2026-09-22 — Analytics · Fundamentals

Estimating your AI-answer monitoring bill before you spend a credit

The question that stalls most AI-visibility pilots is not “does it work” — it is “what will this cost me per month”. Because AI-answer APIs price per request, the bill is fully deterministic once you fix three numbers: how many prompts you track, which engines you run them on, and how often.

The formula

monthly credits = prompts × engines × runs per month × credits per call

Every factor is under your control, which means the plan can be tightened before anything is spent:

• Prompts — the size of your tracked prompt set. Most teams overestimate; 50–150 well-chosen prompts cover a category better than 1,000 redundant ones (see the prompt set design guide).
• Engines — weight engines by where your audience actually is. ChatGPT and Google AI Mode/Overview usually matter most; Grok and Copilot can often run at lower cadence.
• Runs per month — volatility decides this, not habit. High-volatility categories need daily runs; stable brand queries can run weekly without losing signal. The answer volatility post shows how to measure this on your own prompts.
• Credits per call — fixed per engine and listed on the pricing page. Google Search calls are cheaper than AI-answer calls, so a blended SERP+AI plan costs less than an all-AI one.

A worked example

A category tracker for a mid-market SaaS:

• 80 prompts (40 category, 25 competitor-comparison, 15 brand)
• Engines: ChatGPT + Perplexity + Google AI Mode + Google Search
• Cadence: daily on AI engines, weekly on SERP

80 × 3 AI engines × 30 days × 5 credits  = 36,000 credits
80 × 1 SERP engine × 4 weeks × 3 credits =    960 credits
total                                    ≈ 37,000 credits / month

Put that into the estimator on the pricing page and it recommends the matching tier directly.

Where teams overspend

• Running every engine on every prompt. Not every prompt needs every engine — brand prompts may only need the two engines your buyers use.
• Daily cadence on stable prompts. Sample weekly first; increase cadence only where volatility is measured, not assumed.
• Re-collecting instead of diffing. Async batch tasks let you collect once and diff against stored results — cheaper than ad-hoc re-queries.
• Sync calls for scheduled work. Scheduled monitoring belongs in async batches with webhooks; sync calls are for interactive use.

The control loop

Start small — 30 prompts, two engines, weekly — and measure two things for two weeks: volatility (does daily vs weekly change conclusions?) and coverage (do the prompts still represent the category?). Scale the dimensions that prove to matter. Per-request pricing means nothing is wasted while you calibrate.

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