AI Latency Budget Simulator
Model user-perceived task latency, see where time is spent and test a throughput improvement scenario.
← Explore all eight toolsEnter your scenario
The fields below contain example values. Replace them with your own evidence; nothing is sent to a Ninth Post server.
How this tool works
A simplified sequential pipeline sums network, retrieval, tool operations, time to first token, token generation (generated tokens / tokens per second × 1,000) and rendering. The result is not a live measurement. A user-supplied tail multiplier illustrates what slower operations could do to latency, but it is not a statistical p95 estimator. A separate scenario increases output throughput by 30% while holding other stages constant.
Limitations and responsible use
Real systems may execute stages in parallel, stream tokens, retry work, cache inputs, queue requests or use multiple models. Model your actual critical path, capture p50/p95 from production traces, and assess answer quality together with speed. The hypothetical tail scenario should never be presented as measured p95.
Continue the research
Read the related Ninth Post guide: AI Latency Budget Simulator — companion analysis. Review the primary methodology or framework source and our editorial standards. Results reflect assumptions, not independent testing.