I recently watched Fireship’s video about how he replaced his entire tech stack with PostgreSQL. The main idea? Most of what we over-engineer into separate services can actually be handled by Postgres plus its extensions.
🎯 The Core Idea
Modern web stacks often look bloated: Redis for caching, Elasticsearch for search, Kafka for streams, Firebase for realtime, separate auth service, separate analytics DB, and more.
But with PostgreSQL, you can cover ~90% of those needs directly. That means:
- Less complexity
- Fewer moving parts
- Lower costs
- Simpler architecture
⚙️ What PostgreSQL Can Do
Here’s how Postgres (plus its ecosystem) replaces common tools:
| Use-Case | Postgres Alternative |
|---|---|
| Unstructured data | JSON/JSONB types for objects & arrays |
| Scheduled jobs | pg_cron for SQL scheduling |
| Caching | Unlogged tables as in-memory cache with TTL logic |
| Vector search / AI | pgvector for embeddings & similarity |
| Full-text search | Built-in tsvector + inverted indexes |
| API layer | GraphQL/REST auto-exposed from Postgres |
| Realtime sync | Notify clients of DB changes without extra infra |
| Authentication | JWT, row-level security, password hashing |
| Analytics | Time-series + column store extensions |
⚠️ When Not To Do It
Just because you can doesn’t always mean you should. Fireship notes some caveats:
- Dedicated tools may scale better for extreme workloads
- Performance trade-offs exist (e.g. Redis is faster for caching)
- Write-heavy systems need careful benchmarking
- Durability vs. speed (unlogged tables skip WAL logging)
✅ Conclusion
You can build an entire full-stack app on PostgreSQL. From structured & unstructured storage to search, caching, jobs, realtime sync, auth, analytics, and even AI workloads — Postgres covers it all.
This approach may not replace every specialized tool in every scenario, but it’s a strong reminder:
Sometimes, fewer moving parts is the smartest architecture.
💡 Have you tried replacing parts of your stack with Postgres extensions?
