Key Takeaways
- • Done-for-you retainers run $500 to $2,500 per month for a startup. Most funded seed-stage startups land at $1,000 to $1,800 per month.
- • DIY costs $0 to $150 per month. The foundation work is 4 to 6 hours and no software cost beyond a monitoring tool.
- • A one-time setup sprint is $750 to $2,000 and gets you 60 to 70% of the result without a recurring commitment. Best first move for pre-revenue startups.
- • Three things drive the price up: page count, content velocity, and competitive density in your category.
- • Pre-revenue startups should not sign a retainer yet. DIY the foundation, add a cheap monitoring tool, and buy the retainer once you can attribute leads.
- • At $1,200 per month, expect return by month four to six: roughly $5,000 to $40,000 in new revenue depending on your average contract value.
The Short Answer
AI search optimization for a startup costs somewhere between $0 and $2,500 per month. That range is wide because the work scales with how much of your product and market you need to make visible, not with your funding stage or logo.
Here is the honest version. If you have five pages and one product, you can do the foundation yourself in an afternoon for the price of a monitoring tool. If you have 40 pages, a competitive category, and no time, you are looking at a real retainer. Most startups sit between those two, and the number that keeps coming up is $1,000 to $1,800 per month for done-for-you work.
Nobody should quote you a price before they know your page count, your category, and how much content you need per month. Anyone who does is selling a package, not solving your problem. The rest of this post breaks down exactly what moves the number and where a startup should spend first. If you want the general (non-startup) breakdown, we cover what an AI search costs separately.
The Three Price Tiers
There are really three ways a startup buys this, and they map to three price points.
Tier 1: DIY foundation, $0 to $150 per month. You claim Foursquare, Bing Places, and Apple Maps, fix your NAP consistency, add LocalBusiness and Organization schema, and run baseline queries on ChatGPT, Perplexity, and Claude. The only recurring cost is a monitoring tool to watch your citation rate, which runs $0 on free tiers up to about $150 per month. This is the right tier for pre-revenue startups and technical founders. Full monitoring pricing is broken out in our guide on what AI search monitoring costs.
Tier 2: One-time setup sprint, $750 to $2,000. An agency or contractor does the entire foundation once, hands you a citation audit, and leaves you with a clean base. No recurring fee. This is the best value move for a startup that wants the foundation done right but is not ready to commit to monthly spend. You get 60 to 70% of the result and can add ongoing work later.
Tier 3: Monthly retainer, $500 to $2,500. Ongoing foundation maintenance plus content production plus per-platform reporting. The $500 to $900 floor is foundation-only. The $1,000 to $1,800 middle is where most funded startups live: maintenance plus two to four content pieces per month. The $1,800 to $2,500 ceiling adds content volume and deep authoritative-source outreach for competitive categories.
What Drives the Number Up
Three factors, in order of impact.
Page count. A startup with 5 pages costs a fraction of one with 40. Schema, content, and internal linking all scale per page. A 5-page startup can sit at the $500 to $900 floor. A 40-page product with location or use-case pages pushes toward $2,000 plus just to keep the foundation maintained.
Content velocity. This is the biggest lever. Two decision-stage articles per month keeps you near $1,200. Eight pieces per month roughly doubles the retainer to $2,400 plus, because comparison and decision-stage content is the thing that actually grows citation rate over time. The platforms cite you more when you have more citable, specific content.
Competitive density. A startup in a saturated category, fintech, legal tech, healthcare, fights entrenched incumbents with deep authoritative citations. Matching that adds $300 to $800 per month in outreach and production. A startup in an unsaturated category needs far less. This is the same reason a competitor shows up in ChatGPT and you do not: they built citation depth you have not matched yet.
What Drives the Number Down
The same levers work in reverse, and a startup has structural advantages here that an established business does not.
You start clean. Most cost overruns come from cleaning up inconsistent NAP data, duplicate directory listings, and conflicting schema built over years. A startup usually has none of that baggage. Your foundation is a build, not a repair, and builds are cheaper.
Fewer pages, tighter focus. A single-product startup with a clear category has a small surface to optimize. That keeps you at the floor of every range. You do not need 40 location pages optimized; you need your handful of pages done well.
DIY the foundation, buy only the depth. The cheapest real strategy is to do the $0 foundation yourself, then hire only for the ongoing content and citation depth that a founder does not have time to produce. That hybrid keeps a startup at $600 to $1,000 per month instead of $1,800, because you are not paying an agency for work you can do in an afternoon. AI search optimization and SEO overlap here, and knowing the difference saves money: our post on whether AI search optimization is different from SEO covers what you already own.
DIY vs Agency for Startups
Do it yourself if you are pre-revenue, technical, or have a founder with 4 to 6 hours to spend on the foundation. The software cost is $0 to $150 per month for monitoring. You will not get deep content production, but you will get cited for your core queries, and that is often enough to start.
Hire an agency if you have revenue, a clear inbound-lead motion, and no time to produce two to four content pieces per month. At $1,000 to $1,800 per month you get the foundation maintained and the content that grows citation rate. Choosing well matters more than the price; run the seven questions to ask any AI search agency before you sign.
Do neither yet if you cannot attribute a single lead to AI search and have no way to measure whether citations convert for your offer. Spend $0, DIY the foundation, watch your monitoring tool for 60 days, and buy the retainer once you have a signal. Signing a $1,500 per month contract before you can measure return is the most common startup mistake in this category.
The ROI Math for a Startup
Run the numbers before you spend. For a startup at $1,200 per month, citation-rate movement usually shows by month two and attributable inquiries by month four to six. A 30 to 50% citation rate across ChatGPT, Perplexity, Claude, and Google AI Overviews produces roughly 10 to 25 qualified inquiries per month for a focused single-product startup.
At a 25% inquiry-to-customer conversion and a $2,000 to $8,000 average contract value, that is $5,000 to $40,000 in new monthly revenue against $1,200 in spend. The spread is entirely about your average contract value. A B2B startup with a $6,000 ACV clears the retainer cost with two customers a month. A low-ticket consumer startup needs volume to justify the same spend, which is exactly why the pre-revenue rule exists: measure first, scale spend second.
The point is not that AI search is cheap. It is that the number is knowable, and it should be tied to a lead motion you can measure, not to a package price someone quoted you sight unseen.
Want a straight answer on what your specific startup should spend? Book a call and we will scope it against your page count, category, and lead motion. No package pricing, no contact-us-for-a-quote runaround. If you would rather see where you stand first, book a free citation audit and we will show you exactly where AI platforms cite you today.
About the author
Matthew Johnson is the founder of Pleiades Consultancy. He previously scaled his own marketing agency to multiple six figures before serving as CMO of an Amazon agency, where the client base tripled from 15 to 45 active clients during his tenure. He worked with some of the largest names in e-commerce, including Ridge Wallet, HexClad, BK Beauty, The Woobles, Walkize, Lonely Planet, and Obvi. He now works with local businesses to maximize their client acquisition and visibility through AI search with ChatGPT, Claude, Gemini, Perplexity, and Bing Copilot.