# AI Visibility Tools Compared: Profound, Peec, Otterly, DIY > What Profound, Peec AI and Otterly measure and charge, what a DIY API run costs, and where the AI Shortlist Index fits for property-ops software. Canonical: https://webpossible.com/ai-visibility-tools/ Source: https://webpossible.com/ai-visibility-tools/ Format: Markdown version for AI agents. The canonical HTML page is at the source URL above. Last verified: 2026-09-04 --- Methodology guide # AI Visibility Tools Compared: Profound, Peec, Otterly, DIY By Ryan York · last verified 2026-09-04 An AI visibility tool sends a fixed set of prompts to one or more AI engines on a schedule, records which brands the answers name and which pages they cite, and charts the result over time. Profound, Peec AI and Otterly sell that as a subscription. A developer with an afternoon can build the same loop against the OpenAI, Perplexity and Gemini APIs plus a SERP provider for a few dollars a month in fees. This guide compares the three products and the DIY route on what their own pages state, prices what the API route costs at the sample size the AI Shortlist Index uses, and gives a checklist for evaluating any of them. Every vendor fact here comes from a page fetched on 2026-09-04; anything a vendor does not publish is marked as such. ## What a tracker measures Under the dashboards, every tool in this category holds a prompt list and runs each prompt against each engine it supports on some cadence. It then extracts brand names and cited URLs from the answers and stores the runs so a chart can be drawn. The prompt list is the part that decides whether the numbers mean anything. All three products let you write your own prompts, and each also sells prompt discovery: Profound’s “Prompt Volumes” claims to show what people ask AI, Peec’s onboarding is built around setting up the prompts that matter, and Otterly has a “Query Fan out Tool” that lists the sub-queries an engine might generate from one prompt. Discovery is useful for finding phrasings you had not thought of. It does not tell you which phrasings a property manager with 300 doors uses, and a prompt set that skews toward generic “best software” questions will overstate whoever has the most marketplace listings. The [measurement framework](/ai-visibility/) covers how the Index builds a set across six prompt types so one phrasing cannot decide the result. Engine coverage is the second variable and the one the pricing pages sort tiers by. Profound’s homepage lists eight engines but its $99 tier tracks ChatGPT only and its $399 tier tracks three. Otterly includes four engines on every plan and sells Claude, Google AI Mode and Gemini as add-ons. Peec’s homepage names ChatGPT, Perplexity and Gemini. If your buyers ask Gemini, a plan that does not include it is measuring a different market. Citations are the third output, and the one worth paying for. All three state they record the sources an answer cites: Profound as “Source Citations”, Peec as “Find Key Sources”, Otterly as domain and URL citations checked daily with link-position changes over time. A citation list tells you which pages carry your mentions, and those pages are where the work is. Sentiment is the fourth. Profound, Peec and Otterly each list it. Otterly describes its version as sorting mentions into recommended enthusiastically, mentioned with caveats, or dismissed outright. Read any sentiment score as a coarse framing signal. A model deciding whether “solid for small portfolios but pricey” is positive is doing the same thing the Index’s cue-based sentiment does, and neither is a review score. ## What no tracker measures None of these tools sees a click. An API answer or a scraped AI Overview sends no traffic anywhere, and none of the OpenAI, Perplexity, Gemini or SerpApi documentation cited in this guide describes any way to see what a real user did after reading an answer. A tracker can tell you that you were named on 40 of 100 prompts and cited from a G2 page. It cannot tell you whether anyone booked a demo. The gap between a mention count and a lead is the same gap that existed between a rank and a lead, and it is wider, because the buyer may never leave the answer. They also do not see the model version behind each run unless they choose to store it. The Index methodology stores the model identifier the API returns with every run because the engines change models without notice, and a chart that moves on the day a model changed is not a chart of your work. Ask any vendor whether the model identifier is stored per run; none of the three states it on the pages fetched for this guide. ## The three tools side by side | Tool | Engines | Prompts per plan | Citations | Sentiment | Competitor view | Geography | Run frequency | Published price | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Profound | ChatGPT only on Starter; ChatGPT, Perplexity, Google AI Overviews on the $399 tier; up to 9 on Enterprise (homepage shows ChatGPT, Perplexity, Claude, Gemini, Grok, Copilot, DeepSeek, Google AI Overviews) | 50 / 100 / tailored | Yes, “Source Citations” | Yes | Not stated as a feature; Enterprise lists “multiple companies tracked” | 1 region, 1 language on the two self-serve tiers | Not published | $99/mo and $399/mo, billed yearly; Enterprise custom | | Peec AI | ChatGPT, Perplexity, Gemini | Not published | Yes, “Find Key Sources” | Yes | Yes, “Add Brands” | “All countries” | Not published | Not published; four tiers, annual billing, Enterprise by sales call | | Otterly | ChatGPT, Google AI Overviews, Perplexity, Copilot on all plans; Claude, Google AI Mode, Gemini as add-ons | 15 / 100 / 400 / custom | Yes, domain and URL citations, daily | Yes | Yes, “Me + Top 5 competitors” | 50+ countries (pricing page), 65+ countries and languages (features page) | Daily on Standard and Premium | $29 / $189 / $489 per month; $25 / $160 / $422 annual; Enterprise from $1,000/mo | | DIY (OpenAI, Perplexity, Gemini APIs + SerpApi) | ChatGPT via the web search tool, Perplexity Sonar, Gemini with Google Search grounding, AI Overviews via SerpApi | Whatever you write | Yes, if you store them | Only if you build it | Only if you build it | Whatever the API parameters allow | Whatever you schedule | Metered; under $5/month in API fees at 25 prompts by 3 runs by 4 engines, before engineering time | _Four routes to the same loop, from each vendor’s own pages fetched 2026-09-04. Otterly publishes the most detail, including daily runs and a $29 entry price; Profound publishes two self-serve tiers at $99 and $399 with one region each; Peec publishes tier names but no prices, prompt counts or cadence; the DIY route costs the least in fees and the most in hours._ Three things stand out in that table. Only Otterly states how often it runs. Only Profound states a region limit, and its self-serve tiers have one. Peec, whose homepage claims 3,000 brands and agencies, publishes fewer numbers than either competitor, which means a buyer has to take the sales call to learn what a prompt costs. The pricing pages for [Profound](https://www.tryprofound.com/pricing), [Peec AI](https://peec.ai/pricing) and [Otterly](https://otterly.ai/pricing) are the sources; check them before quoting a figure, since these pages change. ## The DIY route and what it costs The Index runs 25 prompts per category, three times each, across four engines, over at least two days. That is 75 requests per engine per month per category. Priced against the documentation fetched for this guide, the API bill is small. OpenAI’s pricing page lists the web search tool at $10 per 1,000 calls with search content tokens billed at model rates, so 75 calls is $0.75 before tokens, and gpt-5-nano tokens at $0.05 input and $0.40 output per million add cents. Perplexity’s pricing page lists sonar at $1 per million tokens in and out plus a per-request fee of $5 to $12 per 1,000 depending on search context size, so 75 requests is $0.38 to $0.90 plus cents in tokens. Google’s Gemini API pricing page gives Gemini 3 models 5,000 free Google Search grounding requests per month and $14 per 1,000 after that, so 75 grounded requests cost nothing unless the quota is spent elsewhere. SerpApi’s documentation says the AI Overview content comes from a second request using a page token that expires within one minute of the first search, so each AI Overview capture is two searches, and 150 searches sits inside SerpApi’s free tier of 250 per month; the $25 Starter plan covers 1,000. Add it up and one category at the Index floor costs under $5 a month in fees, or under $30 with a paid SerpApi plan for headroom. A vendor tracking one category at three times the Index sample is still under $50. The fees are the smallest line. The real cost is the code that extracts names with aliases and case rules, the store that keeps every run with its model identifier and cited URLs, the review queue for names the matcher missed, and the report someone has to read. ## What DIY lacks A DIY tracker lacks two things a commercial tool has, and only one of them matters. SERP capture is the harder part. ChatGPT, Perplexity and Gemini each expose a documented API with web search or grounding, priced on the OpenAI, Perplexity and Gemini pricing pages, and the Index reads citations from those APIs as returned data; the Gemini API covers Gemini models and not Google AI Overviews, which is why the Index captures Overviews through a third-party SERP provider and records “no AI Overview” when the query returns none. A DIY tracker inherits that dependency plus the two-request page-token dance, and the SERP provider’s output format is the one thing in the stack