GEO Monitoring Tools Compared (2026): Profound, Otterly, Peec and More

Independent category research. Every price here was pulled in June 2026 and is shown in USD. Anything we couldn't verify is flagged inline.

The short version

If you came here for a single name to buy, the honest answer is that there isn't one. The right tool depends almost entirely on who you are and how much of your own time you're willing to spend.

But here's the practical guidance, compressed.

No single tool wins for everyone. For most budget-conscious solo operators and early-stage B2B founders, the best 2026 answer is Peec AI (clean daily tracking from ~$89–95/mo, with pricing you can actually read off a page) or Otterly.AI Lite at $29/mo as a toe-in-the-water option. Notice who isn't on that list: Profound. It repositioned in 2026 to enterprise-only custom pricing and is now effectively out of reach for small teams. The Manual Runbook — building the tracking yourself — is genuinely competitive at very small scale (≤25 prompts, 2–3 engines) and for the technically fluent, but its hidden cost is your time, and that cost is real.

The weighted leaderboard, scored through an operator/founder lens: Peec AI (4.10) > Otterly.AI (3.92) > the Manual Runbook (3.55) > Scrunch AI (3.44) > AthenaHQ (3.40) > Profound (3.10). Read that last entry carefully, because it's counterintuitive. Profound scores lowest for this persona despite being the best-funded, most capable platform in the category. That isn't a knock on the product. It's that enterprise-only pricing and an analyst-heavy workflow are simply a poor fit for a time-poor small team.

And the build-vs-buy headline: at around 25 prompts across 2–3 engines, the Manual Runbook is the cheapest path (~$30–60/mo in API and scraping costs) and it wins if you value your time below roughly $60/hr. Somewhere between ~25 and ~100 prompts, a paid tool (Peec or Otterly) almost always pulls ahead once you price your own hours honestly. And above ~100 prompts or 4+ engines, building it yourself quietly turns into a part-time job. At that point, buy the tool.

Everything below is the reasoning, the receipts, and the numbers behind those calls.

Why GEO monitoring matters now (2026)

Picture the moment a prospect actually decides who to evaluate. Increasingly, it doesn't start with ten blue links. A measurable and fast-growing share of buying research has moved into AI answer engines — ChatGPT, Google's AI Overviews and AI Mode, Perplexity, Gemini, Claude, and Microsoft Copilot. The trajectory is steep enough that Semrush's own research predicts traffic from large language models will overtake traditional Google search by the end of 2027.

For a B2B SaaS or dev-tools company, the consequence is concrete and a little unnerving. A prospect asks "best observability tool for Kubernetes" and gets back a clean, synthesized answer naming three vendors. If you're one of the three, you're in the consideration set. If you're not, you never entered it — and the worst part is that no click ever shows up in your analytics to tell you it happened. You don't lose loudly. You lose silently.

This is the gap that Generative Engine Optimization is meant to close. GEO — also called Answer Engine Optimization, or AEO — is the discipline of measuring and improving how often, how prominently, and how favorably your brand appears inside those AI answers. Monitoring is the measurement half of that work. It exists for a specific reason: unlike Google rankings, AI answers are (a) non-deterministic, (b) personalized and regionalized, (c) largely invisible in standard web analytics, and (d) shifting week to week as the models update underneath you. You cannot manage what you cannot see, and GA4, by design, shows you almost none of it.

Two honest caveats frame everything that follows, and they matter enough that you should hold them in mind through every recommendation below.

First, AI answers are probabilistic and unstable. The same prompt can return different brands and different citations just hours apart. Profound's own internal research found that up to 90% of cited sources in AI answers can change over time, with different models leaning on largely distinct source sets. The practical takeaway is to treat any single-week movement as signal-poor. Don't redesign your content strategy around a Tuesday-to-Thursday wobble.

Second — and this is the critique that should temper any purchase decision — there is not yet strong, public, accounting-grade evidence that lifting your "AI visibility score" reliably lifts traffic or pipeline. AI referral traffic is currently a small fraction of total sessions for most sites, even where citation is frequent. So visibility is best understood as a leading indicator and a brand-reputation signal, not as proven revenue. Buy monitoring to understand a channel that is genuinely shifting. Don't buy it because a dashboard number feels intrinsically valuable. It isn't, on its own.

What to actually measure

Five metrics carry the weight here. Everything else on a GEO dashboard is decoration.

1. Visibility, or share-of-voice. Across the prompts you track, how often does your brand show up at all, and what share of the "answer space" do you own versus the competitors you name? This is the headline number, but it's only meaningful in two contexts: relative to competitors, and over time. In isolation it tells you very little.

2. Citations versus mentions. These get conflated constantly, and they shouldn't be. A mention is the model naming your brand in its answer. A citation is the model linking to or sourcing one of your actual URLs. They come apart in both directions. You can be mentioned without being cited (good for reputation, but there's no referral path back to you) or cited without being the subject (your page is used as evidence for an answer that's really about someone else). The better tools separate the two cleanly and show you which URLs are getting cited.

