What's the most cost-effective way to support read-heavy workloads using an in-memory cache service?

For read-heavy workloads, the most cost-effective pattern is usually:

Use a small, shared in-memory cache in front of a cheaper persistent store

Why this is cost-effective

• Most reads hit cache, reducing expensive database or API calls
• In-memory lookups are very fast, so you can often get away with fewer backend resources
• A shared cache avoids duplicating data across many app instances
• A read-through / cache-aside pattern lets you cache only what’s actually requested, so you don’t pay to store everything

Best-practice approach

1. Start with cache-aside
   • App checks cache first
   • On miss, fetch from database
   • Store result in cache with a TTL
2. Use short, sensible TTLs
   • Prevent stale data from lingering
   • Keep memory usage under control
3. Cache only hot data
   • Frequently accessed, expensive-to-fetch, or slow-changing objects
4. Compress or reduce payload size
   • Smaller cached values = lower memory cost
5. Set eviction policy carefully
   • LRU/LFU often works well for read-heavy patterns
6. Monitor hit rate
   • If hit rate is low, the cache may not be worth its cost

What usually gives the best price/performance

• If you’re using a cloud cache service, the most cost-effective choice is often:
  • one modest-sized node/cluster
  • replication only if needed for availability
  • right-sized memory tier
  • no overprovisioning “just in case”

Rule of thumb

A cache is most cost-effective when:

• data is read many times
• data changes relatively infrequently
• your cache hit rate is high enough to offset cache cost

If you want, I can also recommend the best caching pattern for Redis/Memcached or help you estimate cache size and ROI for your workload.