How much AI shopping optimization costs in Romania
The cost has three components, the one-time audit, ongoing monthly optimization and brand authority work, and moves first with catalogue size. Worth it above 100 active products and with at least one competitor already visible in AI.
AI shopping optimization in Romania has three cost components, a one-time audit, ongoing monthly optimization and brand authority work, and the total moves first with catalogue size. The threshold where it becomes worth it, over 100 active products and at least one competitor already visible in AI answers for your category. With a catalogue under 100 products and no competitors in AI, wait.
This is the only article in our AI shopping series that talks directly about money, what you actually pay for, what moves the cost by scenario, the cost of doing it in-house versus with an agency, the payback maths, and the threshold below which it does not make sense yet. The cost depends on the catalogue, the platform, and the current state of your data, so you find your own figure for your own case, not from a table.
What you actually pay for
Optimizing for AI shopping, also called AEO (answer engine optimization), is not a single product. It is three different things, with three different costs, and most stores only need the first two at the start.
The one-time audit. An assessment of how visible your catalogue is to AI assistants. It checks schema markup on products, feed data structure, verifiable brand authority, technical blockers (robots.txt, rendering) and a manual test of your categories in ChatGPT, Perplexity and Google AI Overviews. It is paid once and delivers a gap map and the order of fixes by economic impact.
Ongoing optimization. Implementing the fixes and holding visibility as your catalogue and competition shift, filling missing attributes, writing descriptions in citable language, schema on new products, FAQs in Q&A format, monthly monitoring of your position in AI answers. The monthly cost depends on how many products are touched monthly.
Brand authority work. The slowest and most expensive component. AI assistants recommend brands that appear consistently in credible external sources, reviews, comparisons, editorial mentions, category directory listings. This is PR and content work, not feed work. It sits on top of the technical optimization, is the most expensive component, and shows in results only after 3 to 6 months. Most stores defer this component until the data is fixed.
The cost rule for AI shopping, technical data is cheap and fast, brand authority is expensive and slow. Spend first on what you can control, only then on what you have to earn.
How the cost moves by catalogue scenario
The real cost moves first with the number of products, because every product has data to fill, schema to write and a description to rework, and the time to results moves with it. Three scenarios cover most Romanian stores.
| Scenario | Time to results |
|---|---|
| Small catalogue (under 100 products) | 1 to 2 months |
| Mid catalogue (100 to 1,000 products) | 2 to 4 months |
| Large catalogue (over 1,000 products) | 3 to 6 months |
For large catalogues, optimization does not touch every product monthly. The work runs on priority categories, in order of margin and search volume, while the rest of the catalogue benefits from structure changes applied once.
In-house versus agency, the real cost
The sticker price is not the real cost. The real cost of the in-house route is the time you pull from somewhere else plus the learning curve.
Do-it-yourself. Visible cost, zero. Real cost, your team's time. An AEO audit done in-house by someone learning as they go takes 30 to 50 hours, plus 5 to 10 hours monthly for upkeep. Multiply those hours by what an hour of that person's time is actually worth and you get the real cost, with no guarantee the schema is implemented correctly. For a founder whose hour is worth far more elsewhere, the in-house route is rarely the cheapest, only the least visible on the invoice.
With an agency. Visible cost equals real cost, the audit plus the monthly retainer. The advantage is not just time saved, but that the schema, data structure and multi-assistant testing are done once, correctly, by someone who has seen the pattern on other catalogues. The downside, you pay from month one, before you see movement in results.
The practical threshold, under 100 products, with someone in-house who genuinely has the time, the DIY route can make sense for the audit. Above that, or if nobody has 40 free hours, a paid external audit pays back fast from the time it frees.
The payback maths
The difference from classic Google Shopping is that a spot in the AI recommendation has no cost per click. The assistant hands the buyer a short list, and you are either on it or not. Let us put numbers to it, as a model you can rerun with your own data.
Assume a Romanian store with a category that brings 200 buyers per month from clear commercial intent. Assume that when a buyer asks an AI assistant what to buy, they get a short list of four stores. If you are on that list and the four split attention roughly evenly, you capture around 25%, about 50 buyers per month, with no cost per click.
Multiply those roughly 50 buyers by your own average order value and you get the monthly revenue from the AI channel, with zero click cost. Compared with the monthly cost of optimization for a mid catalogue, the payback is fast even if the assumptions are half optimistic, that is 25 buyers instead of 50.
The figures above are an illustrative assumption, not a promise. The number of stores on the AI list, your share and the order value vary by category. What matters is the mechanism, if you are not on the list, your share is zero, no matter how good the product is.
A signal the direction is real
Adobe Analytics reported that in the first quarter of 2026, traffic from AI sources to retail sites in the United States grew 393% year over year, and visitors arriving from AI assistants converted roughly 31% better than those from other sources. This is US market data, not Romanian, but it shows the direction, buyers who arrive via AI buy more often. Romania is a few quarters behind, which means the window to get on the list before it gets crowded is still open.
