Why RAM Matters More Than CPU
For most web applications, RAM is the bottleneck—not CPU. When RAM runs out, your system starts swapping to disk, which can make response times 100x slower.
Baseline RAM by Runtime
Different languages and frameworks have different memory footprints. These are typical ranges based on common configurations:
| Runtime | Idle Memory | Under Load | Notes |
|---|---|---|---|
| Go | 10-50 MB | 50-300 MB | Very efficient |
| Node.js | 30-150 MB | 150-500 MB | V8 heap dependent |
| Python (Flask) | 30-100 MB | 100-400 MB | Per worker |
| Python (Django) | 80-200 MB | 200-600 MB | Per worker |
| Ruby on Rails | 100-300 MB | 300-800 MB | Per worker |
| Java (Spring) | 150-400 MB | 400 MB-1.5 GB | JVM heap dependent |
| PHP (Laravel) | 30-100 MB | 100-400 MB | Per request |
Note: Actual memory usage depends heavily on your application's specific code, dependencies, and configuration.
RAM for Databases
Databases are memory-hungry. Here are practical minimums for development and production:
SQLite: Minimal (embedded, shared with app memory)
PostgreSQL: 256 MB minimum, 1-4 GB recommended for production
MySQL: 512 MB minimum, 2-4 GB recommended for production
Redis: Depends on data size (data stored in RAM)
MongoDB: 1 GB minimum, 4+ GB recommended for production
See official documentation: PostgreSQL ↗ | MySQL ↗ | Redis ↗
Total RAM by Workload
Add up your components to estimate total requirements:
Simple API
App: ~200 MB
OS overhead: ~200 MB
Buffer: ~100 MB
Recommended: 1 GB
Web App + DB
App: ~400 MB
PostgreSQL: ~1 GB
OS: ~300 MB
Recommended: 2-4 GB
Full Stack + Cache
App: ~600 MB
PostgreSQL: ~2 GB
Redis: ~500 MB
Recommended: 4-8 GB
Signs You Need More RAM
- High swap usage: Check with
free -h - OOM kills: Check
dmesg | grep -i kill - Slow response times under load
- Database "out of memory" errors
RAM Optimization Tips
- Use connection pooling (reduces per-connection memory)
- Set appropriate heap limits (Node:
--max-old-space-size) - Use Redis for session storage instead of in-memory
- Enable gzip compression to reduce memory for responses
Methodology
Memory estimates are based on typical configurations and community benchmarks. Your actual usage will vary based on code, dependencies, traffic patterns, and optimization. Always monitor real usage in your environment.