Choosing a Database Isn’t About Hype - It’s About the Stage of Your Product
Source: Dev.to
Overview
Selecting a database is not about following hype or choosing what’s trending on tech Twitter.
It’s about understanding what stage your product is in, the shape of your data, and the load patterns your system actually handles.
MVP / Early Startup Stage
At this stage, your priorities are clear:
- Launch fast
- Avoid DevOps complexity
- Keep costs low
- Iterate on product, not infrastructure
Recommended options
- Supabase
- Firebase
- MongoDB Atlas (Free Tier)
Why these?
You don’t need multi‑region replicas or an enterprise‑grade SQL setup before you even have users. You need a database that helps you build features quickly with minimal overhead.
Production Stage
Once real users appear, the requirements change significantly:
- Predictability
- Transactional integrity
- Schema stability
- Query planning and indexing
- Long‑term data reliability
Best fits
- PostgreSQL
- MySQL (managed)
Relational databases shine here because your data model stabilizes and you need strong guarantees on consistency, migrations, and analytics. Supabase/Firebase can still work in production, but relational databases remain the safer long‑term foundation.
AI‑Driven Applications
AI workloads behave differently from traditional CRUD systems:
- High RPS
- Expensive operations
- Short‑lived intermediate data
- Heavy caching needs
- Semantic search requirements
Typical hybrid architecture
- Redis + PostgreSQL
- Redis + ClickHouse
- Vector DB (Pinecone, Weaviate) + SQL
Roles
Redis
- Caching model responses
- Rate limiting
- Job processing queues
- Fast temporary storage
SQL
- User history
- Generated content
- Logs
- Analytics
Vector DB
- Embeddings
- Semantic search
- Retrieval pipelines
Pushing AI workloads into a single SQL database usually leads to bottlenecks.
Data‑Centric Applications
Applies to CRMs, dashboards, admin panels, SaaS tools with heavy reporting, and anything where data relationships are the core of the product.
You need
- Consistency
- Clear relational structure
- Predictable JOIN performance
- Strict schema control
Use
- PostgreSQL
- MySQL
If most of your product revolves around data tables, relational databases are the strongest choice.
Summary
- MVP: Supabase / Firebase / MongoDB → move fast.
- Production: PostgreSQL / MySQL → stay stable.
- AI workloads: Redis + SQL + Vector DB → maximize speed and flexibility.
- Data‑heavy applications: Pure SQL → structure is your advantage.
Conclusion
Choosing a database is not about picking the flashiest tool or copying another team’s stack.
- Early products don’t need enterprise‑level infrastructure.
- Mature systems can’t rely on flexible‑but‑loose NoSQL setups.
- AI systems require a hybrid approach.
- Data‑focused apps thrive on relational clarity.
Choose a database for your product stage—not for the hype.