Cache ROI Calculator
A cache ROI calculator. Enter your request rate, cache hit rate, and the latency of a hit versus a miss to see your effective average latency, how much faster that is than going to the origin every time, and how much backend load the cache removes. Add a per-origin-request cost to estimate the money saved.
A cache pays off in two currencies — latency and load — and both are easy to quantify. The latency your users feel is just a weighted average of the fast path and the slow path: hit% × hitLatency + (1 − hit%) × missLatency. Serve 90% of traffic from a 2 ms cache and take 80 ms on the 10% that miss, and the average is 9.8 ms — a fraction of the 80 ms every request would otherwise cost. When misses are expensive, even a middling hit rate transforms the average.
The second currency is backend load, and here the number is even cleaner: the fraction of traffic you remove from the origin equals the hit rate. A 90% hit rate means the backend sees one-tenth of the requests — a 10× reduction that often matters more than the latency win, because it is the difference between your current database coping and needing to scale it. This calculator shows origin QPS before and after, plus optional dollars saved when each origin request has a cost.
The trap is treating hit rate as linear. Going 80% → 90% doesn’t shave 10% off your pain — it halves the miss traffic, cutting both slow requests and origin load in two. That non-linearity is why the last few points of hit rate are worth fighting for. Just remember what the model assumes: a healthy, achievable hit rate on reusable data. Caches do nothing for unique requests, and they hand you invalidation and thundering-herd problems in return for the speedup — the ROI is real, but so is the work to earn it.
How it works
- Effective latency = hit% × hit + (1 − hit%) × miss.
- Backend load removed equals the hit rate (e.g. 90% → 10× less).
- Shows origin QPS before vs after the cache.
- Optional per-request cost → estimated money saved.
Frequently asked questions
How is effective latency computed?
It is the weighted average of a hit and a miss: effective latency = hit rate × hit latency + (1 − hit rate) × miss latency. If 90% of requests hit a 2 ms cache and the other 10% take 80 ms from the origin, the average is 0.9 × 2 + 0.1 × 80 = 9.8 ms. That blend is what your users actually experience on average, and it is why even a moderate hit rate cuts latency sharply when misses are expensive.
Why does hit rate matter more than it looks?
Because misses dominate the average. Going from an 80% to a 90% hit rate halves the miss traffic (20% → 10%), which halves both the slow requests and the load on your origin. The relationship is not linear in the way people expect: the last few percent of hit rate remove a disproportionate share of the expensive misses. That is why teams invest heavily in squeezing hit rate up.
How much backend load does a cache remove?
Exactly the hit rate. If 90% of requests are served from cache, only 10% reach the origin, so the backend sees one-tenth of the traffic — a 10× reduction. This tool shows origin QPS before and after so you can see whether the cache lets your existing backend cope, or how much you would need to scale without it. Load removal is often the bigger win than latency.
What does a cache not help with?
Caches help reads of data that is reused and can tolerate slight staleness. They do not help unique, uncacheable requests (each miss still pays full price), and they introduce their own hard problems: invalidation (serving stale data), the thundering herd when a hot key expires and many misses hit the origin at once, and added system complexity. The ROI here assumes a healthy hit rate — achieving and maintaining it is the real work.