How Much Does AEO Cost?

AEO pricing depends on what's being bought. Visibility-tracking software starts around $50 per month. A one-time on-site foundation covering answer content, schema, and entity structure typically costs $7,000 to $15,000. Managed programs that also build off-site authority generally run $3,000 to $10,000 per month, and full-service engagements at established B2B agencies begin near $10,000 per month. Comparing prices without comparing scope produces misleading conclusions.

What we charge

Our pricing is public because we'd rather you self-qualify than sit through a discovery call to learn a number. Everything below is current on our programs and pricing page.

The two most common shapes are a Launchpad on its own, when you need the on-site foundation and have an internal team to run with it, or a Launchpad followed by ongoing Authority Build, which is where the compounding happens. A company running both is looking at $12,000 up front and $5,000 per month after that, so roughly $42,000 across the first six months.

What the rest of the market charges

HubSpot's guide for its partner agencies suggests $3,000 to $6,000 per month for ongoing AI search optimization, or $7,000 to $12,000 for a six-week sprint (HubSpot services packaging guide). At the premium end, Omniscient Digital publicly starts full-service B2B engagements at $10,000 per month (Omniscient research), which is a fair benchmark for a program with deep content production and digital PR attached.

Software sits in a different category entirely. HubSpot's own AEO product is $50 per month standalone and is included in Marketing Hub Pro and Enterprise (HubSpot AEO launch). Fifty dollars buys you the diagnosis, and the work that diagnosis points at, from the writing through the publisher relationships down to the WAF rule blocking OAI-SearchBot, is priced separately and is where nearly all of the money actually goes. Budget tooling and execution as two line items, because collapsing them into one is how people end up concluding that AEO is either free or wildly overpriced.

Why a quote is meaningless without scope

Two proposals at $6,000 per month can describe completely different amounts of work, so the number on the contract tells you almost nothing on its own. When we look at competing quotes with prospects, the variables that explain most of the spread are:

• Content volume and who writes it. Four expert-reviewed answers a month is a different cost base than twenty AI-drafted ones.
• Technical implementation. Some engagements hand you a schema recommendation, while others deploy it in your CMS and validate it against the live page.
• Off-site authority. Publisher outreach, expert placements, and review programs are the most labor-intensive line item and the most commonly excluded one.
• Monitoring and reporting. Whether the prompt panel runs 10 prompts or 200, and whether anyone acts on what it shows.
• Conversion work. Getting cited sits upstream of getting a lead, and only some programs include the landing pages and CRM plumbing that close the gap between the two.

Get every vendor to itemize those five variables before you compare a single number, and then ask who physically ships the fix once a dashboard flags a gap, because the answer to that one usually explains most of the spread between two quotes that looked identical on the cover page.

Returns from AEO show up in conversion quality before they show up in traffic volume. AI-referred visits are a small share of total sessions, and published data consistently shows them converting at unusually high rates, which makes deal size and close rate the variables that decide whether the program pays for itself. A defensible model tracks attributable AI pipeline, assisted conversions, qualified-visit rate, and share of high-intent answers against total program cost.

What the published numbers actually say

The benchmarks worth knowing come from three sources, and each carries a caveat that matters more than the headline figure.

Those are real numbers from credible operators, and none of them is a forecast for your company. Percentage gains that large usually mean the starting base was close to zero, and a brand with a large existing audience converts AI traffic differently than one nobody has heard of yet. A vendor who quotes HubSpot's 1,850% at you as a projection is doing sales, and you should treat it that way.

The inputs of an honest ROI calculation

The math is ordinary revenue math, and the hard part is that a chunk of the value happens where your analytics can't see it, since Ahrefs found AI assistants linked to a site in only about 28% of brand mentions (Ahrefs citation-versus-mention study). We model the visible part and treat the influenced-but-unlinked share as upside nobody gets to put in the spreadsheet.

Here's what goes into the calculation:

• Program cost. All of it, including internal SME hours, not just the retainer.
• AI-referred sessions, segmented by engine.
• Qualified-visit rate. What percentage of those sessions are ICP-fit, judged by firmographics rather than by time on page.
• Opportunity rate and close rate on AI-sourced contacts, compared against your other channels.
• Average contract value and gross margin.
• Ramp period. Citations typically move before pipeline does, so an ROI calculation run at day 60 will look terrible whether or not the program is working.

