You want to know if ChatGPT and Perplexity are recommending your product. Fair question. A growing set of free tools promises a quick answer, and most will give you a number.
The number isn't useless. But for a developer tool company trying to actually move that number, it isn't enough.
This guide covers three things. First, five free AI visibility checkers, what they return, and where they fall short for dev tool companies specifically. Second, the four surfaces (docs, GitHub, community, third-party content) that actually shape AI citations for technical queries, and the paid tools built around them. Third, a four-week experiment any dev tool team can run to move from "checking a score" to "changing the score."
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, keep reading.
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, Microsoft Bing Copilot, 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 doesn't 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 major AI search platforms.
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's 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.
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, Microsoft Bing Copilot, 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're cited, and how they're framed relative to you. You don't 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.
The four surfaces that actually move AI visibility for dev tools
A visibility check tells you where you stand. It doesn't tell you what to fix. For developer tool companies, four surfaces tend to matter more than the generic "improve your domain authority" advice you'll find in most AI visibility guides.
Your docs site
Documentation is often the highest-leverage surface for developer tool AI visibility. Your docs are the primary place to make the product description, integration paths, and tradeoffs explicit.
Structure matters more than volume. Pages that open with a vague "Let's get started!" give AI systems very little to anchor on. Pages that open with a one-sentence summary of what the feature is, who it's for, and what problem it solves are easier to cite and summarize accurately.
A few specific improvements worth making:
Write a clear "What is [Product]?" section on every major feature page. Not assumed. Not buried in the introduction. Explicit. When an AI system needs to describe your product in a category query response, these summaries give it cleaner source material.
Write comparison pages for your top three competitor alternatives. These are strong candidates for citation in comparison queries because they directly match how developers phrase their questions. Honest tradeoffs beat one-sided positioning.
Build integration guides for every major framework you support, and make them standalone. "Using [Your Tool] with FastAPI" shouldn't require reading five other pages to make sense. A working quickstart, common gotchas, a realistic example. These integration-specific guides are exactly the kind of source material worth checking in Perplexity for stack-specific queries.
One low-cost experiment worth considering: an llms.txt file at the root of your docs site. The spec is still being formalized, and there isn't yet strong evidence that major AI systems consistently use it to decide what to cite. The practical reason to add one is simpler: it's cheap to maintain, it forces you to name the pages that matter most, and it may become useful if crawler support improves.
GitHub
Your GitHub repository is an important AI discovery surface. The README especially.
Most READMEs are written for people who have already decided to look at the project. They assume context. AI systems can use the README to understand what a tool is, who it's for, and how it compares to alternatives. Those three things need to be in the first screen.
The first 200 words of your README should tell a developer who hasn't heard of you: what category this is in, what specific problem it solves, and what makes it different from the obvious alternatives. Not the company backstory. Not a feature list. The job it does.
Include a minimal, working code example. Something that goes from npm install or pip install to "you got the thing working" in under ten lines. These snippets give AI systems cleaner material to cite, paraphrase, or turn into implementation guidance.
GitHub topics and tags are indexed and help establish category placement. If you're a workflow orchestration tool, make sure the repository is tagged with the right category terms.
Stack Overflow and developer communities
You can't manufacture this, and you shouldn't try. Stack Overflow's community is fast to remove promotional content, and Reddit dev communities are even faster. But genuine technical presence in these spaces is worth taking seriously for AI visibility.
Your team should be answering questions in your category, including questions that don't mention your product. An auth tool company should have team members answering "how do I handle JWT refresh tokens in Express?" even when the question doesn't specify a library. Building the association between your team and the problem space earns you the right to mention your tool when it's genuinely the right answer.
A 2025 study on generative engine optimization found that AI search results exhibit a systematic bias toward earned media (third-party, authoritative sources) over brand-owned and social content, compared with Google's more balanced mix. The study classifies community-driven sources like Stack Overflow, Reddit, and Hacker News separately from earned sources, so treat their exact weighting for developer queries as a hypothesis to test rather than a settled result. What is defensible is that consistent, technically honest team presence in these communities builds signal that both humans and AI systems can pick up on.
When your team shows up in a Hacker News or subreddit thread comparing tools in your category and gives a technically honest comparison, including where your tool is weaker, that thread can become source material for similar AI answers.
Third-party technical content
After your docs and GitHub, third-party coverage of your product can be one of the highest-value surfaces for AI citation. This falls into two buckets.
The first is community-authored content: "I switched from X to Y and here's what I learned" blog posts, tutorials developers publish about integrating your tool with their stack, Reddit posts comparing their experience with multiple options. You can't write this for your users, but you can create conditions for it: excellent docs, a community where users share what they built, and low friction for developers who want to write about their experience.
The second is more deliberately acquirable: placement in comparison roundups and "alternatives to X" articles on developer-trusted publications. A "building auth with [Your Tool] in Next.js" tutorial on a high-credibility developer publication may do more AI citation work than the same tutorial on your own blog.
Baseline technical SEO still matters. Schema markup (Article, FAQPage, HowTo, Product), clean structured HTML, and machine-scannable content are prerequisites, not accelerants. What we would push back on is the idea that generic backlink building and content-farm distribution move the needle for developer queries. The sources AI systems use for developer tool questions aren't reducible to domain authority scores. Get the baselines right, then invest the bulk of your attention in the four surfaces above.
