GEO program

What does a GEO program cost to run?

Nobody has published a credible benchmark, so here is the cost structure instead: the unit that recurs, the arithmetic that sizes it, and the five multipliers that make two honest designs differ eighty-fold.

GEO program12 min readUpdated 30 Aug 20267 sources, all primary

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The short answer

No credible industry benchmark exists for what a GEO programme costs, so the honest answer is a structure rather than a number: one line that recurs at full size forever, the measurement panel, priced at prompts multiplied by runs multiplied by engines, plus project-shaped lines that behave like ordinary marketing spend. The panel is the interesting one, because the statistics dictate its size rather than taste, which makes the width of the interval you are willing to publish the thing that sets your bill. Repeated runs of the same prompt at temperature zero change 9 to 28% of the decisions they produce, which is why one run per prompt is the cheapest way to buy a number nobody can act on.¹

Key takeaways

The recurring unit is one observation: one prompt, run once, on one engine. Prompts times runs times engines times periods is the measurement line.
Precision is quadratic. Halving the interval you publish quadruples the observations behind it, and the cost of collecting them.
Repetition is not overhead you can trim. One run of one prompt carries a 95% band about 45 points wide, and no saving makes that usable.
The technical work is one sprint, content is a project, off-site is a small permanent trickle. Only the panel recurs at full size.
Two defensible designs can differ eighty-fold, so publish the design next to the price. A dollar figure with no panel spec behind it describes nothing.

The unit

What is the recurring unit of cost?

One observation is one prompt, run once, on one engine, and it is the atom every other cost is quoted in. A panel of 40 prompts run 5 times a week across 5 engines produces 1,000 collections a week, which is the figure that belongs in the budget line where most plans write “monitoring”. Over a year, 52,000 of them.

Asking is not where the money goes. A collection is worth keeping only if it carries the record that makes it comparable to the next one, and the field’s 2026 critical survey specifies that record as the product, the mode, the model, the date, the locale, the account and whether search was enabled, alongside the answer text and the URLs it cited.¹ Seven system fields plus the payload, on every one of the thousand. Call it fifteen seconds an observation done by hand, for paste, wait, record presence, copy the cited URLs, and a thousand collections is close to four hours a week. That timing is ours rather than a published benchmark, and it is the first number here to replace with your own.

The budgeting consequence is that this line has no economies of scale until somebody automates it: ten times the panel is ten times the hours. The fields worth logging settles what you record, before you settle how often.

Precision

Why does the interval you publish set the bill?

The panel has a size the statistics dictate, not one somebody picked, so what you are really buying is a width in percentage points.

A presence rate is a sample proportion, and every one on this site carries a 95% Wilson interval whose half-width at a 50% rate is 1.96 divided by twice the square root of (n plus 3.84).² Two hundred observations on one engine in one period is a band of about 7 points. Thirty is 17. Five is 33.

Run that formula backwards and it prices the programme. Reaching a half-width of w takes roughly 0.96 divided by w squared, minus 3.84, observations, so the requirement is quadratic: every halving of the band you promise multiplies the collections behind it by four. Going from a 7-point band to a 3.5-point band takes the same panel from about 200 observations a period to about 780, and the bill goes with it. How many prompts and runs works the full table out; the budgeting point is that you are not buying prompts, you are buying a width.

Detecting a change costs more than reporting a level, and the gap catches out teams that budget for the first while promising the second. A weekly report of 200 observations per engine resolves a movement of roughly 13 to 14 points and nothing smaller, which statistical power for a before-and-after test derives in full. So the sentence that sets your budget is never “we want to measure AI visibility”. It is “the smallest movement we would change plans over is N points”, and every N has a price.

Repetition

Can you save money by running each prompt once?

No, and it is the most expensive saving on the menu. The same prompt asked twice does not reliably give the same answer: repeated runs at temperature zero changed 9 to 28% of decisions in the study the 2026 critical survey reports it from.¹ Cutting five runs to one takes a thousand collections a week down to two hundred, and takes the band on any single prompt out to 45 points, a quantity with no decision attached to it.

Runs two through five are the cheapest purchase in the whole programme, and the sixth onward is where optional spending starts. Taking a 40-prompt panel from one run to five costs 160 extra collections a week and moves the panel band from about 15 points to about 7. Doubling the prompts instead, at five runs each, costs 200 extra collections and buys 2 points. The survey records one published proposal of seven to eight repetitions per prompt and qualifies it in the same breath as not a universal standard, from a small Swiss query universe with at most ten repetitions.¹

There is an upper bound worth paying for, and the engines set it rather than the arithmetic. Past roughly 600 observations per engine per period, the precision you buy is narrower than the drift these systems produce on their own week to week, so the money buys a tighter interval around a quantity that has already moved.