3. Sentiment, or framing. When a model describes you, what tone does it take — positive, neutral, or off? "Off" is the interesting category: outdated positioning, the wrong product category, a comparison framed around a competitor's strengths. This is where hallucinations and stale messaging quietly surface, often before you'd catch them anywhere else.

4. Competitor gap. Which prompts do competitors win that you lose, and which sources — Reddit threads, G2, listicles, docs — are feeding those wins? This is the single most actionable output in the whole category, because it doesn't just tell you that you're behind. It tells you what to build, or where to go earn a mention.

5. Agent and bot traffic. Two distinct things hide under this one heading. There are AI crawlers (GPTBot, ClaudeBot, PerplexityBot, and the rest) hitting your server, which are visible only in your server logs and not in GA4. And there's AI referral traffic — actual humans clicking citation links from chatgpt.com, perplexity.ai, and so on — which is visible in GA4 once you set it up. The crawl is the leading indicator; the referral is the lagging business outcome. You want eyes on both.

Methodology and evidence sources

A quick word on how this was built, because in this category the sourcing is half the story.

This report prioritizes evidence in a deliberate order: (a) hands-on independent teardowns and reviews; (b) community sentiment from Reddit, LinkedIn, and verified G2/Capterra reviews; (c) live vendor pricing pulled in June 2026; and only then, last, (d) vendor marketing claims, which are labeled as such wherever they appear.

Three honesty notes are specific to this space and worth stating plainly.

First, a lot of the "review" content you'll find online is published by competing tools. AI Peekaboo, Trakkr, Cairrot, GetMint, Scalenut, Writesonic, Dageno, and others all publish "reviews" of their rivals while selling their own product. I've leaned on that content only for factual pricing and feature data that I could corroborate elsewhere, and I've discounted the verdicts entirely. A competitor's opinion of a competitor is not evidence.

Second, pricing in this category is volatile and frequently gated. Where a vendor shows only "contact sales," I say so directly, and I flag any third-party figure as unverified rather than dressing it up as fact.

Third, two widely-circulated Reddit critiques keep surfacing in secondary coverage. One is a February 2026 r/DigitalMarketing thread reportedly concluding that most tools measure mentions rather than accuracy, and that API results don't always match what you see in the real ChatGPT interface. The other is a multi-month test reportedly finding no consistent correlation between AI mentions and traffic. Both appear repeatedly in secondary sources, but I could not independently verify the original threads. So I cite them only as circulating claims, and I lean instead on verifiable sources — for example Search Engine Land's "directional, not absolute truth" framing — to make the same underlying points on firmer ground.

Where something couldn't be confirmed, it's flagged inline with [Unverified] or [Not publicly disclosed as of June 2026]. Where vendors contradict each other, I note the conflict rather than picking a side silently.

The scoring rubric

The thirteen dimensions below are weighted for one specific buyer: the time-poor, budget-conscious solo operator or small B2B team. The weights sum to 100%. If that isn't you, re-weight freely — the per-dimension scores are all shown later precisely so you can.

Scores run 1–5, where 5 is best. Crucially, they reflect fitness for this persona, not absolute capability — which is exactly why the most powerful platform in the category, Profound, does not top the table.

Tool-by-tool deep dives

Profound

What it is. This is the category's flagship, and it's not close. A New York-based "marketing platform for the AI era" that on February 24, 2026 raised a $96M Series C led by Lightspeed Venture Partners — with Sequoia, Kleiner Perkins, Evantic, Saga VC, and South Park Commons also in — at a $1B valuation, bringing total funding past $155M. It serves 700+ enterprises, including more than 10% of the Fortune 500: Target, Walmart, Figma, MongoDB, Ramp, and U.S. Bank are all on the list. It tracks 10+ engines (ChatGPT, Gemini, Google AI Overviews, AI Mode, Perplexity, Claude, Copilot, Grok, Meta AI, DeepSeek), pioneered "Prompt Volumes" (real AI search-volume data mined from a large anonymized conversation panel), offers crawler analytics through CDN integration, is SOC 2 Type II compliant, and in 2026 launched Profound Agents for content automation. On paper, it does everything.

The 2026 pricing problem for small teams. The catch is that you may no longer be allowed to buy it the way a small team needs to. Profound's pricing has been a moving target. Through late 2025 it published a Starter tier (around $99/mo, ChatGPT-only, ~50 prompts), a Growth tier (around $399/mo, adding Perplexity and AI Overviews, ~100 prompts, with content capped at a few articles a month), and a custom Enterprise tier. As of June 2026, the public pricing page shows only "Currently available through customized enterprise pricing" — no self-serve tier, no free trial, every conversation now starting with sales. Third-party reviews place real enterprise contracts in the mid-four-figures-per-month range [Unverified — third-party estimate, $2,000–$5,000+/mo]. Full engine coverage, API access, and Prompt Volumes all sit at that top tier.

