You want to know if ChatGPT and Perplexity are recommending your product. Fair question. A growing set of free tools promises a quick answer. Many of them will give you a number.
The number is not useless. But for a developer tool company trying to actually move that number, it is not enough.
This guide goes through five free or free-entry AI visibility checkers I reviewed for this draft, what their public pages say they return, and where that still falls short for dev tool companies specifically. If you're at the stage where you just want a rough sense of where you stand, the free tools are fine. If you're trying to understand why you're not showing up for "best auth library for Next.js" and what to do about it, you need more than a score.
What an AI search visibility checker should actually tell you
Before comparing tools, it's worth being precise about what useful output looks like. LLM visibility has three dimensions that matter: whether you're mentioned at all, whether what's said about you is accurate, and how you're framed relative to competitors.
A good checker addresses at least the first two. The best ones get to the third. Here's what I look for:
Platform breakdown, not a composite score. ChatGPT, Perplexity, Grok, Google AI Mode, and Gemini Search can behave differently for the same query. A tool can be invisible on Perplexity while getting cited regularly on ChatGPT. A composite "visibility score of 42" obscures that difference. If your developer audience skews toward one platform for research queries, a composite score can be misleading.
The actual response text. Did the AI mention you? What did it say? Is the product description accurate? There's a real difference between "AuthTool is a flexible auth library" and "AuthTool only supports Python." Both are mentions. One helps you. One doesn't.
Competitive context. If you're mentioned in 30% of relevant responses but Clerk is mentioned in 70%, your 30% means something very different than if Clerk is at 35%. Absolute numbers without competitive framing are hard to act on.
Query coverage. A single branded query ("does [your tool] show up in AI?") answers almost nothing about actual discovery. Developers don't ask "is Neon mentioned by AI?" They ask "what's the best serverless Postgres database?" The queries that matter are category queries, use-case queries, and comparison queries.
Free tools tend to handle the first of these better than the deeper diagnostic work, although the better ones now expose prompts, citations, competitor context, or response text.
Five free AI visibility checkers worth sanity-checking
I reviewed the public pages for each of these in May 2026. Product behavior, free limits, and platform coverage can change quickly, so treat this as a snapshot rather than a permanent feature matrix.
Semrush AI Search Visibility Checker
Semrush's free AI Search Visibility Checker is stronger than a simple score widget. The page says it checks visibility across ChatGPT, Google AI Overviews, and Gemini, and that the report includes an AI Visibility Score, mentions, platform coverage, sources, top industry sources, competitor visibility comparison, prompts, search volume, LLM responses, and opportunities.
The limitation is less about missing data fields and more about control. For a B2B SaaS brand that just wants to know "are we in AI search?", Semrush's report may be enough. For a dev tool company trying to understand how ChatGPT is framing its observability SDK versus Datadog and Sentry, the useful question is narrower: can you define and maintain the exact developer prompts that map to your category, frameworks, SDKs, and competitor set?
Ubersuggest (Neil Patel)
Ubersuggest's AI Search Visibility page describes a project-based report where you enter a website URL, brand name, language, location, and topic or main keyword. It says Ubersuggest runs multiple AI prompts for your keywords and competitors, aggregates brand mentions into visibility share, and reports brand visibility, industry rank, analyzed responses, competitor visibility, top prompts, average rank, mentioned brands, and response details.
That makes it more structured than a single instant score, especially because the setup flow lets you refine the topic or keyword before generating the report. The practical caveat is plan scope: Ubersuggest says free and paid tiers have different project, prompt, and refresh limits, so dev tool teams should check whether the available prompt budget is enough for their SDK, framework, and competitor coverage.
amivisibleonai.com
This one does something different from the broader monitoring platforms. The homepage positions it as a free website AI analysis tool and says it checks whether ChatGPT, Claude, and Perplexity can find your website. Its How It Works page says the scan covers 16 factors across four categories: AI crawler access, content structure, technical infrastructure, and structured data.
The approach is more of an AI-readiness and discoverability scan than a full visibility-monitoring product. The checks it describes are useful because crawler access, server-rendered content, schema, sitemaps, and llms.txt can affect whether AI systems can parse and cite your site at all.
The tradeoff is scope. Based on the public pages, the tool is strongest for checking whether AI systems can access and parse your site. It does not present itself as a competitor benchmark, custom prompt tracker, or response-level diagnostic.
