Information on Tech Stacks and Different Options Available: Pros and Cons

Published

10 Jun 2026

Author

Akash Shakya

Table of Contents

The tech stack debate consumes more founder energy than it should. The right stack is the one your team ships with. Everything else is a conversation that should take 2 hours, not 2 months.

Editorial note: Founder quotes throughout this article are composites drawn from multiple EB Pearls engagements. The numbers and decisions are real. Identifying details have been changed.

Why We Wrote This

Founders spend weeks researching tech stacks when the decision should take a single session in the Discovery Workshop. This article gives you the information you need to have a productive stack conversation: what the options are, what each is good at, and how to choose based on your product, your team, and your scaling needs.

Introduction: Why Stack Choice Matters Less Than You Think (At First)

At the MVP stage, the tech stack is the least important decision you will make. The problem validation, the scope, the team, and the monetisation model all have a larger impact on product success than whether you use React or Vue, Node.js or Python, PostgreSQL or MongoDB.

ThoughtWorks' Technology Radar, which has tracked technology trends since 2010, consistently emphasises that team familiarity with a technology is a stronger predictor of project success than the theoretical advantages of the technology itself. A team that ships confidently in Python will outperform a team learning Go, regardless of Go's concurrency advantages.

Where stack choice matters: scaling beyond 10,000 users, hiring (can you find developers?), and architecture constraints (real-time features, AI integration, compliance requirements). These concerns are Phase 2 and 3 decisions. At Phase 1, ship with what you know. For more on this, see how long does it take to build software.

"We spent 6 weeks evaluating tech stacks for our MVP. We compared React vs. Vue vs. Svelte, Node.js vs. Python vs. Go, PostgreSQL vs. MongoDB vs. DynamoDB. We built proof-of-concept prototypes in three different stacks. At the end of 6 weeks, we chose React, Node.js, and PostgreSQL, the stack our lead developer had 5 years of experience with. The 6 weeks of evaluation produced zero value. We could have shipped week 1 features during that time." Related reading: a complete AI strategy for custom software.

Frontend Options

React / Next.js

Best for: Most web applications. Massive ecosystem, excellent developer availability, strong community. Next.js adds server-side rendering, API routes, and deployment simplification.

Considerations: Can be over-engineered for simple apps. Bundle size requires attention for performance-sensitive applications.

EB Pearls default: Yes, for most web projects. Largest talent pool, most mature ecosystem.

Vue / Nuxt

Best for: Teams that prefer a gentler learning curve. Excellent documentation. Nuxt provides SSR capabilities similar to Next.js.

Considerations: Smaller ecosystem than React. Fewer third-party libraries. Smaller hiring pool.

React Native / Flutter (Mobile)

React Native: Write once, deploy to iOS and Android. JavaScript/TypeScript ecosystem. Best for teams with web React experience. Performance is sufficient for 95% of apps.

Flutter: Google's cross-platform framework. Dart language. Excellent UI consistency across platforms. Better animation performance than React Native. Smaller hiring pool.

EB Pearls default: React Native for most mobile MVPs. Flutter when UI polish is a primary differentiator.

Native (Swift / Kotlin)

Best for: Apps requiring hardware-level performance (AR/VR, real-time audio/video processing, complex animations), platform-specific features, or maximum battery efficiency.

Considerations: 2x development cost (separate iOS and Android codebases). 2x maintenance. Justified only when cross-platform cannot meet performance requirements.

Backend Options

Node.js / Express / NestJS

Best for: Real-time applications (WebSockets), API-heavy products, teams with JavaScript/TypeScript frontend experience (shared language reduces context switching).

Considerations: Single-threaded (use clustering for CPU-intensive tasks). Package ecosystem is massive but quality varies.

Python / Django / FastAPI

Best for: AI/ML-integrated products (Python's AI ecosystem is unmatched), data-heavy applications, rapid prototyping. Django provides batteries-included framework. FastAPI provides modern async API framework.

Considerations: Slower execution than compiled languages (rarely matters at MVP scale). GIL limits true parallelism.

Go

Best for: High-concurrency services, microservices, infrastructure tools. Excellent performance. Simple language with fast compilation.

Considerations: Smaller ecosystem. Less suitable for rapid prototyping. Smaller hiring pool than Node.js or Python.

Ruby on Rails

Best for: Rapid web application development. Convention-over-configuration approach. Excellent for CRUD-heavy applications.

Considerations: Performance ceiling for high-traffic applications. Hiring pool has decreased as Node.js and Python gained market share.