Where it falls down for this persona. Three things, specifically. First, price and gating: the very features that justify Profound are enterprise-only, and there is no longer a realistic self-serve path to them. Second, workflow: it's reporting-first and data-dense. Multiple verified G2 reviewers describe the dashboards as hard to action, with one noting "There have been instances where data is not captured every day across all LLMs, which can make trending less reliable than expected," and another saying it's "harder than it should be to quickly understand what to prioritize." Third, multi-tenancy: agencies have to run a separate account per client. Independent hands-on testing scored it around 3.2/5 [Unverified — single reviewer's figure], citing reliability and learning-curve issues despite the genuinely valuable data underneath.

Verdict. This is best-in-class data — Prompt Volumes is a real, unique differentiator, and the product is a G2 Winter 2026 AEO Leader with ~303 reviews at 4.6/5. But in 2026 it is built for Fortune 500 marketing orgs that have analysts on staff. For a solo operator or an early-stage founder, it's the wrong tool at the wrong price. Revisit it only when you've grown into a dedicated GEO program with budget to match.

Otterly.AI

What it is. If Profound is the enterprise flagship, Otterly is the friendliest front door in the category. Self-serve, transparent pricing, fast setup. The base plans track four engines — ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot — with Google AI Mode and Gemini available as paid add-ons. You also get a "GEO Audit" (an on-page AI-readiness analysis across 25+ factors), link-citation analysis, competitor benchmarking, support for 50+ countries, and Looker Studio export.

Pricing (June 2026, from the vendor pricing page). Lite is $29/mo (15 prompts, the core 4 engines); Standard is $189/mo (100 prompts); Premium is $489/mo (400 prompts). Daily tracking and unlimited workspaces come with every tier. Overages run $99 per 100 prompts, or $85 on annual billing. The Gemini and AI Mode add-ons are separate line items, reported in the $9–$149/mo range depending on tier [Unverified — third-party figure]. There's a 14-day free trial with no card required. Agency partners get extra prompts (150/500) and white-label reporting.

Sentiment. Strong and consistent. G2 sits at 4.9/5, with recurring praise tags of "Valuable Insights," "Easy Setup," and "Intuitive" — one reviewer reported "tracking AI visibility within 10 minutes." A verified agency reviewer put it this way: "my account teams can quickly pull a report during a client call… they have some of the best customer support we've experienced." The criticism is just as consistent, and worth taking seriously: limited depth and weak actionability (it's good at telling you what, less good at telling you what to do), data that feels weekly to some users, and those add-on costs, which make the headline "six engines" true only once you've paid extra.

Verdict. This is the best place to start GEO on a small budget, full stop. The $29 Lite tier is the cheapest credible entry point in the market and it's ideal for validating whether AI visibility even matters for your category before you commit real money. The honest catch is the gap from Lite to Standard: it's a steep jump ($29 to $189), and you'll likely outgrow Lite's 15 prompts quickly — at which point Peec becomes a close substitute well worth pricing against.

AthenaHQ

What it is. A premium, action-oriented GEO platform founded by ex-Google Search and DeepMind engineers (Andrew Yan and Alan Yao), backed by Y Combinator with a $2.2M seed. It covers six engines from day one (ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overviews), and where it really differentiates is the bias toward execution: an "Action Center" with structured on-page and off-page recommendations, an "Ask Athena" copilot, a unified GEO Score, revenue attribution through Shopify and GA4, and a properly developed agency layer (pitch workspaces, lead routing, partner tiers). Enterprise localization spans 60+ countries.

Pricing. Premium and credit-based. The self-serve/Growth tier is $295/mo (with a discounted first month around $95), covering 5-engine tracking, brand-perception analytics, and unlimited topics governed by a credit allowance. A higher tier sits around $545–695/mo [Unverified — third-party figures vary], and Enterprise is custom, with one source citing $2,000+/mo [Unverified]. There's no free trial. Credits cover both tracking and content optimization, and reviewers consistently flag that the credit burn is hard to predict in advance.

Sentiment. G2 sits at 4.9/5 across 32 verified reviews — high, but a small sample, so weight it accordingly. The standout praise is all about action: "Other tools we tried gave us beautiful charts and left us wondering what to do. AthenaHQ tells us specifically which content to create or update." One reviewer described leaving a roughly $6k/month rival because "the dashboards were so dense that extracting a single insight for leadership took hours" — an implicit Profound contrast if ever there was one. The criticism centers on the $295 floor, the lack of a trial, the opaque credit accounting, and the occasional young-product bug.

Verdict. This is the strongest actionability of the dedicated tools, and a genuinely good fit for agencies and for e-commerce/Shopify brands that need attribution and white-label pitching. But the $295 entry, the credit unpredictability, and the no-trial barrier together make it overkill for a bootstrapped solo operator or a pre-revenue founder. Consider it once you've got budget and, just as importantly, a content team standing by to actually execute its recommendations.