Ahrefs AI Visibility Checker
Ahrefs' free AI Visibility Checker is one of the more transparent free options. The page says it checks ChatGPT, Gemini, Perplexity, Microsoft Copilot, Google AI Overviews, and Google AI Mode with search-backed prompts, then reports total AI mentions, platform breakdown, top topics, cited domains, and cited pages. Ahrefs also says the free tool requires no signup, returns results in seconds, and gives a limited preview.
What it doesn't necessarily solve is custom query control at the free-checker level. Ahrefs says its full Brand Radar product supports custom prompts, while the free checker is built around search-backed prompts and limited preview sections. If you're a CI/CD tool, you want to know whether you show up for "best CI/CD pipeline for a monorepo". That query may or may not be included in the free preview.
For a free tool, that transparency is useful. You can see what was asked. You can evaluate whether those questions are actually how your buyers search.
Indexly
Indexly is positioned as a combined SEO and AI visibility platform. Its public site says it monitors visibility score, sentiment, share of voice, prompt-level mentions, citation gaps, AI readiness, AI traffic, and AI crawler activity across platforms including ChatGPT, Claude, Gemini, Grok, and Perplexity.
What Indexly gets right is framing AI visibility alongside traditional SEO, indexing, content generation, and traffic analytics. The caveat for dev tool teams is the same one to check everywhere else: whether the monitored prompts map to technical buyer behavior, whether you can inspect the actual responses, and whether competitor comparisons run against the same query set.
Why these tools aren't enough for dev tool companies
I keep seeing dev tool teams run one of these free checks, get a score, and conclude either "we're fine" or "we're invisible." Both conclusions can be misleading, because free checkers are rarely designed around the full shape of developer-tool discovery.
They may not cover enough category depth. The better free tools now expose prompts or topic areas, and some paid products support custom prompts. But developer discovery is unusually long-tail. Buyers ask "what should I use for job queues in a Python FastAPI service?" or "best Postgres-compatible database for Vercel." A limited free preview may not test enough of those stack-specific prompts.
SDK- and integration-level tracking is easy to collapse into one score. A dev tool company with a JavaScript SDK, a Python SDK, and integrations for six frameworks has dozens of distinct query surfaces where it can either appear or not. "Best auth for Next.js" and "best auth for Remix" are different query surfaces with potentially different answers. A single free-checker score can hide those differences.
Response text matters more than the score. The most actionable finding from an AI visibility audit is often that the LLM is describing your product inaccurately. I've seen tools get recommendations with two-year-old pricing, deprecated SDK mentions, and wrong framework compatibility. If a report gives you the response text, read it. If it gives you only a score or limited preview, the score might be hiding the fact that when ChatGPT mentions your database tool, it describes it as "not yet production-ready", a description that was accurate in 2023 but hasn't been true since.
Competitor framing only helps when the query set is right. Several tools now include competitor comparisons. That is useful, but only if the competitors are measured against the same prompts that matter to your category. If you're an observability tool and Sentry is appearing in 80% of relevant recommendations while you're appearing in 20%, your "AI visibility score" means something different depending on whether those prompts are broad monitoring queries or specific SDK/framework use cases.
The query templates may not match developer discovery. Many prompt sets appear to reflect a marketing-team mental model: "does our brand appear when people ask about our category?" That maps poorly to developer discovery, where the queries are technical, stack-specific, and often phrased as problems rather than category searches.
For the underlying mechanics of why this matters for generative engine optimization specifically, see that post. For how other teams are building systematic tracking, AI Search Visibility Tools covers that ground.
What to look for if you actually want to fix your visibility
If you're past the vibe check stage, the free tools are a starting point, not a destination. Here's what more structured tracking looks like.
Define your query set first, not after. Before picking any tool, spend an hour listing 30-40 prompts a developer in your category would actually ask. Category + framework queries ("best auth for Next.js App Router"), comparison queries ("Clerk vs Auth0 for B2B SaaS"), problem-first queries ("how do I handle multi-tenant auth?"). The quality of your query set determines the quality of everything downstream.
Track platforms separately. ChatGPT, Perplexity, Grok, Google AI Mode, and Gemini Search can produce different outputs because they rely on different product experiences, retrieval systems, and source mixes. Platform-level breakdown is necessary for figuring out where to prioritize GEO work for developer tools.
Read the responses, not just the scores. For your top 10 category queries, read what each AI platform actually says about your product. Is the description accurate? Does it reflect your current SDK support? Does it describe your pricing correctly? The answers are more actionable than any composite score.