you do not control. The [guide to how AI Overviews are triggered and sourced](/google-ai-overviews/) covers why absence is the common result on B2B software queries. UI parity is the other, and it is not worth chasing. A commercial tool ships a dashboard, alerts, competitor overlays, a share-of-prompts chart and a place to invite the agency. A DIY tracker ships a table. If the person reading the numbers is the person who built the pipeline, the table is enough. If the reader is a VP who wants a chart in a deck, the dashboard is what you are paying $189 or $399 a month for, and that is a reasonable trade. The consumer-product gap applies to both routes equally. Otterly’s homepage says outright that a manual search in the ChatGPT app may not match its results because of personalization such as memory and location. That is the honest position. An unpersonalized run measures the baseline; your own signed-in account measures one account. Reading the app rather than an API does not close that gap, which is why the Index captures ChatGPT from the app with memory, custom instructions and chat-history search switched off. Any tool that claims to measure “what your customers see” is measuring a baseline and calling it something else. ## How to evaluate any tool Run these questions past a vendor, or past your own build, before paying for a year. Does the prompt set represent real buyers? Ask to see the prompt discovery output for your category and count how many prompts a property manager would type. A set built from generic “best software for” phrasings measures marketplace presence. A set with problem-led prompts that carry no product words, integration prompts naming a specific property management system, and “alternatives to” prompts naming the incumbent measures the shortlist. The [checker template](/tools/ai-visibility-checker/) has a prompt set shaped that way for hand-running before you buy anything. How many runs per prompt, and over how many days? One run per prompt per day is a common default and it produces a consistency value of nothing, because one run cannot disagree with itself. Three runs in fresh sessions across two days is the floor at which you can tell a stable absence from a coin flip. None of the three vendors publishes a runs-per-prompt figure; ask. Which engines, and on which tier? Match the plan’s engine list to the engines your buyers use, not the homepage’s roster. Profound’s entry tier is ChatGPT only. Otterly’s Gemini is an add-on. Which geography? Profound’s self-serve tiers state one region and one language. Otterly states 50 or more countries. Peec states all countries. For US property-ops software the one-region limit is fine; for a vendor selling into Canada and Australia it is not. Are citations stored per run, and exportable? A tool that shows you the top cited domains this week but cannot hand you the URL list per prompt per run is showing you a summary of data it will not let you keep. Otterly lists API access at 2,000 requests a month on Standard. Ask the others. API model or consumer product? Ask where the answers come from. If the vendor scrapes consumer apps, ask which account, which location and whether memory is off. If the vendor calls APIs, ask which models and whether the identifier is stored per run. Can you leave with your data? A year of runs is the asset. If cancelling the plan means losing the history, the price is higher than the pricing page says. ## Where the AI Shortlist Index fits The [Index](/ai-shortlist-index/) is a published monthly measurement for property-ops software categories. It runs a frozen 25-prompt set three times per engine across ChatGPT, Perplexity, Gemini and Google AI Overviews, stores every run with its model identifier and cited URLs, and publishes mention rate, position, consistency, engine spread and source dependency per vendor. The [methodology](/ai-shortlist-index/methodology/) is public down to the extraction rules, so any figure can be reproduced by anyone with the prompt list and the APIs. That makes it one option among the four in the table, and for a narrow buyer. If you sell maintenance coordination, resident benefits or another property-ops category the Index covers, you get a monthly reading at a defensible sample without buying a tool or writing a pipeline. It is not the winner of this comparison. It covers a handful of categories, it runs monthly rather than daily, it uses its own prompt set rather than yours, and it will not add a prompt because you asked. A vendor that wants its own prompts, its own cadence and its own competitors on a chart needs one of the other three routes, or the [monitoring retainer](/ai-visibility-monitoring/), which delivers a per-edition movement report built from the same run files the Index publishes. Paying for that never affects