Peec AI

What it is. The fastest-growing dedicated GEO tool, and for this persona, the value sweet spot. Berlin-based, founded in early 2025 by Marius Meiners (CEO), Tobias Siwonia (CTO), and Daniel Drabo (CRO), who met in Antler's Berlin cohort. It has raised $29.1M total — a €5.2M seed led by 20VC and a $21M Series A led by Singular that closed on November 18, 2025, with Antler, Combination VC, identity.vc, and S20 also participating. CEO Meiners declined to disclose the valuation but said it had tripled and now sits above $100M. In its own Series A release, Peec reports that it has "onboarded 1,300+ brands and agencies (adding 300+ customers per month)… and grown to $4M+ ARR," with named customers including n8n, Attio, ElevenLabs, Chanel, TUI, Axel Springer, and DEPT. Notably, it tracks via UI scraping — simulated browser sessions — rather than pure API calls, which it argues better matches what a real user actually sees. Sentiment analysis, regional benchmarking across 115+ languages, source-level citation data, unlimited users on every tier, Looker Studio/CSV/API exports, and a beta "Actions" feature (a prioritized owned/earned-media to-do list) round out the package.

Pricing (verified from the vendor page, base/3-models). Starter is $95/mo (50 prompts, choose 3 models, 1 project, 1 country, daily tracking, unlimited users). Pro is $245/mo (150 prompts, 3 models, 2 projects, 3 countries) and is the recommended tier. Advanced is $495/mo (350 prompts, 5 projects, Looker integration, custom onboarding). Enterprise is custom (all models, including Claude/GPT-5 search, plus API and SSO). Pay close attention to the model: each plan includes three models, and every additional model is a paid add-on — reported at +$30/mo on Starter rising to +$140/mo on Advanced [Unverified — third-party figure] — so full multi-engine coverage adds up faster than the headline suggests. Annual billing is available, and one annualized Starter equivalent is cited around $80/mo. There's a 14-day free trial, no card needed.

Sentiment. Universally strong on the fundamentals: a clean UI, fast setup, and — cited in nearly every single review — direct Slack access to the founding team, which is genuinely unusual at this price point. G2 shows 5.0/5, though on a thin review base, so don't over-read it. The consistent critique across independent reviews (and in Profound's own competitive teardown) is that Peec is measurement, not execution. It tells you where you stand and which sources matter, but its recommendations engine is shallower than Profound's, it doesn't quantify prompt search-volume with hard numbers, and it doesn't write content or surface the specific Reddit threads shaping the answers. One European enterprise user, quoted in a review, was "totally dissatisfied with the search volumes" — meaning the color-coded 1–5 volume scale rather than real numbers.

Verdict. For a budget-conscious founder or small B2B team that wants clean daily tracking, real competitor share-of-voice, and citation sources without signing an enterprise contract, Peec is the strongest all-round pick of 2026. The honest catch lives in that pricing structure: the three-models-included, add-on-for-more model means "track everything" costs meaningfully more than the $95 headline, and you still own all of the execution yourself.

Scrunch AI

Major update — now owned by Sitecore. Before anything else, the news that changes the math: on June 3, 2026, Sitecore acquired Scrunch AI for a reported $225M. This materially shifts the buying calculus. Scrunch is no longer an independent startup; it's now a feature of a large enterprise DXP (digital experience platform) vendor. For a solo operator or small team, that raises a real risk of enterprise-ification — pricing, packaging, and roadmap may all drift toward Sitecore's enterprise base, and standalone self-serve access is no longer guaranteed. Verify current availability and pricing directly before you commit to anything here.

What it is. An enterprise-leaning monitoring platform that came out of beta in 2024 and raised a $15M Series A led by Decibel (with Mayfield and Homebrew; ~$19M total) before the acquisition. It served 500+ brands and differentiates on three things: a strong GA4 integration for actual AI-referral traffic, AI bot/agent traffic analytics, and (Enterprise-only) hallucination detection. It also markets an Agent Experience Platform (AXP) that would serve an AI-optimized version of your site to bots — but AXP remained in limited pilot/waitlist with no public launch as of mid-2026, so it shouldn't factor into a buying decision today. SOC 2 Type II certified.

Pricing (verified pre-acquisition, June 2026). Core is $250/mo (125 prompts, 4 engines — ChatGPT, Perplexity, Google AI Overviews, Copilot — plus 5 site audits, 1 brand workspace, 5 users, and Looker + a query API). Agency Core is $500/mo (250 prompts, 3 brand + 3 pitch workspaces, unlimited users). Enterprise and Agency Enterprise are custom (expanding to 9 models, with SSO, hallucination detection, and API). There's no free trial. The structural catch that independent reviewers keep flagging: the differentiating feature (hallucination detection) and the broad engine coverage are both Enterprise-gated, so at $250 you're getting 4 engines and ~125 prompts that deplete across those engines quickly. [Post-acquisition pricing not confirmed as of June 2026.]

Sentiment. G2 sits at 4.6/5 across ~55 reviews (76% five-star) — the second-largest verified review base in this whole set, behind only Profound. Verified praise: "the API returns full response text with citation URLs, and competitor tracking shows position and sentiment… GA4 integration for AI referral traffic is better than anything else we evaluated," and agencies like that it scales up and down and consolidates client reporting. Verified criticism: a frustrating, shifting sales process (one buyer reported that "pricing confirmed in writing got retracted"), the cost, "Insights" and "Site Audits" that still feel like they're maturing, a confusing prompt-credit system, and data visualization that's weaker than the enterprise price tag implies. An independent reviewer at GrowthPact made the bluntest DIY point of the entire category: "Most insights you can get for free: run prompts yourself, note citations and competitor mentions. Scrunch consolidates this, but doesn't discover anything new… Solo marketers can skip it."

