Every new project starts with the same question: what tech stack should we use? It's a question that provokes religious wars on Twitter, but in practice the answer is usually boring. The best tech stack is the one your team knows, that has a healthy ecosystem, and that won't be abandoned in 3 years. Here's how we think about it at Bivic Solutions, informed by 140+ projects shipped since 2017.

The front-end: why Next.js wins

For web front-ends in 2025, Next.js is our default choice. Here's why:

Server-side rendering (SSR) out of the box — critical for SEO and initial page load
App Router with React Server Components — the future of React, and Next.js is the reference implementation
Built-in optimization — images, fonts, scripts, all automatically optimized
API routes — build your backend in the same codebase, no separate service needed for simple apps
Vercel deployment — push to git, get a global CDN-backed deployment in 60 seconds
Massive ecosystem — every UI library, every analytics tool, every CMS has a Next.js integration

When would we NOT use Next.js? For static marketing sites with no interactivity, Astro is lighter and faster. For internal tools where SEO doesn't matter, plain React (Vite) is simpler. For complex stateful apps that don't need SSR, Remix is a strong alternative. But for 90% of web apps, Next.js is the right call.

The back-end: Node.js, Python, or Go?

For the back-end, we work in three languages, each with its sweet spot:

Node.js (TypeScript)

Best for: API servers, real-time apps (WebSockets), and anything that benefits from sharing code with the front-end. If your front-end is React/Next.js, Node.js lets you share types, validation schemas, and even business logic between client and server. We use Node.js (with Fastify or Express) for most web and mobile back-ends.

Python

Best for: data science, machine learning, AI, and anything involving heavy computation. Python's ecosystem (NumPy, Pandas, scikit-learn, PyTorch, LangChain) is unmatched for data work. We use Python (with FastAPI) for AI/ML back-ends, data pipelines, and any service that wraps a model.

Go

Best for: high-throughput services, CLI tools, and infrastructure. Go's compiled binaries are fast, its concurrency model (goroutines) is excellent for I/O-heavy workloads, and its single-binary deployment is a dream for ops. We use Go for API gateways, webhook processors, and Kubernetes operators.

ℹ️

Rule of thumb: if it's a web API, use Node.js. If it's AI/ML, use Python. If it's infrastructure or needs to be blazing fast, use Go. If you're not sure, start with Node.js — it's the most versatile.

The database: PostgreSQL wins

For 95% of applications, PostgreSQL is the right choice. It's free, it's been battle-tested for 30+ years, and it can do almost everything:

Relational data — obviously, it's a relational database
JSON documents — jsonb columns give you document-database flexibility with ACID guarantees
Full-text search — built-in, with good performance for moderate datasets
Geospatial — PostGIS extension is the industry standard for GIS
Time-series — TimescaleDB extension turns Postgres into a time-series database
Pub/sub — LISTEN/NOTIFY for lightweight event streaming

When would we use something else? Redis for caching and sessions. MongoDB for apps with genuinely schemaless data (rare). InfluxDB for high-write time-series (IoT). Elasticsearch for full-text search at scale. But for most apps, Postgres is all you need — and it's free.

The deployment: Kubernetes vs serverless vs PaaS

Three options, three sweet spots:

PaaS (Vercel, Railway, Render) — best for teams that want to focus on code, not infra. Push to git, get a deployment. Limited control but zero ops. Ideal for startups and small teams.
Serverless (AWS Lambda, Cloudflare Workers) — best for spiky workloads where you pay per request. Cold starts and vendor lock-in are the trade-offs. Good for APIs and background jobs.
Kubernetes — best for teams that need full control and have ops expertise. Overkill for most startups, essential for enterprises running dozens of services. We run K8s for clients who need multi-region, auto-scaling, and zero-downtime deploys.

Our default stack in 2025

For a new web app in 2025, our default stack is:

Front-end:    Next.js 16 (App Router, RSC, TypeScript)
Back-end:     Node.js + Fastify (or Next.js API routes for simple apps)
Database:     PostgreSQL (with Prisma or Drizzle ORM)
Cache:        Redis (Upstash or self-hosted)
Auth:         Keycloak or Clerk (depending on requirements)
Storage:      S3 (AWS) or R2 (Cloudflare)
Email:        Resend or Postmark
Monitoring:   Sentry + Grafana Cloud
Deployment:   Vercel (small) or Kubernetes (enterprise)
CI/CD:        GitHub Actions

This stack is boring, proven, and lets us ship fast. Every piece is well-documented, has a massive community, and will be maintained for the foreseeable future. We don't chase shiny objects — we ship.

What about mobile?

For mobile apps, we use React Native (with Expo) for cross-platform apps and Swift/Kotlin for native apps where performance or platform-specific features matter. React Native has matured enormously — Instagram, Discord and Shopify all use it in production. For most business apps, it's the right choice: one codebase, two platforms, 80% code reuse.

We shipped a complex healthcare platform in 5 months instead of the 12 we'd budgeted. Bivic's mix of strategy and engineering made all the difference.
— Dr. Peter Okello, Founder, Clinic Network

Need help choosing?

Every project is different, and the best stack depends on your team, your requirements, and your constraints. If you're starting a new project and want a second opinion, book a free 30-minute consultation. We'll review your requirements and recommend a stack — with reasoning, not just preferences.

Choosing a Tech Stack in 2025: React, Next.js, Node.js and Beyond