The best AI visibility tools for developer tools in 2026
A buyer's guide to GEO and AEO platforms, what each one measures, what they cost, and the one surface none of them cover if you sell developer tools.
There are now more than two hundred products calling themselves AI visibility, GEO or AEO tools. Most of them do the same thing: send prompts to chat models on a schedule, count brand mentions, and draw a chart.
This is a guide to the ones worth considering, what separates them, and the surface none of them measure.
What all of these tools do
An AI visibility tool sends a fixed set of prompts to AI assistants on a schedule, reads the replies, and reports how often your brand appears, in what position, with what sentiment, and which of your pages were cited. That is the product category, and the differences between vendors are in coverage, data quality and what you can do with the result.
The shortlist
| Tool | Position | Entry price | Best for |
|---|---|---|---|
| Profound | Enterprise AEO platform, real user session data, action workflows | Enterprise | Large brands with a dedicated AI search function |
| Scrunch AI | Full AEO and GEO suite: monitoring, auditing, optimisation, AI content delivery | From $250 per month | Teams that want monitoring and action in one place |
| Peec AI | Focused tracker: visibility, position, sentiment across about five engines, daily, with screenshots | From about EUR 89 per month | Mid-market teams that want good analytics without enterprise pricing |
| Otterly | Entry level monitoring | From about $29 per month | A solo operator learning what the data looks like |
| Ahrefs Brand Radar | AI visibility inside an existing SEO suite | Suite price | Teams already paying for Ahrefs |
| Semrush AI Toolkit | Same idea inside Semrush | Suite price | Teams already paying for Semrush |
| Evertune, Brandlight, Athena, Rankscale, Knowatoa, Trakkr | The long tail of dashboards | $50 to $500 per month | Specific niches and regions |
How to choose among them
Three questions settle it.
How many engines do you need, and does synthetic testing bother you? Most tools work by sending prompts. Profound also captures front-end data from real sessions, which is a better signal and costs more. If you are making decisions worth six figures on this data, pay for the better signal.
Do you want monitoring or action? Peec and Otterly report a number. Scrunch and Profound are built to drive changes. A dashboard nobody acts on is a subscription, not a strategy.
Are you already paying for a suite? If Ahrefs or Semrush is already in the budget, their AI modules are the cheapest way to start, and they are competent. They were built for content sites, so the fit for a technical product is looser.
The surface none of them measure
Every tool in that table shares one design assumption: a person types a question into a chat window and reads an answer.
For a developer tool, there is a second surface where nobody reads anything.
A developer opens a repository in Claude Code and types one line. The agent lists the files, opens the package manifest, reads the lock file, decides what the request needs, sometimes searches the web with its own queries, picks a product, installs it, and writes the integration. Then it shows a diff.
No answer was read. No citation was clicked. A product was bought.
We measured that surface across 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic repositories. It behaves differently from the chat surface in ways that make a visibility score a poor proxy for it.
The repository decides more than the content does. The same hosted database request on JavaScript codebases: one product won 111 times out of 111. On TypeScript codebases: 48 out of 132. Same words, different file extension.
What is installed beats what is written. A sentence in a repository file naming a preferred email product was followed 16 times out of 18. Install a rival library into the same repository and the sentence loses two times out of three.
The agents disagree with each other. Claude Code, Codex and Cursor picked different category leaders in 9 of the 18 categories.
Some agents barely search. On decision tasks, Claude Code ran a web search in 1.6% of runs. Codex ran one in 53%.
That last number is the killer for the visibility model. If most of your users run Claude Code, the web content these tools measure the effect of is absent from 98% of the decisions.
The number that matters instead
Install share is the percentage of sessions, in a category, where a coding agent picked your product and wrote working integration code. It is measured by running real agents on real tasks inside realistic repositories, with enough replicates to see past noise.
Here is what it looks like measured properly:
| Category | Leader | Install share |
|---|---|---|
| Payments | Stripe | 88% |
| Databases | Neon | 66% |
| Cloud | AWS | 62% |
| Bot protection | Cloudflare Turnstile | 57% |
| Product analytics | PostHog | 53% |
| Observability | Sentry | 37% |
| Resend | 36% | |
| Authentication | WorkOS AuthKit | 26% |
| Agent frameworks | Vercel AI SDK | 14% |
A payments company at 88% and an agent framework leader at 14% face completely different problems. No mention count distinguishes them.
A buying order that works
- Start cheap on the chat surface. Peec AI or Otterly. An hour to set up, low monthly cost, and you learn what the data looks like.
- Run your own install share test before buying anything larger. Three repositories that look like your users' projects, ten symptom-shaped requests, five runs each with Claude Code and Codex. That is 300 runs and a weekend. Read the sessions you lost.
- Then decide. If the losses are in the chat window, upgrade the visibility tool. If the losses are in the repository, no visibility tool will fix them and you need a different measurement.
- Fix the documentation either way. It is the shared input to both surfaces and it is the cheapest work on this list.
Being fair about the trade-off
Prompt monitoring is cheap because it is API calls. That is a genuine advantage: it can run continuously across many engines for a low monthly price, and for the surface it measures it is the right tool.
Running real coding agents is expensive. Each session is minutes of real agent time and real tokens, and you need hundreds per category because the same agent on the same repository disagrees with itself about a quarter of the time. That is why nobody had this data until recently.
The right answer for most developer tool companies is a cheap visibility tracker for the chat surface, plus a real measurement of the repository surface, because that is where the installs are.
Common questions
What is the best AI visibility tool?
It depends on scale. Profound and Scrunch suit large teams with a dedicated AI search function. Peec AI suits mid-market teams that want good analytics without enterprise pricing. Otterly suits a solo operator testing the water. Ahrefs Brand Radar and the Semrush AI Toolkit make sense if you already pay for those suites.
How much do AI visibility tools cost?
Roughly $29 per month at the entry level, around EUR 89 per month for a credible mid-market tracker, from $250 per month for a full AEO suite, and enterprise pricing for the top tier. Suite add-ons are included in the suite price.
Do AI visibility tools work for developer tools?
They work for the part of the market where a developer asks a chat assistant about your category. They do not measure coding agents installing software inside a repository, which for many developer tools is now the larger surface.
What should a developer tool measure that these tools do not?
Install share: the percentage of sessions where a coding agent picked your product and wrote working integration code. It requires running real agents on real tasks rather than sending prompts to chat models.
Can I test my AI visibility for free?
Partly. You can ask the major assistants your category questions yourself and record the answers, which approximates the entry tier. What you cannot do cheaply is run hundreds of coding agent sessions, because each one costs real tokens and real time.
Where this comes from
Armature ran 5,292 judged sessions with Claude Code, Codex and Cursor inside 51 realistic codebases, and published every run. The numbers on this page come from that work.