placement; the Index measures what the engines say and the practice sells the work. The first edition is a useful reminder of what a sample is. The 2026-09 export records one completed run: ChatGPT, one maintenance coordination prompt, captured through the OpenAI API on 3 September 2026, 124 cited or consulted URLs, with reddit.com the top domain at 38. That is one run of one prompt on one engine, labeled as such on the category page, and it is exactly the kind of figure a tracker dashboard would happily chart as a trend. The [definition of the metric the Index ranks by](/share-of-answer/) explains why a denominator of one is a placeholder and not a share. ## What to pick at three sizes One product line, one category. Hand-run the checker template monthly, or subscribe to Otterly’s $29 Lite tier for 15 prompts on four engines if you want a chart. If the Index covers your category, read it and spend the money on the pages it says carry your mentions. Total outlay is under $50 a month either way. Multi-product vendor, three to six categories. The prompt count is what matters now: six categories at 25 prompts is 150 prompts, which is Otterly Premium territory at $489 a month or Profound’s $399 tier plus prompts beyond its 100, for which the page publishes no price. This is also the size at which the DIY route starts to pay, because 150 prompts across four engines at three runs is 1,800 requests a month, under $40 in fees at the prices above once a $25 SerpApi plan is included, and the extraction code you wrote for one category runs on six. Pick DIY if you have a developer who owns it; pick Otterly if you do not. Agency. You need multi-brand, per-client exports and a login the client can hold. Profound’s Enterprise tier lists multiple companies tracked, Peec has an agencies section, and Otterly’s Standard plan lists unlimited team members. The question to ask each is whether the client owns the run history when they leave you. A DIY pipeline solves that by design and costs an agency engineering time instead of a per-seat fee. ## Limits This comparison rests on what three vendors wrote on their own pages on one day. It has no hands-on trial of any tool, no run-level data from any of them, and no way to verify a claimed engine count or cadence. Peec’s absence of published prices is a fact about its pricing page, not about its product. Prompt counts, add-on prices and tier names change; a fetch six months from now will disagree with this table somewhere. The DIY figures are arithmetic on published API prices at the Index’s sample size. They exclude engineering time entirely, and a tracker with no one reading it is a cron job. The Index itself is small, covers property-ops only, and in its first edition has one completed run. Nothing here tells you which tool produces numbers closer to what a real buyer sees, because no tool, including the Index, sees that. ## What changes month to month Vendor pricing and tier contents, which is why the research file behind this page carries a fetch date on every row and the table will be re-fetched each edition. Engine lists and add-on structures, which differ by tier today and are the part of a pricing page most likely to change. API prices and free quotas, which move the DIY arithmetic; Gemini’s grounding price differs between model generations already. The models behind each engine, which is why the only tool worth trusting on a trend is one that stores the model identifier per run. And the Index’s own coverage, which grows by category and by prompt set version, each logged on the Index changelog with the edition it takes effect. ## Related guides - [AI visibility: the measurement framework](/ai-visibility/), the hub above this page, which defines every Index metric. - [Index methodology](/ai-shortlist-index/methodology/), the source of truth for prompt design, capture, extraction and the metric table. - [The metric that replaces share of voice](/share-of-answer/), for the definition and the arithmetic behind a mention rate. - [How AI Overviews are triggered and sourced](/google-ai-overviews/), for why SERP capture is the hard part of any tracker. - [How ChatGPT picks its web search sources](/chatgpt-seo/), for the engine every tool covers first. - [Monthly monitoring](/ai-visibility-monitoring/), the practice offer that reports Index figures per edition. ## Changelog - 2026-09-19: Corrected the consumer-product gap section. The Index now captures ChatGPT from the app rather than the API, with memory and history off, so the baseline-versus-one-account point no longer turns on API against app. The 2026-09 run is labeled as the API capture of 3 September 2026. - 2026-09-04: First published. Part of [the AI visibility measurement pillar](/ai-visibility/). What to do about it: [the practice page](/ai-visibility-monitoring/).