12 LLM Visibility Tools Compared: Coverage and Pricing

Dashboards on their own won't get this funded. What gets it funded is showing leadership which content earns citations, which prompts move buyers, and where a model is saying something about you that isn't true.

The twelve platforms worth comparing are Strivelabs, AthenaHQ, Profound, Rankscale, Otterly, Peec AI, Scrunch AI, Evertune, Knowatoa, xFunnel, Slate, and the Semrush and Ahrefs add-ons.

An LLM visibility tool samples ChatGPT, Perplexity, Google AI Overviews, Claude and Gemini, then reports where your brand appears and where competitors appear instead. The better ones align that against HubSpot, Google Ads, Search Console and GA4, which is what turns a mention count into something you can defend in a budget meeting.

At a Glance

Twelve platforms, from $29 a month to enterprise quotes. The differences that matter are engine coverage per tier and sampling depth, not feature lists.
LLM answers vary between runs, so manual checks and single-sample tools give you a reading rather than a measurement.
The trade-off is coverage against frequency. Broad coverage suits awareness reporting; daily sampling on fewer engines suits audit-grade proof.
Ask one question above all others: can it export full prompt text, citation URLs and timestamps? If not, you have charts rather than evidence.
Test integrations during the trial by moving exports into a sandbox GA4 property or HubSpot account. Feature lists are not proof.
Run 90 days. Weeks 1 to 4 gather data, 5 to 8 test fixes, 9 to 12 measure share-of-voice change.
Keep a person approving every change. 48-hour SLA on standard edits, 4 hours on false claims.

A decision checklist

Checklist item	Why it matters	Ask the vendor
Engine coverage	Missing ChatGPT or Google AI Overviews hides you on the surfaces buyers actually use	Which engines do you sample, in my tier, and how often?
Data accuracy	Leadership funds evidence, not estimates	Can you export full prompt text, citation URLs and exact timestamps?
Action layer	An insight that triggers nothing is a cost	Do alerts connect to tasks via GA4, your CMS or HubSpot?
Integrations	Joining citation data to outcomes needs your first-party data	Do you connect Google Ads, Search Console, LinkedIn, HubSpot and GA4?
Security	This tool is asking for GA4 and Search Console access	What are your retention policies, and do you offer SSO and SOC 2?
Cost predictability	Prompt-based billing is where budgets break	Exactly how do seats, engine add-ons and credit limits price out?

Coverage and accuracy are the pass/fail items. A vendor failing either doesn't reach the shortlist regardless of how good the rest looks.

The trade-offs worth understanding

Coverage against frequency. Sampling every engine gives you a broad awareness picture. Sampling fewer engines daily gives you evidence that holds up under scrutiny. Hourly checks on ChatGPT cost real money, so decide which you're buying before you compare prices.

Screenshots against API traces. Some tools capture visual evidence, others return structured data and live URLs. Screenshots work in an executive deck. API traces work in an automated workflow. Ask for both, and if you can only have one, pick based on who you're reporting to.

Share of voice against prompt-level tracking. Share of voice supports PR and brand arguments. Prompt-level tracking proves content ROI. If your mandate is leads rather than awareness, you need the tool to connect mentions to HubSpot activity, not just report a percentage.

Brand safety is a separate requirement. If a model is stating something false about your product, you need sentiment alerts and false-claim detection, and those sit in different products from the visibility tools.

Matching tools to your team

Team profile	What you need	Platforms that fit
Small team, no ops capacity	Basic monitoring with clean exports	Otterly ($29/mo), Knowatoa (~$59/mo)
Content team doing refreshes	Monitoring plus recommendations	Rankscale ($385/mo), Peec AI ($80/mo)
Growth team testing content	Monitoring plus execution	Strivelabs (quoted), AthenaHQ ($295/mo)
Technical SEO team	Crawler-level control	Scrunch AI ($250/mo), Profound (quoted)
Enterprise reporting	Audit trails and broad coverage	Profound (quoted), Evertune ($800/mo)
SEO team wanting one stack	Integrated suite	Semrush AI Visibility Toolkit ($99/mo per domain), Ahrefs Brand Radar (~$199/mo per engine)
Funnel attribution focus	Visibility mapped to pipeline stages	xFunnel (not published), Strivelabs (quoted)

Better decisions start with better infrastructure.

