How do retry assumptions affect cost?

Each retry repeats the input cost (prompt + context) and adds a fresh output cost. At 3 retries with a long context, total cost can be 4× the single-call cost. The calculator surfaces both 'happy path' (zero retries) and 'pessimistic' (max retries) numbers — the latter is what to budget against.

Should I budget for the median or the 95th percentile?

If the envelope is volatile (heavy tail of failure modes), budget against the 95th percentile. If retries are rare and cheap, the median is fine. The calculator shows the gap so you can see how tight the distribution is. A 4× ratio between median and 95th means your worst-case is 4× normal — plan accordingly.

What's a common mistake when using Agent Cost Envelope Calculator?

Sizing the cap to the median loop, not the p95. Agents fail upward — one runaway loop dominates the daily cost. Size to p95 at least.

How do retry rates compound the cost envelope?

Ignoring retry inflation. A 5% error rate at 3 retries each adds 15% to every cost line; bake retry policy into the cost model.

AI in Markets Calculator Guide

How to use Agent Cost Envelope Calculator

Model an LLM research loop end-to-end — steps per decision, tool calls, convergence checks, markets per day. The page reports per-loop, daily, and monthly cost with an explicit cost-cap so a runaway agent can't burn the budget.

5 STEPSPublished May 12, 2026Live Content

By Orbyd Editorial · AI Fin Hub Team

Best Next MoveCalculators

Agent Cost Envelope Calculator

Model an LLM research loop end-to-end — steps, tool calls, convergence checks, markets per day — and see per-loop, daily, and monthly cost with cost-cap.

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On This Page

Overview 5 steps Scenarios FAQ

What It Does

Use the calculator with intent

Engineers deploying autonomous LLM agents who learned that a single loop bug can drain a month's API budget in an hour and need a cost-ceiling before deployment.

Interpreting Results

Daily cost at p95 is the realistic worst-case budget. Monthly cap is the absolute kill-switch. Per-loop cost identifies whether the budget breach is one expensive loop or many cheap ones — different fixes.

Input Steps

Field by field

1

Enter inputs

Enter your model selection, prompt length, output length, and expected call volume per task.
2

Set parameters

Set retry assumptions: max retries on error, max retries on timeout, and the cache hit rate for the prompt prefix.
3

Read outputs

Read the cost envelope: minimum (best case), median (typical), and 95th percentile (pessimistic). Budget against the 95th percentile if your workflow has heavy-tailed failure modes.
4

Toggle setting

Toggle 'fallback chain enabled' to model what happens when the primary model fails. Fallback adds reliability at a multiplied cost.
5

Compare results

Compare envelopes across model choices. The cheapest model isn't always the cheapest envelope — frequent retries on a weak model can exceed the cost of a stronger model used once.

Common Scenarios

Use realistic starting points

Research agent (low frequency)

Steps per decision

Decisions per day

Model

Sonnet

Per-loop cost ~$0.10-0.50; daily cost moderate. Cap should comfortably cover p95 daily.

Trading research agent (high frequency)

Steps per decision

Decisions per day

200

Model

Sonnet

Daily cost dominates; one bad week of retries can blow the monthly budget. Tighten convergence + fallback before scaling.

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Fallback Chain Simulator

Define a provider fallback chain, simulate rate-limit and latency failures, and see p50/p95/p99 latency, success rate, total cost, and degradation-event.

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FAQ

Questions people ask next

The short answers readers usually want after the first pass.

The full per-task cost band: minimum (cheapest model + cache hit + no retries) to maximum (best model + no cache + max retries). The envelope shows worst-case spend, not just the average. It matters because agent cost distributions have heavy right tails — one runaway loop can double the monthly bill.

Keep the topic connected

AI in Markets1 FAQS

Agent-Cost Envelope

The agent-cost envelope: the loop of (calls × tokens × retries × model_price) that determines the dollar cost of an LLM-driven trading agent per decision.

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AI in Markets1 FAQS