Verdict. This is best-in-class for AI bot-traffic and referral analytics, and a strong choice for agencies and mid-market teams that want clean multi-client monitoring with a real GA4 tie-in. For a solo operator, the $250 floor plus the Enterprise-gating of its best feature already made it hard to justify over Peec or Otterly — and the Sitecore acquisition layers on roadmap and pricing uncertainty that argues against it even harder for small teams right now. It's strong if bot-traffic visibility is your specific priority and you've got enterprise budget. Otherwise, wait and see how the ownership change settles.

The Manual Runbook — the full DIY spec

A note before the spec: this is designed as a first-class competitor, not a straw man set up to lose. The Manual Runbook is a deliberately small, scheduled system that a technically comfortable operator can stand up in a weekend and maintain in one to two hours a week. It will not match a paid tool on polish, multi-engine parity, or out-of-the-box historical trend storage. But it wins decisively on the three things a small operator often cares about most: cost, control, and transparency at small scale. Here's how to build it.

The prompt battery

Start by designing a representative set of prompts that spans the buyer journey, not just variations on your own brand name. For a solo operator, 20–30 prompts is the realistic, maintainable maximum — go much past that and it becomes a chore you'll quietly abandon. Allocate them roughly like this:

• Brand prompts (3–5): "What is [Brand]?", "Is [Brand] any good?", "[Brand] vs [Competitor]" — these catch sentiment and hallucinations.
• Category prompts (6–10): "best [category] tool for [segment]", "top alternatives to [market leader]" — these are your highest-value share-of-voice queries.
• Problem-aware prompts (5–8): "how do I [solve the problem your product solves]" — this is where you win or lose the un-branded buyer who doesn't know you exist yet.
• Solution-aware prompts (4–6): "best way to [do the thing] with [approach]" — mid-funnel.
• Competitor prompts (3–5): track the share of 2–3 named rivals so you've got a benchmark to measure yourself against.

Store the whole set in a Google Sheet or Airtable with columns for prompt text, journey stage, and target competitor.

Engines and access methods (be honest about what's reachable)

This is where DIY meets reality, so the table is blunt about what you can and can't get to cleanly:

The honest takeaway from that table: the five frontier-model APIs (OpenAI, Anthropic, Gemini, Perplexity, Grok) are easy and cheap to script. That part is genuinely pleasant. The pain is the Google surfaces. AI Overviews and AI Mode — which are among the highest-traffic AI answers for informational queries — have no clean official API. They require a paid SERP API (SerpApi's Developer plan runs $75/mo, or you can do per-result scraping at roughly $2 per 1,000 AI Mode results via Apify) or fragile DIY browser scraping with residential proxies. Meta AI is effectively off-limits programmatically. This single gap is the biggest parity difference versus the paid tools, which handle all of these surfaces for you without you ever thinking about it.

Automation layer (an implementable stack)

A realistic, low-code stack looks like this:

1. Scheduler and orchestration: a Python script on a cron job (or a GitHub Action on a schedule), or a no-code workflow in n8n / Make / Zapier if you'd rather not write code. n8n (self-hostable and free) is the best fit for a technical operator.
2. Engine calls: loop your prompt set across the OpenAI, Anthropic, Gemini, Perplexity, and Grok APIs weekly. For the Google surfaces, call a SERP API or skip them.
3. Parsing: for each response, capture the full text, then run a simple detection pass — string/regex match for your brand and each competitor, capture cited URLs (Perplexity returns these natively; for the others, parse the links), and optionally pass the text to a cheap model (something like GPT-5-mini or Gemini Flash) to classify sentiment and confirm whether your brand was the actual subject or just referenced in passing.
4. Storage: Google Sheets (via Apps Script or the API) or Airtable works fine for ≤30 prompts; reach for a small SQLite/Postgres table if you want real history.
5. Dashboard: Looker Studio connected to the Sheet, or a simple Streamlit page. Track visibility % by engine, share-of-voice versus competitors, and citation counts over time.

What to capture

For each prompt × engine × run, log: mention (yes/no), position or prominence (first-named? buried in a list?), cited URLs, sentiment (positive/neutral/negative), and which competitors appeared. Structure it as one row per prompt-engine-date so that trends stay queryable down the line.

Bot-traffic tracking (free)

Here's the part where the Runbook fully matches the paid tools — because the data is already yours.

