because they chose PostgreSQL instead of MongoDB. Optimize for developer velocity — frameworks with batteries included, mature ecosystems, and abundant documentation. You can always re-architect when you have product-market fit and revenue.

3. Boring Technology Is Good Technology

Proven, well-understood technologies have fewer surprises, better documentation, and larger talent pools than cutting-edge alternatives. PostgreSQL, React, Python, Node.js, and Go are boring — and that is why they are excellent startup choices.

Recommended Stacks by Use Case

Web SaaS Application

  • Frontend: Next.js with TypeScript, Tailwind CSS.
  • Backend: Django (Python), Express/Fastify (Node.js), or Go with Gin.
  • Database: PostgreSQL — the most versatile and reliable choice.
  • Cache: Redis for session management and caching.
  • Hosting: Vercel (frontend), Railway or Render (backend), or AWS/GCP for more control.

Mobile Application

  • Cross-platform: React Native (JavaScript team) or Flutter (new project, custom UI emphasis).
  • Backend: Firebase for rapid prototyping, custom backend for production-grade features.
  • Consider: Start with a PWA if your use case allows it — avoid app store friction entirely.

Data-Intensive Application

  • Backend: Python (FastAPI) with pandas, NumPy, and scikit-learn.
  • Database: PostgreSQL with TimescaleDB for time-series data, or ClickHouse for analytics.
  • Pipeline: Apache Airflow or Dagster for data orchestration.

What to Avoid

  • Microservices at launch: Start with a monolith. Extract services only when specific components need independent scaling.
  • Multiple languages: One backend language keeps hiring simpler and code sharing easier.
  • Building from scratch: Use managed services (database, authentication, file storage) instead of running your own.
  • Premature optimization: Do not build for 10 million users when you have 100. Scale when needed.

The Nexis Limited Stack

We built four SaaS products with a focused stack: Next.js and React for frontends, Django and Go for backends, PostgreSQL for databases, Redis for caching, and Docker with Kubernetes for deployment. This stack serves products from simple digital menus to complex logistics platforms.

Conclusion

Choose a tech stack your team knows, optimize for development speed, and use boring, proven technologies. You can always evolve your stack as your business grows and your requirements become clearer.

Starting a new project? Talk to us about choosing the right architecture.