AI Search Visibility Tools Compared: Pricing & Features

By openllmrank · Updated September 23, 2026 · 6 min read

An AI search visibility tool measures whether AI answers mention your brand, cite your website, or recommend a competitor. The right choice depends on which answers you need to measure, how often you need fresh results, and whether you can inspect the evidence.

We build openllmrank. This comparison uses the official vendor pages linked below, checked September 23, 2026. It is a documentation review, not a hands-on test of competitors. Prices are in USD; coverage and allowances can change.

Compare price, measurement, and evidence

On small screens, scroll the table sideways to compare all columns.

Which option fits your team?

• A small team starting a benchmark: consider openllmrank if five grounded APIs and weekly evidence meet your needs. Choose the $79 report for a single assessment or $49/month for tracking.
• An agency managing several clients: ask each vendor about client workspaces, seats, export rights, and costs per client. openllmrank changes to monthly scheduling beyond three brands, so verify that cadence fits before buying.
• A team needing daily tracking and enterprise controls: evaluate Profound and Athena against your security, region, and workflow requirements. Request a sample export and a written coverage breakdown.
• An SEO team researching a whole category: evaluate Brand Radar’s discovery indexes separately from its custom prompt tracking. A large discovery corpus does not mean every question you care about is tracked.
• A technical team with its own workflow: start with the open-source CLI and budget for API calls, scheduling, and maintaining the analysis.

API benchmarks and consumer AI results are different

openllmrank runs web-enabled provider APIs. Those results can differ from ChatGPT, Gemini, or other consumer applications because the model, search tools, location, conversation history, and personalization may differ. Google AI Overviews and AI Mode are separate surfaces. An OpenAI API response is not a measurement of a particular ChatGPT user’s screen.

Ask vendors which surface they collect, whether prompts are synthetic or based on observed demand, and how they handle failed answers. Compare like-for-like prompt sets and sampling methods. Read our benchmark methodology and limitations.

How to evaluate AI visibility gap analysis

1. Choose a stable set of buying questions, including specific categories and constraints your customers actually use.
2. Record your brand and competitors across repeated answers. Separate a brand mention from a linked citation.
3. Inspect the answers where a competitor appears and you do not. Open the cited sources and check their relevance.
4. Make a specific improvement, such as clearer product information or an accurate independent listing. Record the change date.
5. Repeat the same benchmark. Changes are observational: model and retrieval changes can also move the results.

Questions to ask before buying

• Can I export the exact prompts, timestamps, answers, and cited URLs?
• Does one credit buy a question, one model response, or a full multi-model run?
• What happens to the score when a provider fails or returns no sources?
• Can I separate model changes from changes in my own content?
• Which features require another subscription, extra credits, or a sales agreement?

Can I check AI visibility for free?

You can ask questions manually or use the CLI with your own paid API keys. Manual checks are useful examples, but repeated measurements are needed to describe variability. Our free AI crawler checker diagnoses crawl access; it does not measure whether AI recommends your brand.