The best AI visibility tool is the one that preserves the evidence your team needs to make a decision. For some teams that means a large prompt-and-source dataset connected to SEO research. For others it means a small controlled prompt panel with run-level answers, citations, exports, and a clear weekly workflow. Enterprise teams may also need SSO, APIs, multi-brand governance, bot traffic, and delivery controls.
This guide compares five credible approaches—Ahrefs Brand Radar, Semrush AI Visibility, Peec AI, Scrunch, and OtterlyAI—plus the native measurement stack every buyer should keep. Product coverage and pricing were reviewed on July 27, 2026. Verify current terms with the vendor before purchase.
The short answer
Best for macro discovery plus established SEO data: Ahrefs Brand Radar.
Best for teams that want SEO and AI visibility in one suite: Semrush.
Best focused prompt tracker for lean brands and agencies: Peec AI.
Best for enterprise AI customer experience, bot traffic, and governance: Scrunch.
Best entry-level monitoring and content-oriented workflow: OtterlyAI.
Best source of first-party search and referral truth: your native stack—Google Search Console, Bing Webmaster Tools, analytics, and server logs.
Do not buy from the feature grid alone. Run the same 20 prompts, three intent clusters, two competitors, and one locale through every finalist. Inspect a raw answer, verify a citation, change a prompt without losing history, export the evidence, and price the twelve-month account you will actually use.
What an AI visibility tool should do
An AI visibility tool should help a team answer four different questions.
1. Where are we visible?
The tool should record whether a brand, product, or owned source appears for a defined question on a defined AI surface. It should preserve the denominator: prompts, surfaces, locales, dates, and run conditions.
2. What evidence shaped the answer?
The tool should expose citations or source URLs where the surface makes them available. A reviewer should be able to see which claim the source appears to support and whether the URL is canonical and live.
3. What should change?
Useful analysis connects a missing or inaccurate answer to a gap: crawl eligibility, entity clarity, direct passage, comparison evidence, original data, internal links, third-party authority, or distribution.
4. Did the change work?
The system should support a stable prompt registry, historical runs, release dates, and later comparisons. Visibility, referral traffic, and conversion should remain distinct outcomes.
No vendor can guarantee mention or citation. Tools measure samples and help teams operate; they do not control generated answers.
Start with native signals
Before buying specialized software, connect the first-party sources that already describe discovery and traffic.
Google Search Console
Google says sites appearing in AI features are included in overall Search Console Web performance. In June 2026, Google also began testing dedicated generative-AI performance reports with a subset of sites. Check property availability rather than assuming the dedicated view exists.
Bing Webmaster Tools
Bing Search Performance includes impressions and clicks across sources that include web and chat experiences. Its source definitions are not interchangeable with a custom prompt tracker's mention rate.
Analytics
Segment AI referrals where referrer or campaign parameters are available. OpenAI says ChatGPT search referral URLs include utm_source=chatgpt.com. Preserve landing page, engagement, and conversion separately.
Server logs
Logs can show crawler and agent traffic to the site, subject to correct user-agent identification and infrastructure retention. They do not show the generated answer a user saw.
The specialized tool should add evidence that this native stack lacks—not replace it.
Evaluation criteria
Score each finalist from 0 to 3 and retain notes.
0: absent or not demonstrated;
1: present with material limits;
2: meets the current requirement;
3: exceeds the requirement with usable proof.
Weight only the criteria tied to a real decision.
Prompt data model
Can the tool distinguish a macro prompt dataset from a user-defined panel? Can it version prompt wording and preserve historical denominators?
Surface coverage
Which exact products are tracked? Google AI Overviews and AI Mode are distinct. ChatGPT with search is not identical to a generic model API. Ask how each response is collected.
Locale and personalization
Can the team specify country, language, and other run conditions? What is standardized, and what remains variable?
Run-level evidence
Can a reviewer open the exact prompt, answer, timestamp, citation URL, and competitor mentions behind an aggregate score?
