Quick Summary
- We optimize for maintainability and decision speed, not hype.
- “Boring” tech usually wins in production.
- Popular does not mean correct.
- The best stack is the one your team can change safely.
- Most tech stack mistakes are expensive because they’re hard to undo.
Why Tech Stack Conversations Go Wrong
Most tech stack discussions start with the wrong question:
“What’s the best stack in 2025?”
There is no best stack.
There is only the least wrong stack for your constraints.
In our experience, teams get into trouble when:
- tools are chosen for popularity
- decisions are delegated too late
- tradeoffs are ignored
By the time problems surface, the stack is already locked in.
Unsure whether your current stack is helping or hurting you?
Schedule a technical review
How We Actually Choose a Tech Stack
At Bear, we don’t start with tools.
We start with constraints.
Every project begins by answering:
- Who will maintain this in 12–24 months?
- How often will this need to change?
- What happens when something breaks at 2 a.m.?
- How expensive is a wrong decision?
Stacks are chosen to reduce future regret, not impress developers.
Our Primary Evaluation Criteria
1. Maintainability Over Cleverness
We avoid stacks that:
- only one developer understands
- require constant babysitting
- break often during upgrades
A slightly slower stack that’s easy to reason about is almost always cheaper long-term.
2. Team Availability
A great stack is useless if:
- hiring is difficult
- onboarding takes months
- tribal knowledge accumulates
We prefer technologies with:
- large talent pools
- clear documentation
- predictable behavior
This matters more than benchmarks.
3. Cost of Change
The most important question:
How expensive is it to change direction later?
We favor:
- modular architectures
- replaceable components
- clear system boundaries
Stacks that lock you in early usually cost the most later.
What We Commonly Use (And Why)
Frontend
We typically choose:
- React-based stacks
- SSR or hybrid where it matters
- simple state management
Why:
- predictable mental model
- strong ecosystem
- easy hiring
We avoid frontend complexity unless it directly supports product needs.
Need help choosing or simplifying your frontend stack?
Book a consultation
Backend
Our preference:
- boring, well-understood frameworks
- clear separation of concerns
- explicit data models
Why:
- easier debugging
- safer refactors
- fewer surprises in production
We avoid experimental frameworks for core systems.
Databases
Defaults matter.
We usually start with:
- relational databases
- clear schemas
- migrations from day one
Why:
- most data is relational
- flexibility comes from design, not tools
- premature complexity hurts learning
Specialized databases come later, when justified.
Infrastructure
We optimize for:
- clarity
- observability
- recoverability
We prefer:
- simple deployment pipelines
- minimal environments
- predictable scaling paths
If infrastructure decisions require a full-time expert too early, that’s a warning sign.
What We Actively Avoid
Trend-Driven Choices
We avoid choosing tech because:
- it’s trending on social media
- it looks impressive in demos
- “everyone is switching to it”
Trends move faster than production realities.
Premature Optimization
We don’t optimize for:
- massive scale before validation
- extreme performance before usage
- edge cases that may never exist
Most startups don’t fail because they were too slow. They fail because they couldn’t adapt.
Common Tech Stack Mistakes We See
- choosing microservices too early
- mixing paradigms without need
- overengineering for hypothetical scale
- ignoring operational complexity
- treating refactors as failure instead of progress
These mistakes compound quietly.
When a Stack Is Already “Wrong”
Signs your stack is hurting you:
- simple changes take weeks
- developers fear touching core code
- hiring is painful
- incidents are hard to diagnose
- roadmap decisions are constrained by tech
At this point, doing nothing is often the most expensive option.
Wondering if it’s time to rethink your stack?
Schedule a strategy call
How Stack Decisions Affect MVP Timelines
Tech stack choices directly influence:
- speed of iteration
- learning cycles
- cost of mistakes
A good MVP stack:
- prioritizes speed of change
- minimizes setup friction
- accepts imperfection
Choosing “enterprise-grade” tools for an MVP often slows learning.
A Practical Stack Decision Checklist
Before locking in a stack, ask:
- Can we explain this choice in one sentence?
- What happens if we need to replace part of it?
- How hard is it to onboard someone new?
- What assumptions are we making about the future?
- What would we choose if this project fails?
If these questions are uncomfortable, that’s the point.
Final Take (Bear Version)
The best tech stack in 2025 is rarely exciting.
It’s usually predictable, boring, and flexible.
Great products are built by teams that:
- make clear decisions
- accept tradeoffs
- leave room to change their minds
Technology should enable that—not fight it.
Want a second opinion on your tech stack before it becomes expensive to change?
Schedule a call with Bear