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Reliability economics

LLM Fallback Chain Cost Calculator

Price retries and model fallbacks to see what one successful AI task really costs.

Your scenario

Adjust the assumptions. Results update instantly.

tasks

Completed or attempted customer workflows.

tokens

Context sent on each primary, retry, or fallback attempt.

tokens

Average generated output for each attempt.

%

Timeout, invalid-output, safety, or quality rejection rate.

Maximum times the primary model is retried before fallback.

%

Share of fallback attempts that produce an accepted result.

$

Search, browser, API, or retrieval cost repeated on each attempt.

Methodology

How this calculator works

1

Estimate how many primary attempts occur after accounting for retries.

2

Price the fallback traffic that survives every primary attempt.

3

Divide total workflow spend by successful tasks to expose the reliability tax.

What makes this useful

A cheap request is irrelevant if several attempts are required to produce one accepted result.

Frequently asked questions

Why calculate cost per successful task?

Customers pay for outcomes, while providers bill every attempt. Retries and failures make cost per accepted outcome higher than cost per request.

When should I use a fallback model?

Fallbacks are useful when they improve completion rates enough to justify added cost and latency. The best chain depends on failure modes, not only model price.

Do retries resend the entire context?

Often yes. If a retry repeats the original request or accumulated context, it can incur most or all of the original token cost again.

Continue your analysis

LLM Fallback Chain Cost Calculator uses current model prices and your operating assumptions to estimate business impact. Treat the output as a planning model, then replace defaults with p50, p95, and p99 telemetry from your own product.

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