AI Visibility Monitoring Tools — Profound, Peec, Otterly, Semrush and the "Build Your Own" Option (Comparison)

The short answer before we get to the tables: if you're only just checking whether AI models mention your brand at all — buy nothing, a manual audit plus the free reports (GSC, Bing) will do. If you need regular monitoring on a budget up to a few hundred dollars a month — you're choosing between Otterly, Peec and the Semrush/Ahrefs modules, depending on what you already pay for. If you're enterprise — you're talking to Profound. And if your requirements are unusual (custom evaluation criteria, a non-English market, sensitive data, white-label) — building your own monitoring on the LLM-as-a-judge pattern costs less than you'd think, and that's exactly how I built AnswerLyzer.

The AI visibility tools market raised over $300M in funding within a year, and prices span from $29 to $499+ a month — yet the methodological differences are bigger than the price gaps: some tools query models with synthetic prompts, others build on real queries. I compare Profound, Peec, Otterly, Semrush AI Toolkit, Ahrefs Brand Radar and company, show the free zero layer (GSC, Bing) — and calculate when building your own monitoring on the LLM-as-a-judge pattern pays off, which I know from the inside, because that's what my AnswerLyzer is.

This tool category grew from a niche into a market of its own within months — between summer 2025 and spring 2026 it raised over $300M in funding, with the leader valued at a billion. The reason is simple and I covered it in the zero-click era strategy: as a growing share of purchase decisions happens inside AI answers, companies want to know whether they exist in them. I described the Share of Voice measurement methodology separately; this post compares the finished products — and honestly calculates when none of them is needed.

What these tools actually do — and what they don't

They all run on the same skeleton, dressed up differently:

1. 1.Prompt sampling. The tool regularly asks the models (ChatGPT, Gemini, Perplexity, AI Overviews…) a set of questions from your category — your own or auto-generated.
2. 2.Answer parsing. From each answer it extracts brand mentions, sentiment, position in the recommendation and cited sources (URLs).
3. 3.Aggregation. From thousands of such samples it builds metrics: Share of Voice against competitors, trends over time, a map of cited domains.

And here's the first thing to understand before buying: this is a poll, not a ranking. The same prompt asked twice can return different answers, so every result is a sample from a distribution — which is why sample size and regularity matter, not a single measurement. Second, these tools measure *what the models say* — not *what arrives at your site*. The other half of measurement is AI traffic analytics; only together do they close the loop.

The zero layer: what you get for free

Before spending anything, exhaust the free sources — since June 2026 there are more of them than a year ago:

• Google Search Console — the "Generative AI" report: impressions in AI Overviews and AI Mode. No clicks and no queries, but the visibility trend costs nothing.
• Bing Webmaster Tools — AI Performance: citations in Copilot; the only engine reporting them first-party.
• A manual audit following the methodology from the AI visibility audit: 20–30 real customer questions × 3 models × 2–3 repetitions, results into a spreadsheet. One day's work, zero cost, enough to set priorities.

Signals it's time for a tool: you need weekly instead of quarterly measurement, you track more than 3–4 competitors, you report SoV to the board or clients, you want alerts on changes.

Selection criteria — seven questions before you buy

1. 1.Engine coverage. The minimum is ChatGPT, AI Overviews and Perplexity; check AI Mode, Gemini and Copilot if your customers are there. The market matters too: some tools handle non-English prompts poorly.
2. 2.Where the prompts come from. The biggest methodological split: Semrush generates prompts synthetically, Ahrefs builds on a database of real queries (243M+). Synthetic gives control, real gives volume credibility. Best case: you can add your own set.
3. 3.Frequency and sample size. How many prompts, how many repetitions, how often — that decides whether you see a trend or noise.
4. 4.Depth of analysis. A mention alone isn't enough: you need sentiment, recommendation position and — most valuable — citation sources, because those tell you where to act (which domains feed the answers in your category).
5. 5.API and export. Without an API you can't pull the data into your own dashboard or automate reports.
6. 6.Billing model. Do you pay per prompt, per brand, per engine or per domain? At scale the differences get painful.
7. 7.Real price, not list price. Bolt-on modules require the platform's base subscription — Brand Radar without an Ahrefs subscription doesn't exist, so count the total cost.

The comparison — who each one is for

*Public prices as of July 2026 — this category changes price lists quarterly; verify before buying.

A few sentences of commentary, because the table doesn't say everything. Profound has the deepest citation data on the market (I cite their research — like the 680M-citation analysis — on this blog) and pricing to match the segment. Peec grew into the default mid-market pick: a dedicated tool without enterprise pricing, with agency-oriented features (multi-brand reporting) — if white-label is your only reason to consider building your own, check first whether that's enough. Otterly is the cheapest sensible entry — a small prompt package is enough to learn the category before paying more. Semrush and Ahrefs win on convenience: if you already pay for the platform, the AI module comes "along the way" — shallower than the dedicated tools but often sufficient; just remember the synthetic-vs-real prompt difference. You know this comparison's pattern from n8n vs Make vs Zapier: there's no "best tool", there's the best fit for your budget and existing stack.

