Tools and comparisons
Open source AI visibility tools: what you get, and what you actually pay for
By Arnav Mukherjee, founder of TofuBofu · August 1, 2026
I was reading the alternatives listing for one of the big AI visibility tools last week, mostly to see who else had shown up in the category. Sixty-one entries, nearly all of them venture-backed dashboards with a monthly price. The most-liked alternative on the page was not a startup at all. It was an open-source project you run on your own machine.
That surprised me, and then it stopped surprising me. Measuring AI visibility is a well-defined engineering problem: send questions to engines, read the answers, count the mentions. There is no reason that has to be a subscription. What follows is a straight assessment of what self-hosting genuinely gives you, what it quietly costs, and where it stops. I sell a paid tool in this category, so read accordingly, but I would rather tell you the real trade than pretend the open-source option does not exist.
What actually exists
The main one worth knowing is Elmo, whose source is on GitHub under an MIT licence. It runs your prompts across the major answer engines, ChatGPT, Google AI Overviews, Perplexity, Gemini, Copilot and Grok, and records how often your brand appears, which competitors appear alongside it, and which sources the models cite. You deploy it yourself, typically with Docker, and point it at your own AI provider API keys. It positions itself openly as a free alternative to the paid trackers, and it is the most-liked alternative on the listing I was reading.
On capability, the measurement half is not a toy. Prompts across every major engine, mention counting, competitor co-occurrence, citation sources: that is the same feature list the paid monitors lead with. Anyone telling you the free option cannot measure properly is selling something.
The bill does not disappear, it changes shape
Here is the part worth being precise about, because "free" does a lot of work in this category. The software is free. The measurement is not, and it never can be. Every scan spends real money: each buyer question has to be sent to each engine, and each answer has to be read back. That cost lands on your own API keys instead of a vendor's, and it recurs every single time you check.
This has a consequence people discover late. Sampling is the difference between a real measurement and a guess, because these engines are non-deterministic and the same question returns different answers on different runs. Asking each question once is cheap and unreliable. Asking it several times and aggregating is reliable and multiplies your bill. When you self-host, that trade-off is yours to make on your own credit card, every month, and the quiet failure mode is under-sampling to save money and then trusting the number anyway.
Then there is the cost that never appears on any invoice: someone has to deploy it, keep it upgraded, and fix it when a provider changes an API or deprecates a model. That is not hypothetical. We had a model silently deprecated mid-scan once and every result from that engine quietly came back empty until we caught it. With a vendor, that is their 2am problem. Self-hosted, it is yours, and it fails silently rather than loudly.
The two things open source wins outright
First, verifiability, and this is a bigger deal than it sounds. Run the same brand through three different AI visibility tools and you will get three unrelated numbers, because each one picks different queries, samples them a different number of times, and counts a mention differently. Almost none of them publish the method. With an open-source tool you can read the code and confirm exactly how a metric is produced. In a category with a genuine measurement-trust problem, being able to audit the arithmetic is a real advantage, not a nerd preference.
Second, data ownership. Your prompts, your competitor lists and your results stay on infrastructure you control, and there is no vendor to lock you in or change terms. If you work under data-residency or compliance constraints, or you simply do not want your competitive research sitting in someone else's database, this is not a nice-to-have. It settles the decision.
Want the measurement without standing up infrastructure first?
A free scan runs your buyers' questions across all 6 engines and shows the real answers. Nothing to deploy, no card.
Run your free scanWhere every tracker stops, free or paid
Here is the thing that decides this for most people, and it is not about licences. A tracker measures. It does not fix. Nothing in a self-hosted scanner writes the comparison page you are missing, publishes it to your CMS, or checks that the schema you added actually went live. That is a fair scope decision, because writing and publishing need editorial judgment and integrations, not a scanner.
But it matters, because measurement is the cheap half of this problem. Knowing you are absent from a buying question changes nothing on its own. Something has to answer that question better than the competitor currently winning it, and then something has to prove the answer changed. If you self-host, you own the measuring and you still own all of that. That is completely workable if you have someone who writes. It is a trap if you were hoping the tool would do it.
How I would actually choose
1. You have engineers and want auditable numbers
Self-host. You get a method you can inspect, data that stays yours, and no per-seat pricing. Budget for the API spend properly and decide your sampling depth on purpose rather than by accident.
