This estimator calculates how many concurrent AI fraud-detection requests are needed to sustain a target transaction rate at a given end-to-end latency. It also shows theoretical capacity at the chosen concurrency level and the latency headroom against a service target.
Use it when sizing application workers, model-serving slots, or vendor concurrency quotas. Because throughput and latency interact, the result helps reveal when a low-latency target still requires substantial parallelism during peak traffic.
1. Enter peak traffic
Use the highest sustained transaction rate the service must handle, not the monthly average.
2. Measure end-to-end latency
Include queueing, feature retrieval, inference, and response handling in the average latency.
3. Set the service target
Enter the latency objective used to interpret headroom.
4. Add resilience capacity
Apply a concurrency buffer for variance, then review the rounded recommended request slots.
Raw concurrency = Peak transactions per second × Average latency in seconds
Recommended concurrency = ceil(Raw concurrency × (1 + Buffer %))
This is based on Little’s Law for a stable flow. It assumes requests are independent and that latency does not sharply increase as concurrency rises.
The result is the minimum rounded concurrent-request capacity after the selected buffer.
Load testing is still necessary because queueing, shared dependencies, and throttling can make real capacity lower.
Given: 120 TPS, 180 ms average latency, 250 ms target, and 25% buffer.
Calculation: Raw concurrency = 120 × 0.180 = 21.6. Buffered concurrency = 21.6 × 1.25 = 27.0.
Result: Plan for 27 concurrent requests. At 180 ms each, that supports about 150 TPS in the idealized model.
Capacity failures occur during peaks. Average traffic can materially understate the concurrency needed for fraud checks at busy times.
For conservative planning, use a high percentile such as p95 if it represents your service objective. Mean latency is better for average utilization estimates.
It means measured latency exceeds the entered target by that amount. Increasing concurrency alone may not fix it if processing time is the bottleneck.
Yes. Use effective transaction throughput and batch latency, then validate the result because batching can introduce wait time.
A fractional request slot cannot be provisioned, and rounding down would leave theoretical capacity below the requirement.