The "build your own" option — AnswerLyzer from the inside

There's a scenario where none of the above fits: you want to grade answers by your own criteria (not just "mention yes/no"), you operate in a market with non-English prompts, data can't leave for an external SaaS, or you want to offer monitoring to clients under your own brand. Then you build your own pipeline — and this is exactly the architecture I built AnswerLyzer on:

1. 1.A prompt set — real customer questions from your category, versioned, split by intent.
2. 2.Querying models via API — ChatGPT, Gemini, Perplexity; each prompt several times to average out variance.
3. 3.The judge (LLM-as-a-judge) — a cheap, fast model (Gemini Flash in my case) with a strict instruction grades every answer: was the brand mentioned, in what context, with what sentiment, at what position against competitors. How to design and validate such a judge so it grades reliably is covered in my post on LLM evaluation (evals and LLM-as-a-judge) — it's the heart of the system and the easiest place to get wrong.
4. 4.Aggregation and visualization — SoV charts, sentiment trends, alerts on changes.

The economics are surprisingly kind: with ~100 prompts queried weekly across three models, API costs run to tens of złoty (single-digit dollars) a month, not hundreds of dollars. What you pay instead is build and maintenance time: parsing breaks, model APIs change, the judge needs periodic validation against a hand-graded sample. The practical rule: buy when your needs are standard; build when the measurement itself is meant to be your edge. An agency monitoring dozens of brands under its own banner builds; a single company tracking itself and three competitors buys — or stays on the zero layer.

Measurement traps — what vendors mention more quietly

• Variance isn't a bug. Models answer non-deterministically; a tool showing a "4-point drop" on a small sample is showing noise. Ask about sample size before believing a trend.
• Personalization and location. The answer a signed-in user in Warsaw gets can differ from what a tool receives via API from a server in Virginia. Treat results as an approximation, not a record of every user's reality.
• Synthetic prompts measure a hypothesis. If the tool invents the questions itself, you're measuring visibility in questions nobody asks. Always add your own set drawn from real customer conversations.
• Engines change faster than price lists. We saw it with Reddit citation volatility — one technical change can shift every tool's numbers at once. Monitoring makes sense as a long-term trend, not a daily stock ticker.
• Data without action is a cost. The most common anti-pattern: a company pays hundreds of dollars for a dashboard nobody turns into decisions. Before buying, decide who reacts to this data and how often.

How to choose — four scenarios

1. 1.Zero budget / just starting → manual audit + GSC "Generative AI" + Bing AI Performance. Revisit when measurement becomes a regular need.
2. 2.Already on Semrush or Ahrefs → switch on their AI module and check for a quarter whether the depth suffices. Only missing citation sources or too-shallow prompts justify a dedicated tool.
3. 3.Need dedicated monitoring → Otterly to start at small scale, Peec with several competitors and regular reporting, Profound when budget and requirements are enterprise.
4. 4.Unusual requirements (custom criteria, local market, white-label, sensitive data) → build your own LLM-as-a-judge pipeline; start with 100 prompts and one judge, expand after validation.

A step-by-step implementation plan

1. 1.Run the baseline manual audit (one day) — without it you don't even know what to look for in the tools.
2. 2.Turn on the free reports — GSC "Generative AI" and Bing AI Performance in your regular review.
3. 3.Define the prompt set from real customer questions — the same set serves every tool and your own pipeline.
4. 4.Pick the variant per the four scenarios above — and write down what you expect after a quarter (or the dashboard becomes wallpaper).
5. 5.Test on trials with your own prompt set, not the vendor's demo — check non-English answers and data export.
6. 6.Wire measurement to reaction — who reviews, how often, which SoV changes trigger action (new content, digital PR, entity fixes).
7. 7.After two quarters, count the return — did the data change decisions? If not, step down a tier (cheaper) or redesign the process, not the tool.

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I help choose and implement AI visibility monitoring — from the free zero layer, through tool configuration, to building your own LLM-as-a-judge pipeline like AnswerLyzer. I do this as part of AI optimization (GEO) and AI automation. I teach it in the SEO & GEO course. Get in touch — I'll start with the baseline audit and match the variant to your scale before you sign any subscription.

Worth reading next:

• How to measure brand Share of Voice in AI models
• AI visibility audit — does ChatGPT recommend your brand?
• LLM evaluation — evals and LLM-as-a-judge in practice
• How to measure traffic from ChatGPT, Perplexity and AI Mode