Redis
In-Memory Cache
An open-source, in-memory data structure store, used as a database, cache, and message broker.
✅ Key Advantages
- •Sub-millisecond latency for reads and writes
- •Supports advanced data structures (Lists, Sets, Sorted Sets, Hashes)
- •Built-in pub/sub capabilities
- •Highly versatile (can be used for caching, rate limiting, leaderboards)
⚠️ Trade-offs
- •Dataset size is strictly limited by available RAM
- •Not designed as a primary durable database (though persistence is available)
- •Single-threaded execution model (requires clustering to scale compute)
- •Complex to set up highly available clusters manually
Memcached
In-Memory Cache
A free & open source, high-performance, distributed memory object caching system.
✅ Key Advantages
- •Extremely simple and pure key-value caching model
- •Multi-threaded architecture (scales better vertically than Redis)
- •Minimal configuration required
- •Highly predictable memory management
⚠️ Trade-offs
- •No persistence (data is lost on restart)
- •Supports only basic strings (no complex data structures)
- •No native pub/sub capabilities
- •No replication or true high-availability built-in
System Design Interview Metrics
Expected Latency
Redis<1ms (Sub-millisecond)
Memcached<1ms (Sub-millisecond)
Cost Scaling
Redis
Scales exactly with memory (RAM) usage. Very cost-effective for small cache layers, very expensive for massive datasets.
Memcached
Scales linearly with RAM usage. Generally cheaper operationally than Redis due to simplicity.
Do not guess the costs in a system design interview. Prove it.
Simulate high-traffic workloads against both Redis and Memcached using the MockArch canvas. Let AI analyze your architecture, scalability risks, and cost assumptions.
Start Architecting Now