Latency numbers are the foundation of performance optimization. Every operation has a cost, and understanding these costs helps you optimize at the right level. Memory is 200x faster than disk, but network can be 1000x slower than memory. These aren't just numbers—they're the physics constraints that shape system architecture.
The golden rule: Optimize where the biggest gaps are. Going from HDD to SSD saves 20ms. Adding a cache layer saves 100ms. But moving compute closer to users can save 150ms—often the biggest win.
⚡ Quick Decision
Optimize CPU/Memory When:
- • Latency under 1ms required
- • High-frequency operations
- • Cache hit optimization needed
Optimize Storage When:
- • Database queries slow (>10ms)
- • Need SSD over HDD (20x faster)
- • I/O bound workloads
Optimize Network When:
- • Cross-region latency >50ms
- • Global user distribution
- • Large payload transfers
💡 For implementation guides and code examples: See our technology deep dives: Observability, Redis, PostgreSQL
Latency Numbers Every Programmer Should Know
Key performance numbers across CPU, storage, and network operations.
CPU / Memory
Storage
Network
Network Round-Trip Time Comparison
Understanding the massive scale differences in network latency.
🧠 Latency Memory Palace
Visual mnemonics to remember the most critical latency numbers. These stick better than raw microseconds.
🎯 Optimization Priority Matrix
Where to focus your optimization efforts based on current performance and potential gains.
🔥 High Impact Optimizations
⚠️ Micro-Optimizations (Later)
📏 Scale Perspective
Understanding the massive scale differences helps prioritize optimization efforts.