Citation quality
Does the product expose final URLs, cited pages, domains, and the relationship between citation and answer? Can reviewers export them?
Competitive discovery
Can the tool identify prompts, categories, competitors, and source domains outside the team's fixed panel?
Historical data
How far back does vendor data go? Does history begin at setup? What happens to history when a prompt changes or a plan is downgraded?
Workflow
Can a finding become an owned action with a canonical page, acceptance criteria, and verification date? If not, can the evidence be exported cleanly into the team's workspace?
SEO integration
Does the tool connect AI observations to keywords, rankings, backlinks, content, technical audits, or Search Console? Integration is valuable only if the underlying records remain distinct.
Governance
Evaluate users, roles, SSO, audit logs, APIs, data retention, regions, security review, and deletion. Enterprise controls should be demonstrated, not inferred from a logo wall.
Total cost
Model prompts, surfaces or models, locales, cadence, brands or projects, users, exports, API, onboarding, review labor, and switching cost.
Comparison overview
Ahrefs Brand Radar
Best fit: SEO teams that want broad market discovery, large-scale source analysis, competitor research, and custom prompts within the Ahrefs ecosystem.
Ahrefs describes Brand Radar as a macro discovery product spanning AI platforms and adjacent channels such as SEO, YouTube, Reddit, and TikTok. Its custom prompt tracking supports a focused panel across major AI assistants, with selectable location and frequency.
The product's advantage is the combination of a large search-backed prompt index, cited pages and domains, brand research, and established SEO data. That makes it useful for finding questions and sources beyond a team's initial assumptions.
What to verify
which Brand Radar indexes are included in the current Ahrefs plan;
custom prompt allowance and check calculation;
exact platform and locale coverage;
run-level answer and citation export;
API access for the chosen plan;
distinction between macro index results and tracked prompts.
Pricing snapshot, July 27, 2026
Ahrefs' help center says paid plans include varying custom-prompt allowances, with additional custom-tracking tiers based on monthly checks. It also lists standalone single-platform and all-platform Brand Radar purchases. Because plan inclusion, checks, and index access create different totals, price the exact panel rather than quoting one “Brand Radar price.”
Choose Ahrefs when
the team already works in Ahrefs;
macro discovery matters as much as a fixed panel;
cited domains, SEO performance, backlinks, and topic demand need to be investigated together;
an analyst can separate dataset types and convert findings into owned work.
Do not choose it only because
the index is large. A large dataset does not remove the need for a controlled panel tied to your positioning and buying journey.
Semrush AI Visibility
Best fit: marketing teams that want AI visibility, SEO monitoring, prompt research, competitor analysis, and reporting in a single suite.
Semrush's AI Visibility features include high-level domain benchmarks, brand-performance tracking, competitor research, prompt research, custom tracking, and integrations with its wider SEO workflow. Official documentation distinguishes a broad prompt database from daily tracking of selected keywords or prompts.
The current Base AI Visibility price page lists one domain, 25 custom prompts, and coverage across ChatGPT, Google AI experiences, Gemini, and Perplexity at $99 per month per domain when billed annually. Additional domains, prompts, or users can change the total.
What to verify
whether the needed features sit in AI Visibility, Semrush One, SEO Toolkit, Content Toolkit, or Enterprise;
custom prompt and domain limits;
country and surface coverage;
per-user access charges;
CSV and API requirements;
whether a recommendation is based on correlation, crawl checks, or direct observation.
Choose Semrush when
one team owns SEO and AI visibility;
competitor and prompt discovery should sit beside classic search research;
scheduled reporting matters;
the required domain, prompt, and user counts fit the package.
Do not choose it only because
it produces one visibility score. Keep mentions, citations, source coverage, prompt panel, and business outcomes separate.
Peec AI
Best fit: brands and agencies that want a focused, daily custom-prompt tracker with a clean interface, multi-model coverage, projects, and unlimited users on published brand plans.