• AI crawlers (GPTBot, ChatGPT-User, OAI-SearchBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, CCBot): these never appear in GA4 (it's JavaScript, browser-only) and are largely filtered out as bots anyway. You read them straight from your server logs. A one-line grep over your Nginx or Apache logs by user-agent gives you weekly crawl counts per bot and per page. Edge/CDN logs (Cloudflare and the like) work too. Robots.txt governs access, but it's honored voluntarily, so your logs are the real source of truth.
• AI referral traffic (humans clicking citations): this one is visible in GA4. Google launched a built-in "AI Assistant" channel on May 13, 2026. It named ChatGPT, Gemini, and Claude at launch, and by early June 2026 its live Default Channel Group documentation lists five recognized sources — ChatGPT, Gemini, DeepSeek, Copilot, and Grok — while explicitly excluding AI Overviews and AI Mode. It also doesn't backfill historical data. For complete coverage (including Perplexity, and the history you'd otherwise lose) build a custom channel/segment with a referrer regex matching chatgpt.com|chat.openai.com|gemini.google.com|claude.ai|perplexity.ai|copilot.microsoft.com and similar. Search Console's Bing/Copilot data is thin, and Google's AI-surface reporting remains limited.

Cadence and effort

A weekly refresh is the right rhythm for this channel; daily is just noise at this scale. The realistic effort once it's built is 1–2 hours a week to review results, sanity-check anomalies, and re-run anything that failed, on top of an initial 6–12 hours to build and debug the stack. Budget more if you add Google-surface scraping, which has a habit of breaking the moment Google changes a layout.

Rough monthly cost (stated assumptions)

Let's price it honestly, with assumptions stated so you can adjust. Take 25 prompts × 5 frontier APIs × weekly (≈4.3 runs/mo) ≈ 540 answer-generations per month. Assume each generation is roughly 1,000 input + 1,000 output tokens, and add a cheap sentiment-classification pass. Using mid-2026 rates (Perplexity Sonar at $1/$1 per M tokens with search included; Gemini Flash around $0.30–0.50/$2.50–3 per M; Grok 4.1 at $0.20/$0.50 per M; GPT-5.x mid-tier and Claude Sonnet at $3/$15 per M as the pricier ends), it shakes out like this:

• Frontier-model API calls: ~$5–20/month at this volume. Token costs are genuinely trivial at 25 prompts; the output-heavy models (Claude, GPT) dominate the bill.
• Optional Google AIO/AI Mode via SerpApi: +$75/month (the Developer plan). This single line item often exceeds all of your model-API costs combined, and it's the main reason DIY Google-surface coverage is rarely worth it for a solo operator.
• Tooling: $0 (n8n self-hosted, Sheets, Looker Studio all free) up to ~$20/mo (a Make/Zapier paid tier).
• Total: ~$10–40/month if you skip the Google surfaces; ~$85–115/month if you add them via SerpApi.

Honest limitations versus a paid tool

To keep this fair, here's what the Runbook genuinely cannot match. Clean multi-engine parity is a real gap (those Google surfaces again). So is polished historical trend storage and visualization out of the box. Prompt-volume intelligence is missing entirely — Profound's Prompt Volumes has no DIY equivalent, which means you're guessing at which prompts actually matter. Your results may not be UI-accurate, since API output can diverge from what a logged-in consumer sees (Peec's whole UI-scraping approach exists precisely to close that gap). There's a maintenance burden every time models or scrapers change. And there's the time cost, which is the real price of the whole thing.

Where it wins, though, it wins clearly: on cost (it's an order of magnitude cheaper at small scale), on total control over the prompt set and the logic, on full transparency (you know exactly how every number was produced — the precise opposite of the prompt-volume opacity reviewers keep flagging in the paid tools), and on customization to your exact category.

Honorable mentions

A handful of tools didn't make the main five but deserve a place on your radar, depending on your situation.

Ahrefs Brand Radar. The strongest pick if you already live in Ahrefs. It tracks brand visibility across ChatGPT, Perplexity, Gemini, Copilot, and Google AI surfaces against a very large prompt database (cited at 239M+), right inside Site Explorer alongside your backlinks and rankings. Basic access is free to Ahrefs subscribers; custom-prompt tiers run $50–$100/mo, with full multi-platform coverage reported around $699/mo on top of the base Ahrefs subscription (~$828+/mo all-in [Unverified — third-party figure]). It didn't make the main five because it's an add-on to an SEO suite rather than a standalone GEO tool, and it isn't worth adopting Ahrefs solely to get it.

Knowatoa. The cleanest budget agency option. Starter is $59/mo (30 questions), Growth is $199/mo (100 questions, plus API and Looker Studio), and Enterprise starts at $499/mo. Seven-engine coverage and strong reporting integrations (NinjaCat, Looker, Sheets). Excluded from the main five for thinner independent review volume, but a credible cheaper alternative for multi-client reporting.

Rankscale (Rankscale AI). The budget entry point, from around €20/mo with a flexible credit model, tracking ChatGPT, Claude, Perplexity, and Google AI surfaces with a visibility score and an AI-readiness audit. The UX is praised, and shipping is fast and founder-led. Excluded because it's keyword/term-based rather than true prompt-based tracking, with no real action layer — fine for cheapest-possible monitoring, but limited for client deliverables.