The lines

Which cost lines recur, and which are one-time?

Six lines, and only one recurs at full size. Budgets go wrong when the project-shaped lines get modelled as subscriptions and the panel as a launch cost.

Line	Shape	What drives it
Measurement panel	Recurring, every period	Prompts times runs times engines. The only line that never gets smaller.
Technical fixes	One sprint, then near zero	Retrievability work. Google’s own guide lists llms.txt files and other “special” markup among the things you can ignore for Google Search, which deletes a whole category of proposed spend⁷
Crawler bandwidth	Recurring, usually small	Concentrated in HTML that misses the cache. Read against your own edge logs, never anyone’s benchmark
Content rewrites	Project-shaped, per page	Roughly a day per high-intent page for a real rewrite. Our own estimate, not a benchmark
Off-site work	Continuous and small	A couple of hours a week indefinitely. The line most often cut first
Human answer review	Recurring, non-negotiable	A presence flag counts a wrong answer as a win, so somebody reads a sample

Human review is the line that gets deleted from spreadsheets and should not be. A presence flag records that you were named; it cannot record that you were named incorrectly, and the Tow Center’s audit of eight AI search tools across sixteen hundred queries found they answered more than 60% of them incorrectly, the worst at 94% and the best still at 37%.⁴ Reading a sample by hand each period is a recurring labour cost with no software substitute; the metrics that hold up sets out which sample. For bandwidth, what AI crawl traffic looks like has the method.

The spread

Where does the cost vary by orders of magnitude?

In the multipliers, and they compound. Engines in scope, prompts in the panel, runs per prompt, periods per year and segment splits are five independent choices, each of which reads like a detail in a planning meeting and each of which multiplies the recurring line.

Segment splits are the one teams miss. The critical survey’s logging schema treats locale, account state and mode as part of the system record rather than as filters,¹ which is the same statement as: every combination you want a separate number for is a separate panel. One engine, 40 prompts, 5 runs, reported monthly is 200 collections a month. Five engines, two locales, 80 prompts, 5 runs, reported weekly is 16,000 a month. Both are defensible, both get described to a board in the same words, and one is eighty times the other.

Which is why this article contains no dollar range. A price with no panel specification behind it is not a price for anything, and the arithmetic that opens an eighty-fold spread also makes a published average meaningless: an average across designs two orders of magnitude apart describes none of them. Write the design, count the collections, then price them at what your own team costs. That last step is the only one where a local number is defensible.

The return

How do you know the spend was worth it?

Not from referral traffic, and settling that before the budget rather than after saves a quarter. Pew Research Center’s browsing study of 900 US adults, covering 68,879 Google searches with data collected in March 2025, found users who met an AI summary clicked a traditional result on 8% of visits against 15% for those who did not, and clicked a link inside the summary itself in just 1% of all visits to a page carrying one.³ A surface with a click rate that thin cannot be justified by its clicks.

Nor can the step from citation to money be assumed, because nobody has established it. The 2026 critical survey grades the claim that citation scores predict clicks, conversions or revenue at very low confidence, the lowest rating in its table.¹ Anyone quoting a return on GEO spend is quoting something the field says out loud it cannot support, which is a reason to fund the work as a measured experiment rather than a forecast.

The content line deserves the most scepticism, because it is the one most often sold. C-SEO Bench, a NeurIPS 2025 benchmark covering ten methods, more than 1.9k queries and 16k documents, found only three of 54 cases where the ranking improvements were statistically significant.⁵ SAGEO Arena, published at KDD 2026 over 171,003 documents and 2,700 queries, tested ten strategies and found its body-text-only group below the untouched baseline on all three reported measures: 0.53 against 0.58, 0.84 against 1.00, and 0.47 against 0.50.⁶

A team that prices its first quarter as a content project, buys ten page rewrites and skips the panel ends it unable to say whether anything happened, the most expensive outcome at any budget. The order that survives a finance review runs the other way: panel first, because it is the only line that produces evidence; technical fixes second, because they are a sprint with a finish line; content and off-site third, sized to what the panel says is moving. A defensible report covers what to publish afterwards, including the month when nothing moved.

