AEO for a B2B SaaS company runs anywhere from a few hundred dollars a month in tooling to tens of thousands for managed programs. Anyone quoting one number without asking about your prompt set is guessing.
That range is honest, which is why it frustrates people. The useful move is not narrowing it by fiat but understanding the four variables that place you inside it, because those are the levers a quote is actually pricing.
So here is what drives the cost, what each delivery model really buys, and the questions that make two quotes comparable.
What actually drives AEO cost?
Four variables, and they compound. Every credible quote in answer engine optimization is some function of these.
How many prompts are you competing on?
The prompt set is the unit of work. Twenty prompts means twenty answers to win, each with its own winning sources to study and content to build or fix.
A seed-stage product with 15 buyer questions and an enterprise suite with 200 are different projects by an order of magnitude. This is the first thing a serious provider asks, and silence about it is the first red flag.
Prompt difficulty prices in too. A contested comparison prompt costs more to win than an unowned niche question, so two sets of equal size can price differently once someone reads who currently holds the answers.
How many engines matter to you?
Each engine multiplies the scanning and diverges the tactics. ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews retrieve differently, and the same prompt returns different sources on each.
Five-engine coverage is not five times one-engine cost, but it is not free either. Scope it explicitly.
Engines also differ in what they reward, so coverage decisions are strategic rather than additive. Paying to scan a surface your buyers never use is the most common quiet waste in these budgets.
One-off audit or standing loop?
A diagnosis is cheap. A 40-point audit tells you where you stand and what to fix, once.
The loop is where the spend lives: re-scanning on a cadence, catching answer shifts when models update, feeding losses back into content. It is also where the results live, because a snapshot cannot tell you whether anything you changed worked.
Diagnosis only, or fixes included?
The audit and the remediation are separate scopes, and most confusing quotes blur them. Content production, listing hygiene, technical fixes and community presence each carry their own cost.
Ask which side of that line every deliverable sits on. What a finished audit report contains is a reasonable proxy for whether a provider's diagnosis is worth paying for at all.
What do the delivery models cost?
Three ways to buy this, with genuinely different economics. The honest comparison is cost against who does the work.
| Model | Typical monthly range | Who does the work | Fits when |
|---|---|---|---|
| Tools only | Low hundreds | Your team, entirely | In-house content capacity exists |
| Agency or managed | Low thousands to tens of thousands | The provider, end to end | No spare content or SEO capacity |
| In-house build | A salary plus tooling | A hire you manage | Prompt set is large and permanent |
Why is the tools-only number misleading?
Because the subscription is the visible cost and the labour is the real one. A tracking dashboard shows you losses; someone still has to write, fix listings and re-check.
Teams that budget the subscription and not the hours get a well-measured absence. The tools roundup covers what tracking buys; none of it includes the doing.
What separates a cheap agency from an expensive one?
Mostly whether content production is inside the retainer, how many engines they scan, and whether re-scanning is continuous or quarterly. Occasionally the difference is just positioning.
The comparison that matters is deliverables per month against your prompt count. A large retainer covering 200 prompts with content included can be better value than a small one covering 20 with content extra.
When does in-house win?
When the prompt set is big enough to keep a person busy permanently, which usually means a few hundred prompts across several product lines. Below that, the salary out-costs a retainer.
The hybrid is common and sane: tools plus a fractional owner internally, with production bursts bought outside.
The wrong version of in-house is assigning it to whoever runs SEO as a side duty with no scanning budget. That produces the worst of both models: salary cost, tooling gaps, and a prompt set nobody re-checks.
Which questions make quotes comparable?
Five, and they map directly to the four cost drivers.
- How many prompts does this price cover, and who builds the set?
- Which engines are scanned, and on what cadence?
- Is content production included, and how many pieces a month?
- What does the monthly report show: presence and citation split per prompt, or an aggregate score?
- What happens when a model update shifts the answers?
A provider fluent in those five is pricing work. One who answers with a package tier is pricing a package. The same five questions underpin choosing between the agency roundup options, whoever you end up shortlisting.
What should the money return?
Named citations on named prompts, moving over a measured window. Not an aggregate visibility score, which can rise while nothing commercially useful changes.
Set the baseline before any spend: run your prompt set, record presence and citation per engine, and price every future report against that start line. Movement shows in 8 to 12 week windows, so a program judged at week three will always look like a failure.
Insist on seeing losses in the report too. A provider who only surfaces wins is curating, and curation is the cheapest thing to sell in this market.
And accept the ceiling honestly. Some prompts are answered from training data with no retrieval at all, and no monthly fee moves those. A provider who tells you which of your prompts those are is worth more than one who promises the full set.
Where Unveilr fits
Unveilr prices as a managed loop: the prompt set is scoped first, agents scan it across engines, losses feed into produced content, and re-scans verify each fix. The report is per-prompt presence and citation, so the spend is auditable against specific answers rather than a blended score.
That per-prompt accounting is the fastest way to know whether any AEO budget, ours included, is doing anything.
In one D2C case study, the brand moved from the 9th most-cited domain in its category to number 1, with ChatGPT visibility rising from 3.3% to 44.7%.