A professionally run AI visibility audit typically costs $8,000–$25,000 as a one-time engagement, and where you land in that band comes down to three things: how large a competitive set you benchmark against, how deep a prompt basket you measure, and whether the engagement stops at findings or continues into implementation. That’s the same range we publish in our AI visibility audit playbook; this post is the dedicated pricing breakdown — the models, the drivers, the tooling budget underneath, and the honest DIY alternative.
The three ways AI visibility work is priced
Like most consulting, AI visibility and generative engine optimization (GEO) work maps to three pricing structures. The audit is the entry point; the other two exist because an audit tells you where the gaps are but doesn’t close them.
| Model | Typical range | What it covers |
|---|---|---|
| One-time audit | $8K–$25K fixed fee | Baseline across engines, gap analysis, revenue model, prioritized recommendations |
| Audit + implementation | Mid five figures combined, scaling with content scope | Everything above, plus the content production, schema, and structural work the audit prescribes |
| Ongoing GEO program | A few $K/month up to five figures monthly | Continuous implementation plus recurring re-measurement against the same prompt basket |
The audit band is first-party: it’s what engagement-grade audits typically run, scaling with competitive set size and prompt basket depth. The implementation and retainer ranges follow the same market bands we describe in our AI consulting pricing guide — scoped project work commonly lands in the five figures, and retainers run from a few thousand to tens of thousands per month depending on intensity. Treat those two as orientation rather than a quote, because implementation scope varies far more than audit scope does.
What drives the number up or down
Two audits that sound identical in a proposal can differ by 3x in price. Five factors explain most of the spread:
Prompt-basket size. The core of the audit is running a curated basket of buyer questions — typically 50–500 high-intent prompts — through each engine and scoring who gets mentioned and who gets cited. A 500-prompt basket costs more to curate, run, and hand-review than a 50-prompt one, and curation is where audit quality lives or dies.
Number of engines tracked. ChatGPT, Perplexity, Gemini, Google AI Overviews, and Microsoft Copilot cite differently — ChatGPT favors structured topical authority while AI Overviews lean on content that already ranks — so each engine added is a separate measurement surface, not a copy-paste.
Competitor set size. Every downstream finding inherits the quality of the competitor list, and each added competitor multiplies the keyword gap, content audit, and citation benchmarking work.
Whether implementation is included. An audit that ends with structured content briefs costs less than an engagement that also writes, restructures, and marks up the content. This single scope line is the biggest jump between the $8K end and the $25K end — and beyond it.
Tracking cadence. A snapshot is cheaper than a measurement system. Quarterly re-runs against the same prompt basket add recurring cost but are what turn the audit into a before-and-after instrument rather than a one-time opinion.
The one-line test for any audit quote. You are paying for measurement, not opinions — if the audit can’t be re-run against the same prompt basket next quarter, you bought a slide deck, not a baseline.
The tooling budget underneath the audit
Whoever runs the audit — you or a consultant — the work sits on a tool stack, and the stack has three honest tiers. These are the same tiers, with the same dollar ranges, that we detail step-by-step in the playbook:
| Tier | Stack | Cost | Tradeoff |
|---|---|---|---|
| Minimum viable | Ahrefs or Semrush (~$200/mo) + ChatGPT and Claude APIs (~$100–$300 run cost) + a spreadsheet | ~$1,000 one-time if analyst time is in-house | No ongoing measurement — rerunning means redoing it by hand |
| Mid-tier | Ahrefs + a paid AI visibility platform (Profound, Athena, Otterly, Peec AI), ~$1,000–$2,500/mo combined | ~$3,000–$5,000 for the one-time audit, plus recurring tool cost for tracking | The tool's prompt basket may not match your buyer journey |
| Engagement-grade | Curated prompt basket, custom dashboard, revenue model tied to your conversion data, quarterly re-runs | $8K–$25K one-time; ongoing tracking adds on top | The most expensive tier — worth it only if the findings will drive real budget |
Standalone mid-tier AI visibility platforms typically price in the $500–$2,000/month range on their own; the combined figure above includes the SEO tooling the traditional gap-analysis steps still require. If you want the conceptual grounding before the tooling decision, our complete guide to AI visibility & GEO covers how the engines choose what to cite in the first place.
Can you do it yourself?
Yes — the methodology is public, and we published the complete six-step framework precisely so teams can run it internally. The honest cost of DIY is time. Summing the per-step budgets from the playbook: run entirely by hand, the six steps of an AI visibility audit take 36–70 analyst hours; with a paid tool handling the AI measurement step, the total drops to roughly 28–53 hours.
| Step | Hour budget |
|---|---|
| 1. Domain metrics comparison | 2–4 hours |
| 2. Keyword gap analysis | 4–8 hours |
| 3. Competitor content audit | 6–10 hours |
| 4. AI visibility analysis | 12–25 hours DIY (4–8 with a paid tool) |
| 5. Revenue impact modeling | 4–8 hours |
| 6. Content recommendations | 8–15 hours |
DIY makes sense when you have a senior in-house SEO or analytics lead, an analyst with the hours, and enough engineering bandwidth to build the prompt-running harness. What we consistently see when teams try it: steps 1–3 go well because SEO teams already do that work, step 4 is where most stall — either the harness never gets built or a paid tool’s generic prompt basket gets accepted without curation — step 5 gets skipped, and step 6 ends up as a list of topics instead of structured briefs. Outside help earns its fee on exactly those three: prompt-basket curation, scenario-band revenue modeling, and the recurring measurement infrastructure.
What you should get for the money
Whatever the price, an audit worth paying for delivers four things. A baseline: which engines cite you today, for which prompts, and how that compares to each competitor — with mentions and citations counted separately, because mentions mean AI knows your brand while citations mean AI trusts your content enough to recommend it. A gap analysis: the specific keywords and buyer prompts where competitors are cited and you are absent. A revenue model in explicit scenario bands rather than a single point estimate. And before-and-after tracking, so the same measurement can be re-run after the work and the change attributed per engine.
For a concrete example of what that output looks like, our Fast Growing Trees analysis is the full deliverable published as a case study: 3,154 keyword gaps, 13 AI prompt gaps where competitors are cited by Google AI Overview and the brand is not, and over $760K in estimated annual impact from closing both. That’s the shape of finding the fee is buying — a dominant #1-in-Google brand that turned out to be losing the citation layer, which no traditional SEO audit would have surfaced.
One budgeting note we’d give any buyer: the audit is the cheap part. Its value is realized in the implementation that follows, so if the audit fee would consume the entire budget, run the mid-tier version instead and reserve the difference for closing the gaps it finds.