"What tech stack should I use?" is the most common question we get from clients. The honest answer is always "it depends," but here is how we actually make that decision at HydraBytes.
The Default Stack
For most web projects, we reach for:
- Next.js with TypeScript
- PostgreSQL with Prisma ORM
- Tailwind CSS for styling
- Vercel for deployment
This is not because these are the trendiest tools. It is because this combination gives us server-side rendering, type safety, a relational database with great tooling, and zero-config deployment. For 80% of client projects, this stack lets us focus on solving the business problem instead of fighting infrastructure.
When We Deviate
Need real-time features?
If the app requires live updates (chat, dashboards, collaborative editing), we add Supabase or Firebase depending on the complexity. Supabase when we want to stay in PostgreSQL-land. Firebase when we need its offline persistence and push notification ecosystem.
Building a mobile app?
React Native with Expo is our default. The managed workflow handles 90% of what mobile apps need. We only eject or go native when hardware integration demands it (Bluetooth mesh networking in Safe-Sawar was one of those cases).
AI/ML component?
Python with FastAPI for the inference API. The model training stack varies: TensorFlow for computer vision, scikit-learn for structured data, LangChain for RAG applications. The important thing is keeping the AI service as a separate microservice that the main app calls via API. Never embed ML inference in your web server process.
Client has an existing codebase?
We work with what they have. We have maintained apps in Vue, Angular, Django, Rails, and plain PHP. Rewriting a working system to match our preferred stack is almost never the right call.
The Questions We Actually Ask
Before choosing a stack, we ask:
Who will maintain this after we hand it off? If the client has an in-house team that knows Python, building it in Next.js creates a handoff problem.
What is the expected scale? A tool for 50 internal users does not need the same architecture as a consumer app targeting 100K users.
What is the budget? Some stacks are cheaper to operate. Serverless on Vercel costs almost nothing at low traffic. A Kubernetes cluster costs money even when idle.
What is the timeline? If we need to ship in 4 weeks, we pick tools we know deeply, not tools we want to learn.
Does this need to work offline? This single requirement changes everything. Firebase, React Native's offline persistence, service workers: these are not add-ons, they are architectural decisions that need to be made from day one.
Common Mistakes We See
Choosing a stack because of a blog post
"I read that X is the future" is not a technical requirement. We have seen projects start with bleeding-edge tools and spend half their budget on workarounds for missing features and immature ecosystems.
Over-engineering for scale you do not have
Building a microservices architecture for an MVP that will have 200 users is burning money. Start monolithic, measure bottlenecks, split when you need to.
Ignoring deployment costs
A stack that is free in development can be expensive in production. GPU-powered AI inference, real-time WebSocket connections, large media storage: these costs add up. We estimate production costs before writing the first line of code.
Not considering the hiring market
If you build your app in an obscure language, hiring developers to maintain it becomes expensive and slow. Boring, popular tools have larger talent pools.
Our Recommendation
If you are starting a new project in 2026 and you do not have strong reasons to deviate: Next.js, TypeScript, PostgreSQL, Tailwind, Vercel. It is boring, it works, and you will not regret it in two years.
If you need help making this decision for your specific project, talk to us. We will give you an honest recommendation, even if that means suggesting a simpler solution than you expected.
HydraBytes is an Islamabad-based development agency building web, mobile, and AI solutions.
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