The honest limit of this article

No independent cost benchmark for a GEO programme exists. Nobody has surveyed what teams spend, no peer-reviewed work prices the labour, and a vendor price list prices a product rather than a programme, so you get multipliers here instead of a range. That is the state of the evidence, not modesty. Two figures on this page are ours alone: fifteen seconds per manual collection, and about a day per high-intent page rewrite. Neither has been replicated, and both should be replaced by your own timings in the first month. What holds whatever your labour rate turns out to be is the arithmetic: the Wilson interval, the quadratic precision requirement, and the multiplier structure.

Where a product fits, and where it does not

Everything above runs on a spreadsheet and a timer, and for a single-engine panel doing it by hand for the first month is how you learn what a collection costs in your team. Bavior automates the recurring line and nothing else: it runs a fixed prompt set across five engines on a schedule and records presence and the cited URLs per run with the system fields attached, and where a cited source is a live discussion thread it drafts a reply on an account you control, which you approve before anything posts. It does not do the technical fix work, does not write the content line, does not price your labour, and cannot tell you what a citation is worth, because no published research can yet. The free AI visibility check and the free GEO audit run without a paid plan; paid plans start at $99/mo billed monthly, or $79.17/mo billed annually at $950 a year (as of 30 Aug 2026).

Sources, all checked 30 Aug 2026

“Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026)”, 15 Jul 2026, arXiv:2607.14035 (survey preprint). The 9 to 28% figure is Kirsten et al. reported there, not the survey’s own; same paper for the logging schema and the confidence table: arxiv.org/abs/2607.14035
Brown, Cai, DasGupta, “Interval Estimation for a Binomial Proportion”, Statistical Science 16(2), 2001; the Wilson score interval: doi.org/10.1214/ss/1009213286
Pew Research Center, “Google users are less likely to click on links when an AI summary appears in the results”, 22 Jul 2025; 900 US adults, 68,879 Google searches, data collected March 2025: pewresearch.org
Jaźwińska & Chandrasekar, Tow Center for Digital Journalism, “AI Search Has a Citation Problem”, 6 Mar 2025; sixteen hundred queries across eight engines: cjr.org
Puerto, Gubri, Green, Oh, Yun, “C-SEO Bench: Does Conversational SEO Work?”, NeurIPS 2025 Datasets & Benchmarks Track, arXiv:2506.11097; ten methods; three of 54 cases significant: arxiv.org/abs/2506.11097
Kim et al., “SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization”, KDD 2026 (arXiv:2602.12187v2); Table 2 body-text-only averages: arxiv.org/abs/2602.12187
Google Search Central, “Google’s Guide to Optimizing for Generative AI Features on Google Search”, last updated 10 Jul 2026 (first-party): developers.google.com/search/docs/fundamentals/ai-optimization-guide

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FAQ

Frequently asked questions.

What does a GEO program cost per month?

Nobody can answer that honestly without your panel specification, because there is no published benchmark and the design drives the number. One engine, 40 prompts, 5 runs, reported monthly is 200 collections. Five engines, two locales, 80 prompts, 5 runs, reported weekly is 16,000 a month. Both are defensible programmes and one is eighty times the other, so count your own collections and price them at your own labour rate.

Is it cheaper to measure fewer engines?

Yes, proportionally, and it is the cleanest cut available because engines multiply the recurring line directly. The cost is coverage rather than precision: the panel on each remaining engine keeps its band, you simply stop having a number for the ones you dropped. Never average across engines to save collections, because a blended rate cannot answer the question the metric exists for, which engine to work on next.

Can we run the panel by hand, or do we need a tool?

By hand works and is worth doing for the first month, because it is how you find out what a collection costs in your team. The limit is consistency rather than effort: a person doing a thousand repetitive collections produces quiet inconsistencies in the record, and the record is what makes one period comparable to the next. Most teams automate somewhere between week three and week six for that reason.

What is the cheapest program that still produces a defensible number?

One engine, about 40 prompts, 5 runs each, reported monthly rather than weekly. That is 200 observations behind a panel rate with a 95% band of roughly 7 points, published with its n, its engine and its dates attached. It will not detect a movement smaller than about 13 points, so say that in the report rather than drawing a trend arrow the data cannot carry.

Bavior Editorial

The team that researches and maintains Bavior’s writing on Reddit marketing and AI search visibility. Every figure here is attributed to a named source with the date it was checked, and none of our links are affiliate links.

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