When it is NOT worth it yet
There are two situations where AI shopping optimization is premature spend, however fashionable the topic.
Very small catalogue, under a few dozen products. The fixed cost of the audit and the structure changes does not amortise over so few products. With a small catalogue, the effort is better spent on feed data for Google Shopping, where the result is measurable sooner.
No competitor visible in AI for your category. Run the test, ask ChatGPT, Perplexity and Google AI Overviews what to buy in your category. If the assistants recommend no one, not even competitors, it means AI does not yet treat that category as a buying one. Do not pay to be first in an empty room. Repeat the test every two to three months, and when competitors appear, that is the moment.
The difference between these two scenarios and the rest is the test. You do not decide on press headlines, you decide on what you see in the assistants for your concrete category.
The concrete action
Before you allocate budget, run the manual test described above for your three most important categories. Note whether competitors show up and who. If they do, move to a structured assessment. If not, set a reminder for three months and focus on the channels that convert today.
If the test shows competitors already recommended and you missing, the cheapest way to know exactly what keeps you off the list, before committing monthly budget, is the AI-Ready Catalog Audit. In five days it maps schema, data structure and brand authority on your catalogue, manually tests five categories and delivers the order of fixes by economic impact. It is the lowest-commitment step, you learn what optimization would actually cost in your case before committing to a retainer.
Frequently asked questions
How much does AEO cost in Romania?
AEO (answer engine optimization), meaning optimizing to appear in AI recommendations, has in Romania three cost components, a one-time audit, ongoing monthly optimization and, optionally, brand authority work. The amount depends first on the number of products in the catalogue, a small store pays toward the lower end, one with over 1,000 products toward the upper end. The cheapest starting point is a one-time audit, which shows the real cost in your case before any retainer.
Is it worth investing in AI shopping optimization?
It is worth it if you have over 100 active products and at least one competitor already visible in AI answers for your category. The quick test, ask ChatGPT, Perplexity and Google AI Overviews what to buy in your category and see whether competitors show up. If they do and you are missing, every buyer who gets the AI list without you is a sale lost with no click cost for the competitor. If nobody shows up, the category is not yet treated by AI as a buying one and you can wait.
What's the cost difference between doing it in-house and with an agency?
In-house, the visible cost is zero, but the real cost is time, an AEO audit done by someone learning as they go takes 30 to 50 hours, plus 5 to 10 hours of monthly upkeep, time you multiply by what an hour of that person is actually worth to see the real cost, with no guarantee the schema is correct. With an agency, you pay the audit plus the retainer, but the work is done once, correctly, by someone who has seen the pattern on other catalogues. Under 100 products and with real internal time, the DIY route can make sense for the audit. Above that threshold, a paid audit pays back from the time it frees.
How fast do I see results after paying for AI shopping optimization?
It depends on catalogue size and which component you optimize. Technical fixes (schema, data structure, citable descriptions) typically show in 1 to 4 months, faster for small catalogues. Brand authority, the slowest part, shows only after 3 to 6 months, because it depends on external mentions you have to earn over time. That is why we recommend spending first on the technical data, where you control the result, and only then on authority.
How do I calculate whether AI shopping optimization pays back for my store?
Use a simple model with your own data. Estimate how many buyers a category brings per month from commercial intent. Assume the AI assistant gives a short list of four stores and that, if you are on it, you capture around 25%. Multiply by the average order value to get the monthly revenue from the AI channel, which has no click cost. Compare it to the monthly cost of optimization. On a mid catalogue, if the estimated revenue clearly exceeds the retainer even at half the assumptions, the investment pays back. The figures are a working assumption, not a promise.
Is there a minimum budget below which AI shopping optimization doesn't make sense?
Yes. Below a catalogue of a few dozen products, the fixed cost of the audit and the structure changes does not amortise, and the money is better spent on feed data for Google Shopping, where the result comes sooner. The second threshold is not about budget but about the market, if no competitor shows up in AI answers for your category, do not pay to be first in an empty room. Repeat the test every two to three months and invest when competitors appear.
Every month you are not on the AI list is market share your competitor takes for free.
Before you commit monthly budget, the AI-Ready Catalog Audit tells you in five days exactly what keeps you out of the recommendations and what optimization would actually cost in your case. It is the cheapest way to know before you commit.
Adela Mincea
Fondatoare și strateg în marketing de performanță · DAFE Digital · Formator ANC
Adela is a founder and performance marketing strategist with 10+ years of paid media across Europe, the US and Asia. She founded DAFE Digital in 2023 after agency roles in London and Hong Kong, in-house work inside client organisations, and independent consulting across 27+ industries.
