Profound vs Peec vs Otterly: AI Visibility Tool Pricing

The head-to-head people actually search — Profound vs Peec vs Otterly — is a pricing question wearing a product question's clothes. All 3 tools do the same category of work: run prompts against answer engines on a schedule, record whether your brand shows up, and chart the trend. What differs is scale, polish, and price — from $29 a month to four figures.

We are the right kind of witness here and the wrong kind of vendor: we run 3 production brands and pay for none of these tools, so nothing in this comparison is an affiliate pitch. No links here earn us anything.

What do Profound, Peec, and Otterly cost?

Entry pricing as published on the vendors' own pricing pages, August 2026:

Three cautions on that table. Prices in this category move quickly — verify against the vendor's page before budgeting, because an August 2026 snapshot will age. Tier limits (prompts tracked, engines covered, seats, markets) differ more than the headline prices do, and the tier you actually need is usually not the entry tier. And we quote only prices here, not the vendors' marketing statistics — vendor-reported performance numbers are not independently verifiable, so they do not appear on this page.

What are you actually buying?

Prompt-sampling infrastructure and a reporting layer. The tool maintains a battery of prompts relevant to your category, runs them against the engines on a schedule, parses the answers for brand mentions and citations, and renders dashboards a stakeholder can read.

The measurement core has a structural limit every buyer should understand: answer engines are non-deterministic. The same prompt produces different answers across runs, sessions, accounts, and locations, so any sampled visibility number carries real variance. That does not make the tools useless — a consistent method applied over time yields a usable trend line, and competitor comparison under 1 method is genuinely informative. It does mean a single-number "visibility score" is a summary of samples, not a fact about the engine. Buy trend lines; do not buy scores as truth.

Worth stating against our own interest as an operator of paid builds: for most small sites, the tracking question is premature. Before anything is earning citations, a dashboard measures zero with great precision. Structure the pages first — that work is the operator's guide — then measure.

What should you ask a vendor before paying?

Five method questions separate a measurement product from a dashboard product, and any of the 3 vendors should answer all of them in a sales call. How many prompt runs produce each reported number, and on what schedule? Are prompts run from clean sessions, or do accounts and history contaminate the sample? Which engines and geographies are actually sampled at your tier — entry tiers often cover fewer than the marketing page implies? How is a "mention" scored when the brand appears without a link, or the link appears without the name? And can you export the raw prompt-level results, so the score can be audited rather than believed?

Vendors with defensible methods answer these plainly. A vendor who treats the sampling method as proprietary magic is asking you to buy a number you cannot check — the exact habit this niche needs less of.

What does the free alternative look like?

The free stack, piece by piece — our own fleet runs on it, with no paid tracker anywhere [our data]:

A GA4 AI-referral channel. A custom channel group that isolates sessions referred by ChatGPT, Perplexity, Copilot, Gemini, and peers. We built and shipped this on our insurance build; the method is documented in how to track ChatGPT traffic in GA4, and Semrush's guide describes the same GA4 approach (Semrush, ai-referral-traffic).

Search Console mining. Google exposes no AI Overview report, so our GSC miner approximates from query and impression patterns — the method in AI Overview impressions in Search Console.

A manual prompt battery. A fixed list of category prompts run against the major engines once a month, with brand appearances logged to a spreadsheet — the procedure in how to measure AI share of voice free. It is the same sampling the tools automate, at smaller scale, with full method transparency.

Cost: hours, not dollars. For network-level context on which bots are fetching at all, Cloudflare's Radar bot insights are free (Cloudflare changelog, July 2025).

When does paying become the right call?

When reporting is an obligation rather than a curiosity. The honest triggers we would accept for our own operation:

• Client reporting at agency scale. Ten brands × monthly stakeholder reports makes manual sampling the expensive option; the fee buys consistency and presentation.
• Executive visibility programs. When leadership consumes an AI-visibility number quarterly, a tool's consistent method beats a hand-run battery's variance in analyst time.
• Multi-market coverage. Sampling engines across languages and geographies manually does not scale past a handful.

And the trigger that should not move you: fear. Pew measured users clicking a source cited in an AI summary in roughly 1% of summary visits (Pew, March 2025 data) — AI referral traffic is real but small for most sites, and a ROI model built on your own numbers will tell you whether a tracking line item belongs in the budget at all. Start free, prove there is something to watch, then pay for the watching if the reporting load justifies it.