Cache Size Calculator
Sizing a cache correctly balances memory cost against performance gain. Too small a cache causes frequent evictions and a poor hit rate; too large wastes expensive RAM. This calculator estimates the optimal cache size based on your working set (total unique objects that need to be cached), the average object size in kilobytes, and a buffer to account for metadata and fragmentation overhead. It also converts your current cache size into an estimated hit rate using an approximation of the relationship between cache size, working set size, and Zipf-distributed access patterns. Use these estimates to configure Redis, Memcached, CDN caches, or application-level in-memory caches.
Cache size formula
full_cache_MB = objects * obj_kb / 1024 * (1 + overhead/100)
target_cache_MB = full_cache_MB * (target_hit_rate/100)^(1/0.8)
recommended_GB = target_cache_MB * 1.2 / 1024
The hit-rate-to-cache-size relationship uses a power-law approximation based on Zipf access distributions, which are common in web and database workloads.
Cache sizing guidelines
- Start by identifying your hot working set: the 10-20% of objects that receive 80% of requests.
- For Redis and Memcached, add 20-30% memory overhead for data structures and fragmentation.
- Monitor your actual hit rate after deployment and adjust cache size if the rate is below target.
- Use TTL (time-to-live) policies to prevent stale data from consuming cache space indefinitely.
- Consider a two-tier cache (L1 in-process, L2 Redis) for microsecond vs. millisecond latency trade-offs.
Frequently asked questions
What is a cache hit rate?
Cache hit rate is the percentage of requests served from cache rather than fetching from the origin (database or upstream service). A hit rate of 90% means 9 out of 10 requests are served from cache. Higher hit rates dramatically reduce latency and backend load.
How do I determine the optimal cache size?
The optimal cache size depends on your working set - the set of objects accessed frequently enough to benefit from caching. Start by analyzing access logs to identify the hot 10-20% of objects that receive 80% of traffic (Pareto principle). Size the cache to hold this hot set.
What is the working set in caching?
The working set is the collection of cache objects actively requested within a recent time window. Objects outside the working set are evicted before they can be reused. The working set size determines the minimum cache size needed to maintain your target hit rate.
How does cache eviction policy affect cache size requirements?
LRU (Least Recently Used) eviction works well when access patterns follow temporal locality. LFU (Least Frequently Used) is better for stable popularity distributions. LRU with a small cache can evict hot objects during scans. If you use LRU, add 20-30% extra cache to compensate for scan-contamination.
What is cache thrashing?
Cache thrashing occurs when the working set is larger than the cache, causing objects to be evicted before they can be reused. This results in a very low hit rate and effectively negates the cache's benefit. The cache must be sized to accommodate the entire working set.
Official sources
- NIST: SP 800-145 - The NIST Definition of Cloud Computing.
- IEEE: IEEE ISPASS - Cache Performance Studies.
Reviewed by the CalculatorHub team, edited by James Graham, 14 June 2026. See our methodology.