Most mid-market teams pick a channel and hope. Strivelabs gives you the data to know, and the infrastructure to act on it.

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The 12 LLM Visibility Platforms

Ordered by how directly visibility data becomes action, which is a choice. A team buying purely for measurement depth would reverse it.

1. Strivelabs — quoted

Samples ChatGPT, Gemini, Claude, Perplexity and Google AI Overviews with user-defined prompt sets and three samples per prompt, working standalone. Also connects to HubSpot, Google Ads, GA4, Search Console and LinkedIn Ads, so a visibility finding becomes a routed task with a named owner and enforced approval. Limitation: three samples per prompt shows whether you appear consistently, occasionally or not at all, which is enough to act on but lighter than specialists running higher counts. Five engines, so no Copilot, AI Mode, Grok or DeepSeek. Teams needing statistically robust trend reporting want a specialist alongside.

2. AthenaHQ — from $295/mo, free tier below

Nine or more models with an Action Center converting findings into owned tasks. The most balanced monitor-and-act option among the specialists. Limitation: credit-based costs move with usage.

3. Profound — enterprise, by quote

Deepest measurement in the category, with crawler analytics and long-retention history. Built for the conversation where you have to defend a number. Limitation: slow onboarding, and the depth assumes a dedicated owner.

4. Rankscale — from $385/mo

ChatGPT, Gemini, Perplexity, Claude and Grok, with page-level recommendations and refresh as often as hourly. Limitation: newer, so less history behind the trends.

5. Otterly — from $29/mo for 15 prompts

Cheapest credible entry. Scheduled scans, citation alerts, useful on day one. Limitation: API access and exports restricted on lower tiers.

6. Peec AI — from $80/mo for 50 prompts

Fast setup, clean interface, daily monitoring. Strongest coverage is European and German-language. Limitation: thin crawler analytics; confirm US English prompt handling if that's your market.

7. Scrunch AI — from $250/mo

Crawler control is the distinguishing feature, covering how AI systems reach and read your content. Limitation: needs technical ownership to justify the cost.

8. Evertune — from $800/mo

Aimed at larger brand teams, stronger on sentiment and distribution than core citation depth. Limitation: price sits outside most mid-market budgets.

9. Knowatoa — around $59/mo

Fast, inexpensive audits for a quick read on position. Limitation: smaller dataset, better for snapshots than programmes.

10. xFunnel — not published

Connects AI visibility to funnel attribution, which is a genuinely different angle. Limitation: less depth in the underlying data.

11. Semrush AI Visibility Toolkit — ~$99/mo per domain

Visibility data alongside keyword and audit data you already have. Limitation: works from estimates rather than dedicated prompt-level sampling.

12. Ahrefs Brand Radar — from $129 base, ~$199 per engine

URL-level citation signals rather than raw mention counts, extending beyond AI engines into Reddit and the wider web. Limitation: slow refresh on standard indexes, cost climbs per engine.

Before shortlisting: verify engine coverage per tier against each vendor's own documentation, not third-party roundups. Those copy each other and go stale within weeks.

How to evaluate vendors

Build a trial rig and give every vendor the same one.

Hand each of them 20 core buyer queries, three regions and your buyer personas. Ask for raw exports containing mentions and citation URLs. Ask for one sample alert covering a high-risk hallucination, because how a vendor handles a false claim tells you more than any demo.

Then check the evidence properly: exact prompt text, timestamps, specific URLs. Estimates dressed as measurements are the most common thing you'll find.

The step most teams skip

Move those exports into a sandbox GA4 property or a test HubSpot account and confirm the integration actually works. Can it tag contacts in HubSpot by AI referral? Does the Slack alert fire? Can you pull a Search Console CSV alongside it? Time how long it takes to go from alert to usable export. A pilot that depends on manual scraping loses weeks.

On commercials, push for a fixed-price 90-day pilot with guaranteed data access. Billing comes as prompt volume, engine add-ons, response credits or seats. Volume models suit scale; credit models let you pay for output rather than capacity.