Track competitors in the same responses. If you're running 30 category prompts, capture which competitors appear in those same responses, how often they are cited, and how they are framed relative to you. You do not always need to rerun the prompt separately "against" each competitor; the important signal is the competitive set that shows up when buyers ask the category question.
Measure daily, interpret weekly. AI model versions update, competitors publish content, and citation patterns shift. Daily measurement gives you enough signal to catch movement in mentions, cited sources, and competitor pages. A weekly review cadence gives you enough distance to interpret those signals without overreacting to one noisy run.
Free checkers answer "are we mentioned?", which is the beginning of the question. The harder questions (in what context, with what accuracy, compared to whom, for which queries) require more structured tracking.
An honest note on DevTune
DevTune is our product, so take this with appropriate skepticism.
We built it specifically for developer tool companies because we kept seeing the same gap: dev tool teams running generic AI visibility checks, getting a score, and having no idea what to do with it. Many free or entry-level tools are optimized for broad brand visibility, and their prompt sets, citation source weighting, and metrics can reflect that.
What DevTune does differently: it tracks AI search visibility for developer-tool queries across ChatGPT, Perplexity, Grok, Google AI Mode, and Gemini Search, with plan-based access to the full five-platform set. Inside AI search tracking, it surfaces presence rate, share of voice, brand mention rate, average citation position, source-type presence rates, citation counts, competitor citations, source classification, prompt-level results, and the actual response text.
It is also broader than a visibility checker. DevTune connects AI search citations with competitor influence, cited-source analysis, content gaps, AI referral and bot traffic, developer community signals, package downloads, GitHub stars, a unified timeline, prioritized actions, content blueprints, alerts, and API/MCP access for agent workflows.
Who it's for: dev tool companies (auth libraries, databases, observability tools, deployment platforms, API services) that are past the "do we have a visibility problem?" stage and trying to do something about it.
Who it's not for: consumer SaaS, e-commerce, or any company whose primary AI visibility concern is brand mentions in broad category queries. While DevTune can absolutely support those companies, its feature set and workflows are optimized for companies serving developers or other technical audiences.
If you want to try it, DevTune offers a 7-day trial with no credit card required. But if you want to understand what ChatGPT is actually saying about your SDK, whether it's accurate, and how you compare against the tools developers would pick instead of you, that's what the platform is designed for.
FAQ
What is an AI search visibility checker?
A tool that measures how often your brand appears in AI-generated responses from platforms like ChatGPT, Perplexity, and Google AI Mode. Most free tools return a score based on branded queries. More advanced tools track category queries, measure response accuracy, and compare against specific competitors. For a fuller explanation, see What is LLM Visibility?.
Are the free AI visibility checkers accurate?
Accurate as far as they go, but the scope is limited. Many free-tool scores reflect small or auto-generated query sets, often weighted toward branded prompts rather than the category and use-case queries that drive product discovery. Treat them as a rough baseline, not a diagnostic.
Which AI platforms should I be tracking?
For AI search visibility, a defensible starting set is ChatGPT, Perplexity, Grok, Google AI Mode, and Gemini Search. Keep search-style visibility tracking separate from coding-assistant evaluation: one tells you whether you show up in answer and recommendation surfaces, while the other tests how well assistants explain, compare, and implement your product.
How is AI search visibility different from traditional SEO visibility?
Traditional SEO gives you ranked positions: 3rd or 14th for a query, and that position can be tied to expected traffic. AI search visibility is closer to answer inclusion. When a developer asks an AI assistant for a recommendation, the model synthesizes a direct answer. Your brand is either included in that answer, cited as a source, or absent from the response. For a detailed breakdown of how the two relate, see AEO vs GEO vs SEO.
I got a low score. What do I actually fix?
Start by running 10-15 category queries manually across various platforms, not branded queries but the questions your buyers would actually ask. Read the full responses. Note what competitors are mentioned, what's said about them, and whether your product appears at all. One common finding is that AI models describe products from outdated docs or stale GitHub READMEs. Fixing those is a high-leverage starting point. See GEO for Developer Tools for a full playbook.
Does a good free check mean I don't need ongoing monitoring?
No. A point-in-time check is a snapshot. AI model versions update, competitors publish content, and your docs change. Citation patterns shift. Monitoring is what makes the data actionable.
DevTune tracks AI search visibility for developer tool companies across ChatGPT, Perplexity, Grok, Google AI Mode, and Gemini Search. Start your free trial - no CC required.