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AI in Markets Calculator Guide

How to use Fallback Chain Simulator

Define a provider fallback chain. The page simulates rate-limit and latency failures across a configurable load profile and reports p50/p95/p99 latency, success rate, total cost, and degradation events so you can size the chain before deploying it.

By AI Fin Hub Research · AI Fin Hub Team

What It Does

Use the calculator with intent

Define a provider fallback chain. The page simulates rate-limit and latency failures across a configurable load profile and reports p50/p95/p99 latency, success rate, total cost, and degradation events so you can size the chain before deploying it.

Reliability engineers designing multi-provider LLM stacks who learned that a single-provider outage takes the agent down — and need to size the fallback before it matters.

Interpreting Results

p99 latency is the headline — most agents need p99 under a budget for interactive use. Success rate close to 100% means the chain is robust enough; below 99% suggests adding another fallback. Cost is the trade-off — robust chains cost more.

Input Steps

Field by field

  1. 1

    Add legs

    Add up to three legs in priority order: primary, then fallback 1 and fallback 2.

  2. 2

    Configure each leg

    For each leg, pick the provider and model, then set the 429 failure-rate slider (0–30%) and the p99 latency.

  3. 3

    Set request inputs

    Set the request-level inputs — input/output token counts and the per-call deadline. Cost is derived from tokens × each model's price, not entered per call.

  4. 4

    Run calculation

    Run the simulator. Read overall success rate, p50/p95/p99 latency, total cost, and the degradation-event distribution.

  5. 5

    Compare results

    Compare a 2-leg chain against a 3-leg chain; the marginal success-rate gain usually shrinks after the second fallback. Each leg is tried once — there is no retry toggle.

Common Scenarios

Use realistic starting points

Two-provider chain (Claude → GPT)

Primary

Sonnet

Fallback

GPT-5.4 mini

p99 dominated by fallback latency; success rate ~99.5%. Cost slightly higher than primary-only when fallback fires.

Three-provider chain (Claude → GPT → Gemini)

Primary

Sonnet

Fallbacks

GPT-5.4 mini, Gemini 2.5 Flash

Success rate ~99.9%; p99 driven by the slowest provider in the chain. Cost steps up further when both fallbacks fire on the same call.

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FAQ

Questions people ask next

The short answers readers usually want after the first pass.

A sequence of LLM endpoints to try in order when an upstream call fails: e.g., primary Anthropic, fallback OpenAI, last-resort cache. Production agent systems use chains to maintain availability when individual providers go down or rate-limit. The simulator models cost, latency, and success rate across the chain.

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