How much does it cost to run an AI citation tracking tool you build yourself?

About twenty cents per pass against a real query set, or about five cents for a smoke run with a handful of test queries. If you run it once a week, that is roughly a dollar a month in API spend. Productized versions of the same function typically range from $99 to $399 per month, depending on the feature bundle they include around the scan.

When does it make sense to build your own AI visibility tracker instead of buying a SaaS tool?

When the productized version sells a bundle and you only need one feature from it, the build version costs pennies and gives you the exact tool you want. When you need the whole bundle, or when nobody on your team can write the script, the productized version is the right call. The build version makes sense when twenty percent of the productized tool does eighty percent of the work for you.

What does an AI citation tracker actually measure?

It asks the same query to multiple AI engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overview) and records whether and where a specific brand or site shows up in the answer. It is the GEO equivalent of "what is my Google ranking" but it measures position in generative AI answers rather than search-engine result pages.

How long does it take to build a basic AI citation tracker?

A working version that queries five engines, parses the responses, and writes results to a local file takes about ninety minutes if you are comfortable with Node or Python and the major LLM SDKs. The hard part is not the code, it is deciding which queries actually represent your buyers' likely AI prompts.

The Stack·No. 02·June 15, 2026·5 min read

I built the AI visibility tool from the ad this morning. It cost twenty cents.

When a SaaS ad reads as a build prompt, the math changes. Today’s ad: a productized AI visibility tool at $399 a month. Today’s build: ninety minutes, twenty cents.

I saw an Instagram ad this morning for an SEO platform that runs agentic systems across ChatGPT, Perplexity, and Google’s AI Overviews. The pitch was sharp. Three brands per niche are locking in default recommendations across the AI engines, the window to move is now, here is the product that will get you there. Their flagship automation tool is $399 a month.

I grabbed a screenshot and sent it to Claude with one ask: build me a version of this.

Claude tried to talk me out of it, which was a reasonable response to the pitch I had pasted in. The ad was vague about what the tool actually does in concrete terms, the price was middle-of-the-market for SEO software, and I already have plenty going on. But somewhere in the middle of pushing back, I realized that the thing I actually wanted was not their product. I wanted the thing that asks the question I care about: when ChatGPT, Claude, Perplexity, Gemini, and Google AI Overview answer “who do I hire for AI Operations in Portland,” how often does kariops show up?

That is a much smaller question than the platform was pitching, and it is the exact question I want answered.

What I actually built

geo-scoreboard is a command-line tool that runs the same set of prospect-style queries against five AI engines and records whether kariops appears in the answer, where it appears in the citation chain, and what the rest of the citation list looks like. It runs from my terminal, writes its output to a local file, and costs about five cents per smoke run or twenty cents for a full pass with my real query set.

That is the whole thing.

There is no dashboard, no agent workflow, no marketing automation, and no “deploy this and watch your visibility grow.” I do not need any of that. What I need is a number I can look at once a week to see whether the thing I am writing and shipping is getting picked up.

What the productized version was selling

I am going to be careful here because I do not want to pick on a specific company, and I genuinely think there are operators for whom a $399 a month productized AI visibility tool is the right answer. If your operation does not have someone who can write a Node script that calls five APIs and parses a JSON response, the price gap between $0.20 and $399 closes fast. The build is the cost, and that is what they are selling you.

What the ad was actually selling was a bundle: the visibility scan, plus restructure-your-site recommendations, plus agentic workflows that “deploy your visibility automatically.” For my operation, two of those three are noise. I already write what I write, I already structure pages the way I structure them, and I do not want agents deploying anything on my site without me reading it first. The thing I actually wanted from their tool was item one of three. Twenty percent of what they sell does almost all of the work for me.

The moment the build changed shape

The conversation with Claude pivoted when I said “yes I hear you, but isn’t there a version of this that is actually what I need.” That sentence is the difference between building somebody else’s product and building your own.

Most operators I talk to are stuck on the first half of that sentence. They see a tool, they look at it, they decide whether the productized version fits, and if it does not they keep shopping. The version that pivots the conversation is the second half: but isn’t there a version that is actually what I need.

When the answer is no, you buy the productized tool. When the answer is yes, you build the version that fits your actual question, and the productized version becomes a footnote.

What it costs to run

About five cents per smoke pass (a handful of test queries against four engines, skipping Gemini because the free tier rate-limited me and I have not bothered to wire up the paid one). About twenty cents for a full pass against my real query set. If I run it weekly, that is one dollar a month.

The productized version was $399 a month.

I am not making a “SaaS is overpriced” argument here. The productized vendor has to pay engineers, support staff, and marketing, absorb the cost of customers who never open the tool, and stay a business. Their unit economics require pricing well above their per-customer API spend, and that is how SaaS works.

What I am noticing is that the gap between productized-price and roll-your-own-cost is wide enough now that any operator with a small amount of scripting comfort should at least look at the second column before signing up for the first. The build takes a few hours and the API costs are pennies. The version you build is the version that answers your question, not their version that answers a generalized question for a thousand customers.

What this is not

A few things this is not.

It is not a claim that productized AI tools are always the wrong call. They are usually the right call when your need exactly matches their template, or when nobody on your team can build the version that fits.
It is not a claim that I will not buy SaaS. I buy plenty of SaaS, including the writing tool I am drafting this in.
It is not a claim that the productized tool I saw advertised this morning is a bad product. I have not used it and have no opinion on it, and I am sure it is well-built for the audience it is built for.

What I am saying is that the AI Operations practice I run has reached a point where I see a SaaS ad on Instagram and my first instinct is not “should I subscribe” but “is there a version of this that I actually need, and if so, how long would it take me to build it.” The answer this morning was ninety minutes and twenty cents.

What I keep coming back to

Most people are selling SaaS features that no one asked for. The features sound good in the pitch and look good in the demo and then sit unused in a dashboard nobody opens. The pitch on the visibility tool this morning had three pillars and I wanted twenty percent of one of them.

The shift the AI Operations practice produced for me is the confidence to build the version I actually wanted, in an afternoon, for the cost of a coffee. The productized tool will keep selling, and good for them. I will keep building the versions that fit, because now I can.

Filed underGEOLLM citation sharebuild vs buyCLI toolsAI Operations

← All entries