2. You have compliance or data-residency constraints
Self-host, and stop reading comparison posts. This is not a preference question, it is a requirement, and open source is the only option that satisfies it cleanly.
3. You are an agency tracking many clients
Look hard at self-hosting. Per-brand pricing across a client book adds up fast, and you probably have the technical capacity. The catch is that your clients are paying you for the fixing half, which you will still be doing yourself.
4. You are a founder without an engineer
Do not self-host. You would be trading a monthly bill for a maintenance job that lands on the person with the least time. Use a free tier to see where you stand, and pay for a tool only when you want the content and publishing half done for you.
5. You want the numbers AND the work done
No open-source tracker does the second part today, so this is a paid decision. Judge tools on whether they actually produce and publish content against the gaps, not on the length of their dashboard feature list.
The honest summary is that open source has made the measurement half of AI visibility a commodity, and that is good for buyers. It should push every paid tool in this category to justify itself on something other than counting mentions, which is exactly the pressure the category needed.
Frequently asked questions
Is there an open source AI visibility tool?
Yes. Elmo (elmohq.com, source on GitHub under an MIT licence) is a self-hosted platform that runs your prompts across the major answer engines, including ChatGPT, Google AI Overviews, Perplexity, Gemini, Copilot and Grok, and records how often your brand appears, which competitors appear alongside it, and which sources the models cite. It positions itself explicitly as a free alternative to Profound, Peec and Otterly. It is the most-liked Profound alternative on AlternativeTo at the time of writing.
Is open source AI visibility tracking actually free?
The software is free. The measurement is not. Every scan spends real money on AI provider API keys, because each buyer question has to be sent to each engine and the answer read back. You also pay for the infrastructure you run it on and, less visibly, for the engineering time to deploy it, keep it upgraded, and fix it when a provider changes an API. The honest framing is not free versus paid, it is paying in API keys and ops time instead of paying in subscription.
What is the real advantage of self-hosting?
Two things that matter and are hard to get otherwise. Verifiability: you can read the code and confirm exactly how a visibility metric is collected and calculated, which is a genuine answer to the problem that different tools report wildly different scores for the same brand. And data ownership: your prompts and results stay on your infrastructure, with no vendor lock-in. If either of those is a hard requirement, open source is not a compromise, it is the correct choice.
What do open source tools not do?
They measure. They do not fix. Nothing in a self-hosted tracker writes the comparison page you are missing, publishes it to your CMS, or verifies that the schema you added went live. That is deliberate and reasonable, because those jobs need editorial judgment and integrations rather than a scanner. The gap matters because measurement is the cheap half of AI visibility: knowing you are absent from a buying query changes nothing until something answers that query better than the competitor currently winning it.
Who should self-host instead of buying a tool?
Teams with real engineering capacity who want auditable numbers, teams with data-residency or compliance constraints that rule out sending prompts to a vendor, and agencies tracking many clients where per-seat or per-brand pricing gets expensive fast. If you are a founder without an engineer, self-hosting trades a monthly bill for a maintenance job you will end up doing yourself, which is usually a bad trade at small scale.
Is a self-hosted tool as accurate as a paid one?
Accuracy in this category comes from method, not from price: which queries you ask, how many times you sample each one, whether mentions are verified against the real answer text, and whether a failed API call is recorded as failed rather than as a zero. An open-source tool lets you inspect all of that, which is more transparency than most paid tools offer. What you have to supply yourself is the discipline to sample properly, because every extra sample is your own API spend.
Can I use both?
Yes, and for an engineering-heavy team it is a sensible split: self-host the measurement so the numbers are yours and auditable, and pay for the part that produces and publishes content against the gaps it finds. The two halves of AI visibility have genuinely different shapes, one is a scanner and the other is an editorial pipeline, and there is no rule that the same vendor has to do both.
Sources and further reading
- Elmo on GitHub: the source, licence and deployment instructions, so you can verify every claim above yourself
- elmohq.com: the project's own documentation of engine coverage and self-hosting requirements
- Your AI visibility score says 36%. ChatGPT never named you.: why methods differ so wildly between tools, which is the problem auditability solves