TokenPad
Reference

LLM Pipeline Latency Estimator

Add up a multi-step pipeline and find the step to fix.

One step per line: name | input tokens | output tokens

67 characters3 lines0 tokensor drop a file

Latency EstimatorExact
0Token change
Token change0no change
Input tokens0what you pasted
Output tokens0what you would send
Token cost of this result
Output tokens0
As input$0.00
× 100K requests$0.00

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Result
 

Output length dominates latency

Generation is sequential — one token at a time — so latency scales almost linearly with output length. Input affects the first token; output affects everything after it.

That makes "ask for a shorter answer" the single most effective latency optimisation available, and it reduces cost at the same time.

Most pipelines are needlessly serial

A pipeline that classifies, then summarises, then drafts is often three calls with no real dependency between the first two, presented as a sequence out of habit.

Running independent steps in parallel bounds total latency by the slowest step instead of the sum. The comparison here makes the size of that difference obvious.

Frequently asked questions

What latency do users actually tolerate?
Under three seconds feels responsive. Up to ten is tolerable with streaming or visible progress. Beyond that abandonment rises sharply unless the user has been told to expect a wait.
Does a bigger model always mean higher latency?
Usually higher per-token latency, yes. But a larger model that answers in 200 tokens can finish before a smaller one that takes 600 — compare total time, not tokens per second.

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