Goodie AI. A purpose-built, full-stack GEO platform (monitoring + optimization + attribution) with the broadest published engine coverage in the category (11+, including Amazon Rufus) and custom pricing reported from ~$495/mo. Excluded from the main five because it's enterprise/custom-priced and optimization-heavy rather than a focused monitor for small teams — but it's the most complete platform on offer if you want one system to own the entire GEO loop.

Bluefish AI. The best-funded enterprise entrant after Profound: $68M total funding (a $43M Series B in April 2026 co-led by Threshold and NEA), customers including Adidas, American Express, LVMH, and Ulta Beauty, and coverage across ChatGPT, Gemini, Claude, Perplexity, Copilot, and Amazon Rufus. Excluded because it's explicitly Fortune-500-only, closed-pricing, and structured around multi-team marketing orgs — actively wrong for a solo operator, but worth knowing as the enterprise alternative to Profound.

(Briefly noted and excluded: the Semrush AI Toolkit / Ahrefs ecosystem add-ons for existing suite users; Scalenut/Writesonic, which bundle GEO into content tooling; Daydream, an AI-native SEO agency play with $21M raised that isn't a self-serve monitor; seoClarity, BrightEdge, Conductor, enterprise SEO suites adding AI modules; and the very-low-cost trackers like ZipTie, LLMrefs, Geneo for the curious-on-a-budget.)

Head-to-head comparison table

With all the detail on the table, here's the whole field side by side:

Weighted leaderboard

Now the scores. Each per-dimension figure (1–5) is for the operator persona, multiplied by the rubric weights from earlier. Re-weight it for your own priorities and the ranking may well shift.

Ranked: 1) Peec AI 4.10 · 2) Otterly.AI 3.92 · 3) Manual Runbook 3.55 · 4) Scrunch AI 3.44 · 5) AthenaHQ 3.40 · 6) Profound 3.10.

A few of these races are close enough that a tie-breaker helps. For Peec vs. Otterly: choose Otterly if your budget is genuinely under $50/mo and you just want the cheapest validated start; choose Peec the moment you need more than ~15 prompts and want cleaner competitor and citation data plus founder-level support. For Scrunch vs. AthenaHQ: choose Scrunch if bot and referral traffic analytics are your priority and you're comfortable with the Sitecore ownership change; choose AthenaHQ if specific content recommendations and attribution are what you're after. And remember that Profound ranks last here purely on persona fit. For a funded enterprise team, it would rank first on raw capability.

Build-vs-buy breakeven analysis

This is the decision most small operators actually agonize over, so let's run the numbers properly.

Assumptions, stated so you can re-weight them: operator time valued at $60/hr (the midpoint of the $50–75 range — re-run it mentally at your own rate). Weekly refresh. The Runbook build cost amortized: roughly 9 hours of one-time build ≈ $540, spread over 12 months ≈ $45/mo in year one. Runbook run time: around 1.5 hrs/week ≈ 6.5 hrs/mo ≈ $390/mo in time. Tool subscriptions add near-zero operator time beyond 1 hr/week of review ($260/mo in time, which applies to both paths and so largely cancels out for comparison — but the Runbook stacks the maintenance and build time on top). "Engines" here means the number of AI surfaces you're tracking.

Where the crossovers actually fall. First, time value: if you price your own time at roughly $0 (you've got slack and you genuinely enjoy building things), the Runbook is the cheapest option at every tier, right up until Google-surface scraping fragility starts to bite around 100+ prompts. If you price your time at $60/hr, a paid tool wins from about 25 prompts upward. Second, engine count: the moment you need Google AI Overviews or AI Mode reliably, the Runbook's +$75/mo SerpApi cost plus the maintenance erases its price advantage — at which point you should buy a tool that includes those surfaces. Third, volume: above ~100 prompts, prompt management, history, and QA start to overwhelm a solo DIY effort; above ~500, only a paid platform is genuinely sane. As for which paid tool wins at each tier — Otterly Lite for the smallest, cheapest start; Peec Starter or Pro for the 50–150 prompt sweet spot (the best value in the field); Scrunch or AthenaHQ at the agency/multi-client tier; and Profound or Bluefish only at true enterprise scale.

Persona recommendation matrix

To make this fully actionable, here's the whole thing mapped to who you are:

Limitations and what we couldn't verify

In a category this young and this fast-moving, transparency about the gaps is part of the deliverable. Here's everything that should carry an asterisk.

