In today’s fast-moving startup world, choosing the right tech stack can make or break your product’s journey. During our recent webinar, Emma Delescolle—a seasoned software architect who shipped her first software over 30 years ago—broke down the how, why, and what of tech stack planning for startups and new projects.

Here’s a distilled summary of her invaluable insights.

🚀 Why Tech Stack Decisions Matter Early On

Emma started by debunking a common myth: startups don’t fail because of the wrong tech stack—but because of what surrounds that decision—developer availability, hiring costs, and scalability challenges.

Choosing your stack isn't just a technical decision—it's a strategic business move.

The Framework: Rational, Not Emotional Decisions

Emma introduced a clear takeaway: use a structured framework to evaluate technology decisions. Instead of being swayed by hype (hello, microservices and GraphQL), she urged founders to weigh:

• Talent availability

• Cost of ownership (including licenses and infrastructure)

• Maintenance over the long term

• Productivity vs performance trade-offs

Key Factors to Consider

1. Technical Requirements

   • Real-time collaboration ≠ high-tech complexity (libraries already exist!)

   • Sub-millisecond latency or IoT use cases do demand specific stacks.

2. Team Capability

   • A proficient team using familiar tools is 10x more effective.

   • Want to try a new stack? Make sure the productivity gain justifies the learning curve.

3. Recruitment Realities

   • Compare JavaScript (millions of developers) vs Rust (far fewer).

   • Cost of hiring is deeply impacted by developer availability and language verbosity.

4. External Factors

   • Licensing, vendor lock-in, documentation, and community health must be factored in.

   • Even geopolitical concerns, like data sovereignty, influence your hosting and provider choices.

Stack Selection Tips

Emma outlined common pitfalls startups fall into:

• Overengineering for scale you'll likely never reach.

• Trend-chasing without regard to support or maintainability.

• Ignoring perceived performance (UX) in favor of raw speed metrics.

Instead, she suggests startups should:

• Optimize for developer speed, not theoretical scalability.

• Think modular from day one—so you can swap out parts later.

• Create a decision matrix to score stack options based on weighted business and technical criteria.

What About AI in Your Stack?

Emma highlighted the strengths—and severe limitations—of AI-generated code:

• ✅ Great for boilerplate, tests, and prototypes.

• ❌ Poor for strategic planning, architecture, and long-term maintenance.

AI tools are like expensive screwdrivers—effective only when used by skilled engineers. They amplify existing knowledge but don’t replace thoughtful design.

What to Expect Over Time

Emma provided a realistic timeline of challenges startups face:

• 0–6 months: Focus on fixing bugs and launching your MVP.

• 6–18 months: Handle library and framework upgrades.

• 18–36 months: Plan for modularization and potential rearchitecture.

• 3+ years: Gradually replace components—avoid full stack rewrites.

Example: Instagram started with Django + PostgreSQL and scaled to millions—gradually replacing components, never rewriting everything.

4 Guiding Principles to Remember

1. Hardware is cheap; people are expensive.

2. AI accelerates—but doesn’t strategize.

3. Tech stack should suit your team, not just your dream.

4. Build changeable systems—don’t aim for perfect, aim for flexible.

👋 Final Words

The session ended with great audience engagement, including thoughtful questions on political instability and data hosting. Emma's message was clear: tech decisions are long-term bets. They must be made with a balanced view of today’s reality and tomorrow’s flexibility.

🎥 Watch the full webinar