By Arjun Mehta
Every engineering team debates their tech stack. Should we use Node.js or Python? PostgreSQL or MongoDB? React or Vue? Kafka or RabbitMQ? The debates are passionate, detailed, and often endless.
But here's what most teams get wrong: the choice matters less than most people think. What matters far more is that your tech stack is intentional, documented, and communicated clearly so that everyone on your team—and crucially, your new hires—understands why things are the way they are.
A mediocre stack that everyone understands is better than an optimal stack that only three people fully comprehend. And a stack that was chosen deliberately with documented reasoning is better than one that just "happened" through years of accumulated decisions.
This guide will walk you through choosing a tech stack, documenting why you chose it, and communicating it in ways that help engineers understand the tradeoffs—not just the choices.
What Is a Tech Stack (And Why It Matters)
Your tech stack is the collection of programming languages, frameworks, databases, infrastructure, and tools you use to build your product. It's also the accumulated decisions about how you build software.
But tech stack is more than just what you use. It's why you use it. It's the architectural decisions that led to those choices. It's the constraints and assumptions that made sense when you chose them, and the constraints that have emerged since.
A tech stack matters because it affects everything:
Hiring: If your tech stack is uncommon or niche, you'll have a smaller pool of candidates. If it's boring but solid (Python, PostgreSQL, React), you can hire many people.
Speed: Some technologies let you ship features faster. Some are slower but more reliable. Your choices create tradeoffs.
Scaling: Technologies that work fine for 100k daily active users might create problems at 10M. Your stack needs to match your scale, both now and in the future you're planning for.
Maintenance: Code written in well-understood technologies with good libraries and community support is easier to maintain. Niche choices can leave you isolated.
Flexibility: Some choices lock you in. Some keep options open. Understanding the difference helps you make better decisions.
Onboarding: A new engineer joining your team needs to understand your stack. If documentation exists and the stack is conventional, they'll be productive quickly. If the stack is unusual or undocumented, onboarding takes longer.
How to Choose Your Tech Stack
Most tech stack discussions become religious wars because people are making different decisions about what matters.
One engineer values "it has the best performance." Another values "it's easy to hire for." A third values "I've used it before." Without a framework for making the decision, arguments are endless.
Here's a better approach:
1. Define Your Constraints and Priorities
Before choosing anything, write down what actually matters to your business:
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Scale: How many users will you have in year one? Year three? Does your stack need to handle global scale or regional scale?
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Performance requirements: Do you need response times under 100ms or is 500ms acceptable? Do you process millions of transactions per day or thousands?
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Team expertise: What do your current engineers know? Can you hire people in your market who know the technologies you're considering?
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Time to market: Do you need to ship in weeks or months? Some technologies are slower to develop in, but produce more reliable code. Others are faster but require more maintenance.
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Long-term maintainability: Will you maintain this code for 5 years? 10 years? Or is it an experiment that might be replaced?
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Operational complexity: Can you run this in your current infrastructure? Do you have people who can debug it when it breaks?
Write these down. Be specific. "We need to scale to 100M requests per day" is better than "it needs to scale."
2. Evaluate Options Against Your Priorities
Now look at technologies that could work:
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Python (Django, FastAPI, Flask) + PostgreSQL: Easy to hire for, great ecosystem, good performance for most use cases, readable code.
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JavaScript/TypeScript (Node.js, Next.js): Can write frontend and backend in same language, good for web apps, large ecosystem, sometimes performance issues at scale.
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Go: Fast, good for microservices, compiled language, smaller community than Python/JS, steep learning curve.
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Java: Mature, runs on JVM, great for large-scale systems, verbose, slower to develop in.
Each has tradeoffs. Map them against your priorities. If hiring speed matters most and you're in a market with many Node.js developers, JavaScript might win. If performance at massive scale is critical, Go or Java might be better.
3. Make a Decision, Not a Perfect Choice
There is no perfect tech stack. There are only decisions that make sense for your constraints. Spend a few weeks evaluating, but not months. The cost of deciding is lower than the cost of never deciding and drifting.
Decode bias towards choices that are:
- Boring: Well-established, lots of resources, many people know them.
- Hireable: You can find people who know them.
- Supported: Active communities, regular updates, good libraries.
Avoid choices that are:
- Cutting-edge: You're using something new because it's new, not because it solves a real problem.
- Niche: You're using something because one engineer is passionate about it, without considering hiring or support.
- Inconsistent: Your backend is JavaScript, your frontend is Rust, your data pipeline is Erlang, and no one knows why.
Documenting Your Tech Stack
Once you've chosen, document it. Not in a wiki that no one reads. Document it in places engineers actually look:
Architecture Decision Records (ADRs)
For each major technology choice, write a short document explaining:
- What choice you made
- Why you made it (constraints it satisfied)
- What alternatives you considered
- What tradeoffs you accepted
- When you'll revisit the decision
Example ADR:
"Decision: Use PostgreSQL for our primary database
Context: We need a database for a SaaS application handling financial transactions. We considered PostgreSQL, MySQL, and MongoDB.