Peec's current pricing page organizes brand plans by prompts, selected models, projects, countries, and daily tracking. Its July 2026 published product instructions list Starter at $95 per month for 50 prompts, three selected models, one project, one country, and unlimited users; Pro and Advanced expand prompts and projects. Enterprise adds broader model coverage, API access, SSO, and custom setup.
Peec explains its usage in “AI answers”: one prompt across one model for one day is one observed answer. That arithmetic is useful because it reveals the cost driver.
What to verify
current monthly versus annual price;
how prompts, models, countries, and projects are allocated;
whether history survives plan or prompt changes;
raw answer and citation export;
agency workspace and client isolation;
API, Looker, MCP, and SSO availability.
Choose Peec when
the primary need is a versioned custom panel rather than a giant macro index;
multiple team members need access;
agencies need multi-project allocation;
daily tracking across selected models is worth the observation volume.
Do not choose it only because
daily data sounds more precise. Daily runs can create noise and review cost if the team makes decisions weekly.
Scrunch
Best fit: larger organizations that want prompt monitoring and citations plus an agent-experience layer, site auditing, bot traffic, integrations, and enterprise controls.
Scrunch positions its product across monitoring, citations, insights, agent traffic, site maps, and an Agent Experience Platform. Its published Core plan is $250 per month with 125 unique prompts, five site audits per month, one brand workspace, five user licenses, and four supported LLMs. Enterprise adds custom coverage, APIs, SSO, dedicated support, and broader agent-experience capabilities.
This is a materially broader proposition than prompt tracking alone. The buyer should decide whether agent delivery and bot analytics are actual requirements or attractive extras.
What to verify
which product modules are included in Core versus Enterprise;
evidence for each site-audit recommendation;
data handling when content is served differently to agents;
expanded model coverage and collection method;
API, retention, SSO, and security terms;
separation between monitoring results and vendor-delivered content changes.
Choose Scrunch when
prompt monitoring is part of an enterprise AI-customer-experience program;
bot traffic and agent-facing delivery are governed projects;
security, procurement, and implementation resources exist;
the organization needs a vendor partnership rather than a lightweight tracker.
Do not choose it only because
it has the broadest product story. Buying agent delivery before establishing a reliable measurement contract increases cost and causal ambiguity.
OtterlyAI
Best fit: smaller marketing and content teams that want a lower-cost entry into prompt research, brand and link monitoring, content audit, and GEO workflows.
Otterly's current site says pricing starts at $29 per month and describes monitoring across ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews, and AI Mode. The product emphasizes prompt research, AI search analytics, link tracking, content audit, and optimization.
Published pricing details have changed over time, including prompt allowances and platform coverage, so the live checkout and plan page should be treated as the source of truth.
What to verify
prompts included at the current entry price;
weekly versus daily refresh;
platform coverage by plan;
user and project limits;
raw answer and citation export;
content-audit methodology;
historical retention and API availability.
Choose Otterly when
the team needs a low-friction pilot;
weekly monitoring is sufficient;
content research and link analysis are valuable;
the panel is small enough to review manually.
Do not choose it only because
the entry price is low. Confirm the cost of the full prompt panel, required surfaces, and history.
Which tool is best for each team?
A two-person B2B content team
Start with native signals and a 20-prompt manual panel. Trial Otterly or a focused Peec plan if manual collection becomes the bottleneck. Keep the canonical prompt and decision records in your workspace.
An SEO team already using Ahrefs
Use Brand Radar for macro discovery and custom prompts if the included allowance and required indexes fit. Link discoveries to Site Explorer, Keywords Explorer, backlink, and content work—but label each dataset.
A marketing team standardized on Semrush
The AI Visibility Toolkit or Semrush One can reduce tool switching. Confirm per-domain, prompt, and user economics, then test whether exports contain the run evidence your review process needs.