• Profound pricing is gated. As of June 2026 the public page shows only "customized enterprise pricing." The former public tiers ($99 Starter / $399 Growth) and the enterprise estimates ($2,000–$5,000+/mo) come from third parties and prior-period reporting — treat the current numbers as [Unverified].
• Scrunch AI's status changed mid-research. The Sitecore acquisition (reported at $225M, June 3, 2026) is confirmed by the acquisition release, but post-acquisition pricing, packaging, and standalone availability are not confirmed. All Scrunch pricing here is pre-acquisition. Verify directly before purchasing.
• Per-add-on and mid-tier figures for Otterly (Gemini/AI Mode add-ons $9–$149), Peec (extra-model add-ons +$30–$140), and AthenaHQ ($545–695 mid-tier, $2,000+ enterprise) come from third-party reviews and may not match current vendor pages. Verify on the pricing page before buying.
• Two headline Reddit critiques (the Feb 2026 r/DigitalMarketing "mentions not accuracy" thread; the "no correlation between AI mentions and traffic" multi-month test) are widely cited but I could not verify the original threads. They're reported here only as circulating claims, corroborated on the underlying point by verifiable sources (Search Engine Land's "directional, not absolute truth" framing; industry data showing AI referral traffic is currently a small fraction of total sessions).
• G2 review counts vary by source (Profound is cited as 140+, ~303, and implausibly ~2,065; the ~303 figure from G2's own page is the most reliable). Peec's 5.0/5 sits on a thin review base. All five have minimal Capterra/TrustRadius presence — G2 is where the reviews actually live.
• The "3.2/5 independent hands-on" Profound score is a single reviewer's figure, not a consensus.
• Runbook API cost estimates assume mid-2026 published token rates and ~1,000-token responses; your actual costs scale with response length and model mix. SerpApi/scraping costs for the Google surfaces are the largest variable and the least stable.
• AXP (Scrunch) and prompt-volume accuracy are vendor claims; AXP remained pre-launch as of mid-2026 and should not factor into a decision.
• Engine coverage claims (especially "10+ engines," "115+ languages") are vendor-stated; independent per-engine verification is limited, and API-mediated coverage may differ from what a real user actually sees in each consumer UI.
• GA4's "AI Assistant" channel recognized sources are evolving (three engines named at the May 13, 2026 launch; five listed in docs by early June, excluding Google's own AI Overviews/AI Mode); confirm the current recognized-source list in GA4 before relying on the built-in channel rather than a custom regex.

Frequently asked questions

What is the best GEO monitoring tool for a solo operator in 2026?

There's no single winner, but for budget-conscious solo operators and early-stage B2B founders the best 2026 pick is Peec AI, with clean daily tracking from around $89–95/mo. Otterly.AI Lite at $29/mo is the toe-in-the-water option. Peec also tops the operator-weighted leaderboard at 4.10, ahead of Otterly at 3.92.

What is the cheapest credible GEO monitoring tool?

Otterly.AI's Lite tier at $29/mo is the cheapest credible entry point in the market — 15 prompts across the core four engines, with a 14-day trial and no card required. It's ideal for validating whether AI visibility matters for your category before spending real money. The catch: 15 prompts run out fast, and the jump to Standard is steep ($29 to $189).

Is Profound worth it for a small team?

No. Profound is the most capable, best-funded platform in the category, but in 2026 it repositioned to enterprise-only custom pricing — no self-serve tier, no free trial, every conversation starting with sales. Its features are enterprise-gated and its workflow is analyst-heavy, so it scores lowest (3.10) for the time-poor operator persona despite being best-in-class on raw data.

Should I build my own GEO tracking or buy a tool?

It depends on volume and how you value your time. At about 25 prompts across 2–3 engines the Manual Runbook is cheapest (~$30–60/mo) and wins if you value your time below roughly $60/hr. Between ~25 and ~100 prompts a paid tool like Peec or Otterly usually pulls ahead. Above ~100 prompts or 4+ engines, building it yourself becomes a part-time job — buy the tool.

Does AI visibility actually drive traffic or revenue?

Not provably, yet. There is no strong, public, accounting-grade evidence that lifting your AI visibility score reliably lifts traffic or pipeline, and AI referral traffic is still a small fraction of total sessions for most sites. Treat visibility as a leading indicator and brand-reputation signal, not as proven revenue. Buy monitoring to understand a shifting channel, not because a dashboard number feels valuable.

How does the Sitecore acquisition affect Scrunch AI buyers?

On June 3, 2026, Sitecore acquired Scrunch AI for a reported $225M, so it's now a feature of a large enterprise DXP vendor rather than an independent startup. For small teams this raises real risk of enterprise-ification — pricing, packaging, and roadmap may drift toward Sitecore's enterprise base, and standalone self-serve access is no longer guaranteed. Verify current availability and pricing directly before committing.

Which GEO tool is best for tracking AI bot and referral traffic?

Scrunch AI is best-in-class for AI bot-traffic and referral analytics, with a strong GA4 integration and dedicated agent-traffic analytics. But you can replicate much of it for free: AI crawlers (GPTBot, ClaudeBot, PerplexityBot) read straight from your server logs, and GA4's built-in AI Assistant channel, launched May 13, 2026, captures referral traffic from humans clicking citation links.

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Bottom line: in 2026, the smart-money move for a small B2B team is to start cheap (Otterly Lite or a Manual Runbook), graduate to Peec AI once AI visibility has actually proven it matters for your category, and reserve the enterprise platforms (Profound, Bluefish, post-acquisition Scrunch) for when you have a funded GEO program and the analyst hours to extract their value. Monitor to understand the channel — but remember that the data is directional, the revenue link is still unproven, and the cheapest tool that gets you looking honestly at your AI share-of-voice is usually the right one.