Estimate context usage for prompt caching. Enter workload and measured system values to see a practical main result plus supporting capacity indicators. The calculator keeps assumptions visible so you can compare baseline, peak, and optimized scenarios without treating a rough estimate as a benchmark guarantee.
Values are combined without early rounding; displayed results are rounded for readability.
The result converts the entered prompt caching workload into an operational estimate. It is most useful for scenario comparison and identifying which assumption has the largest effect.
Planning estimate only. Validate the result with load tests, provider limits, production telemetry, and a separate reliability reserve.
Load the default values shown above and select Calculate. The displayed result is generated by the same formula used for every custom scenario, so the default case is a reproducible worked example.
Use production observations from the same model, index, hardware, batch size, and traffic pattern you plan to operate.
Only when the entered request rate or workload represents your peak scenario. Run separate normal and peak cases.
Queueing, tail latency, network overhead, data distribution, model settings, and provider limits can change real performance.
Averages are useful for a baseline, but p95 or p99 scenarios are safer for user-facing service objectives.
Yes. This calculator shows the modeled estimate; maintenance, failures, bursts, and measurement error require a separate reserve.
| Queries | operations/task |
|---|---|
| Items | chunks or segments/query |
| Item size | tokens/item |
| Overhead | tokens/task |
| Context limit | tokens |