Database Options

Database	Best For / Considerations
PostgreSQL	Most applications. ACID compliance, JSON support, full-text search. Default choice.
MongoDB	Document-heavy apps, flexible schemas, rapid prototyping. Avoid for relational data.
MySQL	Legacy compatibility, WordPress integrations. PostgreSQL is generally preferred for new projects.
Redis	Caching, session storage, real-time leaderboards. In-memory, not a primary database.
DynamoDB	Serverless, massive scale, AWS-native. Vendor lock-in. Complex query patterns are expensive.

Infrastructure Options

AWS (Amazon Web Services)

The default for most products. Broadest service portfolio. Best for complex, multi-service architectures. Steeper learning curve. EB Pearls primary platform.

Google Cloud Platform (GCP)

Excellent for AI/ML workloads (Vertex AI, BigQuery). Strong Kubernetes support. Simpler pricing model than AWS.

Azure

Best for Microsoft ecosystem integration. Enterprise-focused. Strong hybrid cloud capabilities.

Vercel / Railway / Render

Platform-as-a-Service options. Fastest deployment for Next.js (Vercel) or containerised apps (Railway, Render). Less configuration overhead. Higher per-unit cost at scale.

Non-Obvious Truth: The Best Stack Decision Is the Fastest Stack Decision

Every week spent evaluating tech stacks is a week not spent validating the product. The stack decision should happen in the Discovery Workshop architecture review (Pillar P03 of Built to Last). Input: product requirements, team capability, scaling path. Output: stack recommendation with rationale. Time: 60 to 90 minutes. Move on.

"A founder came to us wanting to use Elixir and Phoenix because they read that WhatsApp used Erlang (the language Elixir is built on) to handle millions of concurrent connections. Their product was a project management tool for teams of 5 to 20. Maximum concurrent users at their 12-month target: 500. We built it in Node.js and PostgreSQL. The same two developers who would have spent 4 weeks learning Elixir shipped the MVP in 6 weeks using tools they already knew. WhatsApp's concurrency needs were not this founder's concurrency needs."

Common Mistake: Choosing Technology Based on Blog Posts Instead of Requirements

Tech blogs promote the newest, most interesting technologies. Those technologies are not always the right choice for your product. Evaluate based on your specific requirements, your team's proficiency, and your hiring market. Boring, proven technologies ship products. Exciting, unproven technologies ship blog posts.

Product requirements documented: platforms, real-time needs, data volume, integrations
Team proficiency assessed: which stacks does the team ship confidently in?
Hiring market evaluated: can you find developers for this stack in your market?
12-month scaling path assessed: can this stack handle your growth target?
Architecture review completed in Discovery Workshop (P03 of Built to Last)
Stack decision made in under 2 hours, not 2 months
Decision documented with rationale (Architecture Decision Record)

Frequently Asked Questions

What tech stack should I use for my MVP?

The one your team knows best. For most MVPs: React or Next.js frontend, Node.js or Python backend, PostgreSQL database, AWS or GCP hosting. Stack choice matters less than team proficiency at the MVP stage.

Should I go native or cross-platform for mobile?

Cross-platform (React Native or Flutter) for 95% of MVPs. Native (Swift/Kotlin) only if you need hardware-level performance (AR, real-time audio/video processing, complex animations) that cross-platform cannot deliver.

When should I worry about scalability?

Design for it in Phase 1 (architecture decisions). Build for it in Phase 3 (when data justifies the investment). Do not optimise for 100,000 users when you have 100. But do choose an architecture that can scale without a rewrite.

What about serverless?

Excellent for event-driven workloads, APIs with variable traffic, and products with unpredictable usage patterns. Less suitable for long-running processes, websocket-heavy applications, or when cold start latency matters.

How do I evaluate whether my team picked the right stack?

Three tests: Can you hire for it in your market? Can it scale to your 12-month user target without a rewrite? Does the team have production experience with it? If yes to all three, the stack is right. For more detail, see from prototype to product.

Free Founder Resources

Tech Stack Decision Matrix (PDF)

A one-page framework for matching product requirements to technology options across frontend, backend, database, and infrastructure.
Architecture Decision Record Template (Google Docs)
Document the stack decision with context, alternatives considered, and rationale. Prevents revisiting the decision later.
Stack Cost Calculator (Google Sheets)
Estimate infrastructure costs for different stack combinations at MVP, Scale, and Enterprise stages.

Final Thought

The tech stack is a tool, not a strategy. The best stack is the one that lets your team ship a validated product as fast as possible. Choose based on your requirements and your team, not on what is trending on Hacker News. And make the decision in hours, not months. For more on this, see what's included in development costs.

Nobody ever said: 'The product failed because we used PostgreSQL instead of MongoDB.' Products fail because of wrong problems, wrong scope, and wrong timing. Not wrong databases.

Akash Shakya Chief Operating Officer and Co-Founder

Discover app development insights and AI trends with Akash Shakya, COO of EB Pearls. Learn how we build successful digital products.

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