Decision: PostgreSQL
Rationale:
- ACID compliance is critical for financial data. PostgreSQL has strong ACID guarantees.
- We have relational data with clear schemas. PostgreSQL handles this well.
- Our team has PostgreSQL experience.
- We need complex queries with joins. PostgreSQL is excellent here.
Consequences:
- We can't do horizontal scaling as easily as with NoSQL databases.
- PostgreSQL requires more thought around query optimization for large datasets.
- Our hiring pool is smaller than MySQL but larger than specialized databases.
Alternatives considered:
- MySQL: Also good, slightly easier to hire for, but slightly less powerful.
- MongoDB: More scalable horizontally, but weaker consistency guarantees and harder to query.
Review date: End of year one. If we're handling 10x more data and PostgreSQL can't keep up, we'll reconsider."
These aren't fancy. They're documentation for your future self and your new hires. They explain the thinking, not just the choice.
Architecture Documentation
Create diagrams showing:
- How different parts of your system connect
- What languages/databases/services are used
- Data flow between systems
- External dependencies
Tools like Glue can help here. By analyzing your actual codebase, you can generate accurate architecture documentation showing the real dependencies and relationships, not the imagined ones.
Deployment and Operational Guides
For each major technology:
- How to set it up locally
- How to deploy it to production
- How to debug it when it breaks
- What to monitor
- Runbooks for common issues
This is crucial. When something breaks at 2am and your on-call engineer is trying to debug, they need guides that work.
Communicating Your Tech Stack
Documentation only matters if it reaches people. Here's how to communicate your stack effectively:
1. To New Engineers
When someone joins, don't just say "we use Python and React." Explain the why:
"We use Python because it's readable and productive, which matters when we're rapidly shipping features and iterating on product. We use PostgreSQL because we have relational data and need strong consistency. We use React because it's the most-hired-for frontend framework and we need to ship UI fast."
Context makes choices stick. It helps new people understand how to make decisions when they face ambiguity.
2. To Product and Leadership
Explain how tech stack affects product velocity:
"Our choice of boring, well-supported technologies means we can hire fast and maintain code easily. This lets us ship features 20% faster than we would with a cutting-edge stack where we'd spend more time on infrastructure."
This connects tech decisions to business outcomes. Leaders understand speed and hiring. Frame tech stack decisions in those terms.
3. When Evaluating New Technologies
When your team suggests a new tool or language, evaluate it against your existing architecture:
"We're considering Go for our new data pipeline. How does it fit with our existing Python stack? Can we hire for it? What's the operational cost of maintaining another language?"
Unless there's a specific reason (performance, ecosystem, skillset), consistency usually wins.
Maintaining Your Tech Stack Over Time
Your tech stack isn't a one-time choice. It needs maintenance:
Monitor Technology Health
Regularly ask:
- Are the technologies we chose still good choices? Have constraints changed?
- Are we hiring people who know our stack?
- Is the community around our tools still active?
- Are we having performance or maintenance problems?
Update Dependencies and Versions
Stay current on major versions of your frameworks and libraries. Falling too far behind creates debt. Upgrading constantly creates chaos. A reasonable rhythm is upgrading major versions once or twice per year.
Evolve Deliberately
If you need a new tool (new language, new database, new framework), add it deliberately. Document why. Plan how it integrates with existing stack. Don't just accumulate technologies.
Consider Team Growth
As your team grows, your tech stack needs to scale. A stack that works for 10 engineers might have problems with 100. Plan for this. Don't wait until you have 50 engineers and realize your architecture doesn't scale.
Common Tech Stack Mistakes
Building a Resume Stack Instead of a Business Stack
Choosing technologies because engineers want to learn them ("let's use Rust for this service") rather than because they solve your problem is expensive. Reserve learning to small experiments, not core systems.
Overestimating Your Scaling Problem
Most scaling decisions should wait until you have a real scaling problem, not an imagined one. Don't use cutting-edge distributed databases for what should be PostgreSQL.
Underestimating Operational Complexity
The trendiest tool in the industry might require expertise you don't have. Kubernetes is powerful but complex. A boring VPS might be what you actually need.
Losing Institutional Knowledge
When people who understand your tech stack leave, knowledge walks out the door. Document it. Actively onboard replacements.
Frequently Asked Questions
Q: What's the best tech stack?
A: There is no best. There are choices that fit your constraints. For most SaaS companies, Python/Node + PostgreSQL + React is a boring, smart choice. Fast to develop, easy to hire for, scales well, great ecosystem.
Q: Should we use microservices?
A: Not unless you have a reason. Start with a monolith. Monoliths are easier to develop, deploy, and maintain. Split into microservices when you have specific scaling or team scaling reasons. Most companies split too early.
Q: How long do we have before we need to rewrite our stack?
A: If you chose well initially and maintain it actively, a well-designed system can last 10+ years. If you ignore it and let it rot, even good choices become problematic in 5 years. Maintenance matters more than the initial choice.
Q: Can we change our tech stack later?
A: Yes, but it's expensive. You can migrate to new technologies if needed, but assume it'll take months or years. It's better to choose something you can live with for years than to chase trends.