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AI Agent Shadow / A-B Test Cost Calculator

Estimate the cost to run an AI agent in shadow mode or A/B test against an existing system, including duplicated inference, human review, decision logging, and outcome tracking.

Scenario presets

Test configuration

Test type Production requests / month Test traffic percentage (%) Agent tasks per request Input tokens / task Output tokens / task LLM provider Cached input tokens (%) Decision log size (KB) Storage backend Log retention (months) Human review rate (%) Review minutes / decision Reviewer hourly cost ($) Enable outcome tracking Outcome cost / tracked event ($) Evaluation runs / month Eval input tokens Eval output tokens Setup hours Setup hourly rate ($) Maintenance hours / month Maintenance hourly rate ($) Projection (months)

Estimates are directional. Last updated: 2026-07-08. See notes.

Monthly test requests

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Monthly inference cost

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Monthly review cost

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Total period cost

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Logging & storage cost

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Outcome tracking cost

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Evaluation cost

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Setup + maintenance

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Cost per 1K production requests

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Cost per decision reviewed

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Cost breakdown

Cost line	Monthly	Period	Share
Duplicated inference	—	—	—
Decision logging & storage	—	—	—
Outcome tracking	—	—	—
Human review	—	—	—
Evaluation runs	—	—	—
Setup & maintenance labor	—	—	—
Total	—	—	—

LLM provider comparison (inference only)

Provider	Monthly inference	Period inference	Note

Verdict

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Frequently asked questions

What does this calculator estimate?

It estimates the incremental cost of running an AI agent alongside an existing system: shadow mode (duplicate inference on 100% of traffic without changing user output) or A/B test (serving the new agent to a percentage of users).

Does it include the cost of the existing production system?

No. It only adds the new shadow/A/B test cost. The baseline production cost is assumed unchanged unless you are running an A/B test where some traffic now uses the new agent directly.

What is decision logging?

Every shadow or A/B agent run produces a decision record: input, output, latency, model version, and (in A/B tests) bucket assignment. Logging cost depends on record size, retention, and storage backend.

Why is outcome tracking a separate cost?

Outcome tracking connects an agent decision to a later business event (e.g., conversion, ticket resolution, churn). It may require event ingestion, attribution joins, or feedback labels.

How is human review cost modeled?

Reviewers inspect a percentage of decisions where the new agent disagrees with the old system or where confidence is low. Cost is review rate × decisions × review minutes × reviewer hourly rate.

When is shadow mode cheaper than A/B?

Shadow mode avoids user-facing risk and can often use a smaller traffic slice, but it duplicates every logged decision. A/B testing is cheaper per observed decision when you only need a sample, but it requires rollout tooling and may affect user outcomes.

How can I reduce shadow/A/B test cost?

Use a smaller, cheaper model for the candidate agent, cache repeated context, sample a lower traffic percentage, shorten retention, automate evaluation instead of full human review, and use object storage rather than managed databases.

Prompt Testing Budget → API Pricing Comparator → Agent Output Value → Evaluation Planner →

Directional estimates. Shadow and A/B tests duplicate inference on a subset of production traffic. Costs include duplicated LLM calls, decision logging, outcome tracking, and human review of disagreements. They do not include the base production agent cost, product engineering, or rollout tooling.