Run it as a break-even question. If your first six months cost $42,000 and your average contract value is $25,000, you need roughly 1.7 closed deals to cover the program on first-year revenue. At a 20% close rate on qualified opportunities, that's about 8 or 9 AI-influenced opportunities across six months, a little over one a month, which is a number you can hold up against your own funnel and your own sales cycle and get a straight answer about whether it's plausible.

What determines whether the return shows up

The economics work best when a single deal pays for a meaningful chunk of the program, which is why we work with Seed through Series C B2B SaaS companies rather than low-ACV or transactional businesses. A high contract value on a genuinely considered purchase makes the break-even math forgiving, since one or two closed deals can cover a year of program cost. Small deal sizes force you to win a volume of AI-referred traffic that the channel doesn't currently produce at that scale.

Two other conditions matter as much as spend. Buyers in your category have to be using AI to research the purchase, since a citation only earns anything if somebody in the buying group is reading the answer it sits inside. You also need conversion paths that already work, because an engine can put you on the shortlist while your site still has to turn that visit into a conversation.

We'll be straight about our own proof: the results we publish, including 740% organic growth for Qualio and 300% more organic leads for The Predictive Index, came from full-category execution across web, content, and SEO rather than isolated AEO experiments. They demonstrate that we can move the number, though they aren't clean AEO causality and we won't present them that way.

Qualified leads from AEO generally lag visibility by a quarter or more. Citation and recommendation activity commonly appears within 60 to 90 days of a completed foundation, while pipeline effects usually surface between three and six months as coverage compounds across engines. Companies with existing brand recognition, an active content operation, and working conversion paths tend to see leads sooner, sometimes within weeks of launching.

The sequence that has to happen before a lead exists

A qualified lead is the last step in a chain, and each link has to be in place before the next one can move. Tracking them in order is how we tell a program that's working slowly from one that's stuck, because a stall at step two looks identical to a stall at step five if all you're watching is form fills.

Steps one through four are where the program earns its keep early, and they're visible long before revenue is. Single Grain's published windows stop at that visibility layer, reporting initial citation improvements at two to three months and more substantial visibility at four to six (Single Grain AEO services). Pipeline sits one stage past where that guidance ends, which is why we add roughly a quarter on top of it whenever a client asks us to forecast leads rather than citations.

Why some brands get leads in weeks

Fast results are real, and they belong to companies that had most of the chain already built. Sandler generated 8,000 new visitors, 4,000 ICP-fit prospects, and 12 new account conversions within weeks of launching its AEO work, with those visitors converting 2.7 percentage points better than typical marketing traffic (HubSpot Sandler case study). The setup explains the speed, because Sandler walked in with an established content operation, category demand that already existed, and a sales team standing by to work whatever came through, which meant their AEO work was giving existing demand a path into the pipeline instead of manufacturing the demand first.

A company with no citations, few reviews, and a category buyers aren't yet asking AI about is starting several steps further back and should plan its budget accordingly. In our experience the honest way to hold that is to treat the first 90 days as the work of getting into the consideration set at all, then put the lead forecast in the quarter after it. The engagements that go badly are almost always the ones where somebody promised a board that both would happen at once.

What moves your timeline in either direction

The two ends of the chain are where your timeline is actually decided, since the middle takes about as long for everybody. At the front, an indexable site and existing domain authority clear steps one and two quickly, and that's largely settled before we ever start. At the back, the difference between a 90-day lead and a 180-day one is usually conversion and sales: whether your demo request and pricing pages hold up when an AI-referred visitor arrives already half-decided, and whether somebody follows up on the resulting inquiry in hours instead of days.

Delays cluster in a few predictable places, and most of them are internal. A review cycle that adds two weeks to every publishing sprint pushes every stage of the table back by the same two weeks. Thin customer proof means we're assembling the evidence base through off-site corroboration, which accumulates over months if it's going to be authentic. And in categories where buyers aren't yet asking AI about the problem, the citations land on schedule and simply sit there until the audience catches up.

Our public position is deliberately conservative: measurable citation and recommendation activity within 60 to 90 days of completing setup, compounding over three to six months, with lagging visibility unless demand and conversion paths already exist. If you want to see what the sequence looks like in practice, we built a live demonstration of the mechanics at howaeoworks.com, and our own AEO service page lays out which pieces get built in which phase.