The paid AI visibility tools worth knowing about
Once free checkers hit their ceiling, the paid tool market is where dev tool teams tend to look. This space changes quarterly. Pricing, feature sets, and LLM coverage at every vendor listed here will likely shift. Treat this as a starting framework, not a definitive comparison.
Profound (enterprise pricing, starting ~$2,000/month as of early 2026) is the most feature-complete tool in the category: 8+ LLMs, thorough competitor tracking, serious infrastructure. For a large company with a real AI marketing budget, it's defensible. The limitation for dev tool teams is that it's built for enterprise brand and category queries, not technical developer discovery. You're paying for breadth and reliability, not dev-tool-specific intelligence.
Otterly (starting at ~$29/month as of early 2026) is the entry-level option that's actually usable for small teams. It covers core platforms, doesn't require a sales call, and the trade-offs are predictable: shallower LLM coverage, less granular competitor tracking, and a lighter analysis layer. Fine if you just want to know whether you show up. Not built for dev tools in any specific way.
Peec AI (starting at ~€89/month as of early 2026) sits in the middle and has better agency-oriented reporting than either extreme. It's solid for a team that needs to report AI visibility metrics to stakeholders regularly. Like the others, it's not calibrated for the kinds of technical, framework-specific queries that dominate developer tool discovery.
LLMClicks and Scrunch are both worth evaluating for specific use cases. LLMClicks focuses on attribution, connecting AI mentions to actual traffic, which is useful once you established baseline visibility. Scrunch has strong content optimization features for GEO. Neither is developer-tool-specific.
SE Ranking added AI Overviews and AI visibility tracking to its existing SEO platform, which makes it appealing if you're already in their ecosystem. The AI visibility features are younger than its core SEO tooling, but the integration is useful if Google AI Overviews is a significant channel for you.
Pricing and feature details for all vendors were verified in early March 2026. This space changes quickly, so check vendor websites directly for current information.
Most of these tools were built for generic marketing teams, not developer GTM. If your prompts are technical and your citation sources are GitHub and Stack Overflow, you'll feel the gap within the first week of using any of the generic options.
For broader context on the GEO and AEO space these tools operate in, see the complete guide to AEO, GEO, and LLM visibility.
A four-week experiment any dev tool team can run
This is a practical starting point, not a definitive playbook. The signals aren't fully mapped, and anyone telling you they have AI visibility perfectly figured out is overselling. But this sequence should produce actionable findings.
Week 1: Establish your baseline honestly.
Run 20 prompts across ChatGPT, Perplexity, and Microsoft Bing Copilot. Not branded queries. Category queries, integration queries, and comparison queries: "best job queue for Python," "[Your Tool] vs [Top Competitor]," "how do I handle [the problem you solve] in [main framework you support]."
For each response: Does your tool appear? What does it say? Are competitors appearing instead, and which ones? Read the full response text, not just whether your name shows up.
Also run each prompt in Google AI Mode and Gemini Search. Note where the behavior differs from ChatGPT and Perplexity. Different retrieval architectures produce different results for the same query.
Week 2: Fix your docs for extractability.
Take the top five queries where you didn't appear. Look at who did appear and read their docs page or GitHub README. You're looking for specific gaps: a clear comparison section you don't have, an integration guide for the specific framework the query mentioned, a quickstart that actually runs without three config steps.
Fix those gaps. Write the comparison page. Build the integration guide. Rewrite the README opening paragraph. These are concrete content changes you can connect back to the prompts that exposed the issue.
Week 3: Find your third-party citation gaps.
Search each major competitor on Stack Overflow. Find the highly-voted answers that recommend them for the specific use cases you also solve. Note what makes those answers get recommended: usually they're technically specific, they acknowledge tradeoffs honestly, and they were written by someone with real credibility in the community.
Look at what GitHub awesome-lists your competitors are on that you're not. Check StackShare and AlternativeTo for whether your listing is current and whether competitors have richer descriptions.
This week's output is a prioritized list of specific third-party gaps to close, not a vague "earn more mentions" aspiration.
Week 4: Set up ongoing measurement.
A one-time audit is better than nothing. Ongoing tracking is what makes the work compound. Set up a simple spreadsheet to track your 20 core prompts across platforms weekly. Or use a tool that does this systematically. Either way, establish the weekly cadence before you finish the experiment.
The goal is to connect content actions to citation outcomes. When you publish the FastAPI integration guide, do you appear more often for "best [your category] for FastAPI" four weeks later? When you fix the inaccurate product description in your docs, does the AI stop attributing the wrong feature set to you? These are the feedback loops you're trying to build.
For a deeper understanding of the GEO and AEO framework this sits inside, or more detail on GEO strategies specific to developer tools, those posts go further.
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, Microsoft Bing Copilot, 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's 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 that requires a card, with no charge today. 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. Many free tools return a limited snapshot based on provider-defined or auto-generated prompts. More advanced tools track category queries, measure response accuracy, and compare against specific competitors. For a fuller explanation, see the complete AEO, GEO, and LLM visibility guide.
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, Microsoft Bing Copilot, 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, Microsoft Bing Copilot, Google AI Mode, and Gemini Search. Start your 7-day trial - card required, $0 today.