An agency
Prioritize multi-client separation, prompt allocation, reusable setup, exports, reporting, unlimited or economical seats, and the right to retrieve historical data. Peec publishes agency-specific packaging; Semrush, Ahrefs, Otterly, and Scrunch should be priced against the same client panel.
An enterprise web and brand organization
Shortlist products that can pass security review and provide SSO, roles, API, data retention terms, multi-brand governance, onboarding, and support. Scrunch is relevant when bot traffic and agent delivery are in scope; enterprise offerings from Semrush, Ahrefs, or Peec may fit different data and workflow requirements.
The 90-minute vendor proof test
Run this test with every finalist.
Minutes 0–15: load the panel
Import 20 prompts from category, workflow, and comparison intent. Add two competitors and one locale. Record setup time and required manual cleanup.
Minutes 15–35: inspect one observed answer
Open the exact prompt, run time, surface, visible answer, brand mention, competitors, and citations. Follow a citation to the final URL. Confirm the source supports the associated claim.
Minutes 35–50: change the specification
Create version two of a prompt, change frequency, and add a surface. Verify that version one history and denominators remain available.
Minutes 50–65: find a content decision
Choose one recurring gap. Ask the vendor workflow to identify the source pattern and candidate owned page. Reject generic advice that cannot cite the observed runs.
Minutes 65–75: export
Export prompt definitions, runs, answers, citations, URLs, timestamps, locales, competitors, and metrics. Confirm the format can be joined to canonical resource IDs and public URLs.
Minutes 75–90: price and exit
Price twelve months for the real brands, projects, users, prompts, models, locales, cadence, API, and reports. Ask how to export all history at cancellation.
A vendor that cannot demonstrate raw evidence, stable history, or usable export should not win because its dashboard looks polished.
Total cost model
The basic observation volume is:
prompts × surfaces or models × locales × run frequency
Then add:
brands, projects, or domains;
users and permission tiers;
reports and exports;
API or data warehouse integration;
onboarding and support;
human review time;
historical data and switching risk.
Example panel
A team wants 40 prompts across four surfaces, two locales, weekly.
That is 40 × 4 × 2 × 52 = 16,640 prompt-surface-locale observations per year before retries, variants, or expansion.
If the product prices by “prompts,” ask whether each prompt includes all models and locales. If it prices by “checks” or “answers,” calculate the multiplication explicitly.
Review labor
Software can classify mentions, but humans still need to review inaccurate descriptions, negative framing, ambiguous brand names, and whether a citation supports the answer. Estimate minutes per reviewed run and sample intelligently.
Switching cost
Ask whether prompt definitions, raw answers, citations, annotations, and historical metrics can be exported. A low subscription with locked history may be expensive to leave.
Pricing snapshot
Pricing changes frequently. The following public starting points were reviewed on July 27, 2026 and are not quotes.
Ahrefs Brand Radar: plan-dependent custom prompt allowances plus add-on or standalone index options; price the exact indexes and checks.
Semrush AI Visibility Base: public page shows $99/month per domain when billed annually, with 25 custom prompts.
Peec AI Starter: its July 2026 product instructions list $95/month, 50 prompts, three selected models, one project, one country, and unlimited users.
Scrunch Core: public page shows $250/month, 125 unique prompts, one brand workspace, five users, and four LLMs.
OtterlyAI: current homepage says pricing starts at $29/month; verify the live prompt and refresh allowance.
Taxes, annual commitments, extra users, domains, prompts, models, locales, APIs, and enterprise terms can materially change the total.
Procurement checklist
Data contract
Macro and custom datasets are clearly separated.
Prompt, run, answer, citation, and source schemas are documented.
Surface, locale, date, and model or product state are preserved.
Metric denominators are exportable.
Collection methods and known limitations are explained.
Evidence
Exact observed answers can be reviewed.
Citation URLs and final URLs are available.
