7 Tech Stack Mistakes That Kill Startups
The graveyard of startups is full of great ideas killed by bad technical decisions. Here are the patterns we see repeatedly — and how to avoid them.
00 Quick Navigation
01 Building for Scale You Don't Have
The Problem
You architect for millions of users when you have 100. Kubernetes clusters, microservices, event sourcing - all before product-market fit.
A fintech startup spent 8 months building a "Netflix-scale" infrastructure. They ran out of runway before launching. Their eventual traffic? 50 requests per day.
The Fix
Start with a boring monolith. PostgreSQL handles more than you think. Move to microservices when you have 50+ engineers, not 5.
Before You Decide, Ask:
- Can a single PostgreSQL instance handle your load? (Hint: yes, up to millions of rows)
- Do you have separate teams that need to deploy independently?
- Are you solving a scaling problem or an imagined one?
02-03 Technology Selection Traps
Choosing Tech for Your Resume
The newest framework won't save your startup - shipping will.
Engineers pick technologies to learn on the job, not to ship fast. Rust for a CRUD app. Kubernetes for 3 containers. GraphQL when you have one client.
A SaaS startup chose a cutting-edge serverless stack. Six months later, they were still debugging cold starts and regional issues instead of shipping features.
Use boring technology. The companies that win use proven stacks, not bleeding-edge experiments.
Checklist (3 items)
- Would a 5-year-old technology solve this problem?
- Can you hire for this stack in your market?
- Have you shipped production code with this before?
DIY-ing Everything
Building in-house what you could buy is the slowest path to failure.
Custom auth systems. Homegrown payment processing. Self-built analytics. Every reinvention costs months and introduces security risks.
A healthcare startup built custom auth to "control security." Their implementation had OWASP Top 10 vulnerabilities. A $15/month service would have been compliant out of the box.
Buy > Build for anything that's not your core product. Auth0, Stripe, Segment exist for a reason.
Checklist (3 items)
- Is this our core competency?
- What's the opportunity cost of building this?
- Does a battle-tested solution exist?
04 Ignoring Technical Debt
The Problem
Every shortcut accumulates. Copy-pasted code. No tests. Hardcoded values. Eventually, every change breaks something else.
The Fix
Allocate 20% of each sprint to debt reduction. Write tests for critical paths. Refactor before it's painful.
An e-commerce startup grew to $5M ARR. Then feature velocity dropped to near-zero. A 2-hour feature took 2 weeks because of spaghetti code. They lost market share to faster competitors.
05-07 Operational Blind Spots
Single Points of Failure
One person, one server, one vendor - all ways to lose everything.
The CTO is the only one who understands the infra. The database has no backups. The entire business runs on one API with no fallback.
A logistics startup ran on a single developer's machine for "cost savings." Laptop stolen. No backups. Company folded.
Document everything. Automate deployments. Have fallbacks. No single person should be irreplaceable.
- Can someone else deploy if the CTO is sick?
- When was the last backup tested?
- What happens if your main vendor goes down?
Choosing Vendors Without Exit Plans
Lock-in feels fine until it doesn't.
All-in on a platform that changes pricing. Proprietary formats with no export. APIs with no alternatives.
A startup built entirely on Parse (Facebook's BaaS). Parse shut down with 1 year notice. The migration took 8 months and $200K.
Use open standards where possible. Keep data portable. Have migration plans documented before you need them.
- Can you export your data in a standard format?
- What would switching vendors cost (time + money)?
- Are there at least 2 alternatives if needed?
Not Measuring What Matters
If you're not measuring it, you're not improving it.
No performance monitoring. No error tracking. No user analytics. Flying blind until customers complain.
An API startup had a memory leak for 3 months. They only found out when a customer's bill tripled from retry storms. Proper monitoring would have caught it day one.
Set up monitoring before launch. Track errors, latency, and user journeys. Alert on anomalies.
- Do you know your p99 response time?
- Are you alerted when errors spike?
- Can you trace a user request through your system?
08 Quick Reference Table
| # | Mistake | Severity | Key Fix |
|---|---|---|---|
| 1 | Building for Scale You Don't Have | CRITICAL | Start with a boring monolith. |
| 2 | Choosing Tech for Your Resume | HIGH RISK | Use boring technology. |
| 3 | DIY-ing Everything | CRITICAL | Buy > Build for anything that's not your core product. |
| 4 | Ignoring Technical Debt | HIGH RISK | Allocate 20% of each sprint to debt reduction. |
| 5 | Single Points of Failure | CRITICAL | Document everything. |
| 6 | Choosing Vendors Without Exit Plans | MODERATE | Use open standards where possible. |
| 7 | Not Measuring What Matters | MODERATE | Set up monitoring before launch. |
09 Frequently Asked Questions
Avoid These Mistakes With Data
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