On security, most B2B procurement wants SOC 2 Type II and SSO. Make sure the contract prohibits training on your data.

Piloting in 90 days

Week 0, baseline. Connect Search Console, GA4, HubSpot and Google Ads. Give the vendor your 20 core queries and three personas, and verify the data mapping before anything else starts.

Weeks 1 to 4, gather. The vendor samples across engines while you pull raw mentions and citation URLs. Cross-check every cited link against Search Console to confirm it was indexed before the model referenced it.

Weeks 5 to 8, test fixes. Fix the top ten items: content updates, structure changes, metadata. Tag AI referrals in HubSpot and build a GA4 filter so the traffic is isolated.

Weeks 9 to 12, measure. Share-of-voice change, content uplift, HubSpot activity from cited pages. Then decide: roll out, renegotiate, or stop.

Three checks that make the pilot readable:

A GA4 referral filter isolating AI-sourced sessions
A Search Console cross-check confirming the cited URL was indexed before the citation appeared
HubSpot lead tagging identifying contacts who arrived via cited pages

Good starting queries: your product name plus "alternatives", feature names plus "how to", and your category plus "pricing".

Note the two numbers are different things. Twenty queries is a vendor comparison rig, deliberately small so you can inspect every export by hand. A full prompt library for ongoing measurement runs 50 to 150 queries. Don't confuse the trial rig with the production set.

Human-in-the-loop and governance

Automated changes without limits create risk faster than they create value. The workflow that holds up is: the agent proposes, a marketer reviews, and nothing ships without approval.

Connect HubSpot for lead data, CMS APIs for drafts, Slack for alerts, GA4 for performance. Paid owns budget responses, content owns page updates.

Governance checklist:

Approval SLA. 48 hours for standard edits, 4 hours for false claims about your product.
Audit trail. Store the prompt, the timestamp, and the name of whoever approved the response.
Rollback. Your CMS needs one-click revert for at least 30 days.
Pre and post measurement. Track CTR and HubSpot activity for 30 days after each change.

That last one is what turns this from monitoring into a programme you can report on.

Conclusion

Pick the tool that samples the engines your buyers actually use, exports evidence rather than charts, and connects to the systems where your outcomes live.

Run 90 days. Verify the integrations yourself rather than trusting the feature list. Keep a person approving changes before anything reaches the live site.

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Frequently Asked Questions

What are the best LLM visibility tools?

Twelve worth comparing: Strivelabs, AthenaHQ, Profound, Rankscale, Otterly, Peec AI, Scrunch AI, Evertune, Knowatoa, xFunnel, plus the Semrush and Ahrefs add-ons. Otterly is cheapest at $29 a month, Profound deepest, AthenaHQ the most balanced monitor-and-act option.

What's the difference between an AI mention and a citation?

A mention is the model saying your brand name. A citation is the model naming you as the source, usually with a link. Mentions prove awareness; citations send traffic you can attribute.

Why won't manual checks work?

LLM answers vary between runs, so a single check tells you what happened once. Measurement requires repeated sampling across the same prompt set, which is the thing these tools automate.

How do these tools handle personalised or session-based answers?

By sampling repeatedly from multiple locations and averaging, rather than treating any single response as the answer. Ask specifically how geographic sampling works, because answers shift by region.

Which engines are the minimum for a B2B brand?

ChatGPT, Perplexity and Google AI Overviews cover most B2B research behaviour. Add Claude and Gemini if your buyers are technical. Copilot matters more in Microsoft-heavy enterprises.

Why does SOC 2 come up so often?

Because these tools request access to GA4 and Search Console. Most B2B procurement requires SOC 2 Type II and SSO before approving that access, so it's worth confirming early rather than at contract stage.

How many queries should a pilot use?

Twenty for a vendor comparison rig, small enough to inspect every export manually. 50 to 150 for a production prompt library once you've chosen. They're different exercises.

How do tools detect hallucinations about my brand?

By comparing model output against your own product facts and flagging contradictions. Coverage varies a lot, so ask for a worked example during the demo rather than accepting the capability claim.