A reviewer can judge which claim a citation supports.
Historical runs survive prompt versioning.
Failed or unavailable runs are not silently dropped.
Workflow
Findings can link to canonical content resources.
Owners and verification dates can be assigned or exported.
Release dates can be annotated.
Reports preserve segmentation.
The team can retrieve its records at exit.
Governance
Roles and user access meet policy.
SSO and audit needs are satisfied.
Retention and deletion terms are clear.
API and integration permissions are scoped.
Security review covers any agent-facing delivery.
Commercial
The quote uses the real panel.
All brands, projects, users, and locales are included.
Overage and add-on rules are explicit.
Annual renewal terms are known.
Export and cancellation paths are demonstrated.
Common buying mistakes
Buying the largest index
Macro datasets are valuable for discovery. They do not replace the questions unique to your product, positioning, and buying journey.
Comparing plan names instead of observations
“100 prompts” may mean 100 prompts across all platforms or 100 prompt-platform checks. Normalize vendors to annual observations.
Ignoring citation evidence
A citation count without the URL and supported claim is too weak for editorial decisions.
Treating an optimization score as causation
Vendor recommendations may use correlations, heuristics, crawl checks, or observed gaps. Ask which, and verify the destination after each change.
Paying for daily tracking without daily decisions
More frequent data increases cost and noise. Match cadence to the operating rhythm.
Letting the vendor own the strategy record
Store the prompt registry, canonical resources, accepted changes, approvals, and QA in a durable workspace. The vendor should supply evidence, not become the only place decisions exist.
Assuming one tool covers every truth
Prompt trackers, search platforms, analytics, and logs observe different layers. Join them through prompt clusters, canonical URLs, dates, and changes.
Frequently asked questions
What is the best AI visibility tool?
Ahrefs is strong for macro discovery and SEO-connected research; Semrush for an integrated SEO and AI suite; Peec for focused custom-prompt tracking; Scrunch for enterprise agent experience and governance; and Otterly for a lower-cost monitoring and content workflow. The best choice depends on the evidence and decisions required.
Are AI visibility tools accurate?
They can accurately report the runs they collected if the methodology and evidence are preserved. They cannot represent every answer every user will see. Review prompts, surfaces, locales, timestamps, citations, and collection methods.
How many prompts should a company buy?
Start with 20–40 prompts across real intent clusters. Add prompts only when each has a decision use, owner, and review capacity.
Should AI visibility be tracked daily?
Daily tracking is useful for volatile categories or active experiments with daily decisions. Weekly is usually enough for content operations; monthly may fit stable monitoring.
Is Ahrefs Brand Radar better than Semrush AI Visibility?
Ahrefs emphasizes a large search-backed macro index plus SEO and source research. Semrush offers AI visibility inside a broader integrated marketing and SEO suite. Test both with the same custom panel, required exports, domains, and users.
Is a low-cost AI visibility tool enough?
It can be enough for a small, well-designed panel. Confirm surface coverage, refresh cadence, run evidence, citation export, history, and the cost of the panel after growth.
Do I still need Search Console and analytics?
Yes. Specialized trackers observe generated answers and citations. Search Console, Bing Webmaster Tools, analytics, and logs provide first-party discovery, referral, crawl, and conversion evidence.
Can these tools improve AI visibility automatically?
They can identify gaps and may recommend changes. The team still needs to evaluate evidence, publish useful and accurate content, verify the destination, and rerun the panel. No tool can guarantee inclusion.
Final recommendation
Buy in this order:
connect native search, analytics, and log signals;
define a versioned 20–40 prompt panel;
trial tools with the same panel;
reject products without run-level evidence and export;
model twelve-month observation volume and review labor;
store accepted decisions against canonical content resources;
expand only after the workflow produces verified changes.
The best AI visibility tool is not the one with the most colorful score. It is the one that helps your team move from a reproducible observation to a better public source—and prove what happened next.