Calculator Inputs
The page remains single-column overall. Only the calculator fields use a responsive 3-column, 2-column, and 1-column layout.
Example Data Table
| Scenario | Total Requests | Hit Ratio | Hit Path | Miss Path | Average Latency | Efficiency Gain |
|---|---|---|---|---|---|---|
| Example Network Cache | 120,000 | 82% | 17.50 ms | 109.50 ms | 34.06 ms | 68.89% |
| Higher Hit Ratio | 120,000 | 90% | 17.50 ms | 109.50 ms | 26.70 ms | 75.62% |
| Slower Origin | 120,000 | 82% | 17.50 ms | 134.50 ms | 38.56 ms | 71.33% |
Use the example button to prefill the first scenario directly into the calculator.
Formula Used
The approximate P95 estimate uses a simple threshold rule: if the hit ratio is at least 95%, it assumes the tail is dominated by hits; otherwise, it assumes the miss path dominates the tail.
How to Use This Calculator
Enter the total number of requests for the analysis window. This can represent a minute, hour, day, or traffic test batch.
Provide the cache hit ratio as a percentage. A higher number means more requests are served by the cache instead of the origin.
Enter the cache lookup overhead and hit service latency. These values represent the fixed lookup cost and response time when content is already cached.
Enter the origin fetch latency and miss transfer latency. These values represent the slower path when the cache cannot serve the object directly.
Enter response processing overhead and the SLA target latency. The calculator then estimates weighted latency, saved time, capacity gain, offload, and SLA compliance.
Press Calculate to show the result above the form. Use Download CSV for spreadsheet export and Download PDF for a printable performance summary.
Frequently Asked Questions
1) What does cache hit latency mean?
It is the total response delay when requested content is already available in the cache. It usually includes lookup time, cache processing time, and transfer time to the client.
2) Why is weighted average latency important?
Users experience a mixture of hits and misses. Weighted average latency combines both paths into one realistic number, making it easier to compare performance across configurations and traffic patterns.
3) What is origin offload?
Origin offload is the number of requests the cache prevents from reaching the backend or origin server. Higher offload reduces backend load, bandwidth use, and infrastructure stress.
4) How should I choose the SLA target?
Use the maximum response time your service promises internally or externally. Common choices come from dashboard objectives, API contracts, user experience standards, or CDN performance budgets.
5) Can this calculator help compare two cache strategies?
Yes. Run one scenario with current values, then adjust hit ratio, lookup cost, or origin latency for another design. Compare average latency, capacity multiplier, and compliance rate.
6) What if my hit ratio changes during the day?
Run separate calculations for peak and off-peak periods. This reveals how traffic variation changes latency savings, origin load, and SLA risk across different operating windows.
7) Why does the P95 figure look simplified?
A true percentile requires request distribution data. This tool provides a quick approximation to support planning, but detailed observability data is better for production-grade percentile analysis.
8) When is this calculator most useful?
It is especially useful for CDN tuning, reverse proxy design, API gateway optimization, backend offload studies, edge caching reviews, and performance budgeting before infrastructure changes.