Profound vs Peec: Which AI Visibility Tracker Fits Your Team?

Marketing teams that have started tracking AI answers usually arrive at the same shortlist, and the profound vs peec decision is the one that stalls longest. Both tools run a set of prompts against AI engines every day, record whether the brand is named, and report which sources the answer used. Neither vendor publishes an independent head-to-head test proving its citation capture is more accurate than the other's, so an ai visibility tracker comparison built on accuracy claims has nothing solid underneath it. The useful comparison is the shape of the workflow each one supports. Profound is built for depth: source categories, citation share by competitor and topic, watched pages, raw data access, and agent analytics. Peec is built for cadence and delivery: daily prompt monitoring across several engines, source inspection, and client reporting that an agency can hand over without editing.

Profound vs Peec at a glance

Visibility and citation are not the same measurement

The most common mistake in a tracker evaluation is treating a headline visibility number as evidence that the brand's own pages are earning source links. They measure different things, and both vendors document the difference.

Peec defines Visibility as the percentage of AI responses that mention the brand, calculated as responses mentioning the brand divided by total responses, multiplied by 100. Profound's Visibility Score is described in similar terms, as the percentage of tracked responses that include the brand, divided by the responses that include at least one brand. Profound's own guidance calls this a snapshot of presence rather than a measure of citation quality.

A citation answers a narrower question. Peec defines it as a URL retrieved and used in the final answer, and its documentation separates retrieval, where a model fetches a page as a candidate, from citation, where that page is actually referenced in the response. A brand can be mentioned constantly and cited rarely, which is a completely different problem with a completely different fix.

A tracker that supports citation work should expose the prompt and the engine, the answer record, the cited URL and its domain, whether that domain is owned, competitor, earned media, or something else, the frequency and trend over time, competitor citation share, and enough history to tell daily volatility apart from a durable change. Both products cover parts of that list. The depth of coverage is where they separate.

Profound: citation depth, source taxonomy, and agent analytics

Profound is the more expansive platform, and its citation product is the clearest expression of that. Setup is straightforward: add a domain, import a prompt set, and Profound collects citation data across the tracked answer engines daily.

What the citation view then supports is unusually granular. Users can see every cited URL for each monitored prompt, including citations to competitor domains. Sources are grouped into Owned, Competitor, Earned Media, PR Wire, Social, and Institution, which turns a flat list of links into a picture of who is supplying the engine with its evidence. Citation share can be broken down by platform, topic, or prompt, and compared directly against competitors on any of those axes. Day-over-day movement sits alongside a competitor ranking table. Watched Pages tracks individual URLs and reports how their citation volume changes over time, which is the mechanism that connects a specific publishing action to a specific measurable outcome. Earned-media sources can be filtered to find the publishers and authors that get cited repeatedly.

Profound's guidance on reading that data is sensible rather than promotional. Patterns become meaningful over seven to 30 day windows, weekly review is generally sufficient for citation-share trends, and Watched Pages deserves closer attention in the days immediately after a page is published or updated.

Around the citation layer sits Answer Engine Insights, which covers Visibility, Source Citations, Brand Sentiment, and Content AEO. Profound also offers Prompt Volumes, positioned as a way to understand what people actually ask AI, and Agent Analytics, which addresses how AI bots interpret and crawl a site. Agent Analytics gained Bot Visits and Human Referrals nodes in May 2026. Bot Visits covers verified AI-agent activity reaching a connected site, and Human Referrals covers people who arrive by clicking embedded links in AI answers. This is adjacent to citation tracking rather than a substitute for it: citation data names the sources that win in answers, while Agent Analytics connects that activity to what happens on the site.

The integration surface is the other enterprise signal. Profound connects to CDNs including Akamai, Cloudflare, Fastly, and Vercel, to content systems including Adobe Experience Manager, Contentful, Sanity, Webflow, and WordPress, and to analytics including Adobe Analytics, Google Analytics, and Google Search Console, alongside productivity tools such as Slack, Notion, and Linear. The REST API returns raw per-execution prompt-answer rows containing model responses, mentions, citations, and search queries, with report families for visibility, citations, sentiment, query fan-outs, and fact checking.

The trade-offs are real and they concentrate at the bottom of the pricing ladder. Starter is limited to ChatGPT, one seat, and 50 unique prompts, with no export and no API. Growth is four times the price of Starter. API access is request-based on Enterprise rather than a self-serve switch. A broad integration catalogue is an advantage for a mature stack and an implementation project for a team that wanted a prompt dashboard, which is an inference from scope rather than a measured usability finding. Profound also sells FactCheck, a separate accuracy-analysis workflow that compares what engines say against a knowledge base the customer maintains, and it should not be read as part of the citation tracker.

Peec: daily prompt monitoring and agency reporting

Peec approaches the same problem from the operating end, and a peec ai alternative comparison that stops at the metric list will miss why. The setup is choose the AI models, add the competitors, add the prompts. From there the platform reports Visibility, Position, Sentiment, and Share of Voice, updated daily per engine, with mentions, citations, and key sources underneath.

Two design decisions matter more than the metric list. The first is prompt consistency: prompts selected in one month remain the same prompts in later months, which is what makes month-over-month comparison meaningful rather than noisy. The second is collection method. Peec uses UI scraping to interact with the same web interfaces users see, rather than relying only on model APIs. That is a genuine methodological difference and worth understanding, but the vendor's framing of it as fully authentic data is marketing language, not an independently proven accuracy guarantee. The reasonable technical caveat runs the other way as well: an interface-based method may be more exposed to interface changes than an API-based one, which is a consideration rather than a documented reliability problem.

The source layer carries most of Peec's analytical weight. Sources are the websites, articles, and other content that models reference when they mention a brand, and they can be filtered by time period and compared across multiple competitors. Peec's Looker Studio connector exposes brand, country code, date, prompt, source domain, source URL, source URL classification, tag, topic, and workspace, with citation count, retrieval count, and sentiment as metrics, which is enough structure for a team that wants to build its own reporting layer rather than live inside a vendor dashboard.

Agency delivery is where Peec is most clearly differentiated, and it is not a repackaged brand product. Agencies get unlimited client workspaces, each isolated with its own prompts, competitors, and channels, switchable without separate logins or mixed data, and unlimited client seats with no per-seat fee. The reporting side is unusually complete: automated weekly reporting, dashboards comparing visibility, sentiment, and share of voice, plain-language summaries posted into each client's Slack channel, a Google Slides deck with one slide per client, branded Looker Studio dashboards that can be cloned per client, read-only dashboard links clients open without a Peec login, and CSV export. Peec MCP connects the data to Claude, Cursor, n8n, and Make, which supports scheduled tasks that loop through client projects and write summaries against the prior seven days.

Peec has also published research on citation rates, drawn from more than one million AI citations across ChatGPT, Google AI Mode, and Perplexity, with citation rate defined as the ratio of citations to retrievals. The headline findings are directional rather than universal: on Perplexity, 64% of retrieved URLs were never cited in an actual text answer, while 6% of URLs reached a citation rate of 2.0 or higher and accounted for nearly half of all citations. That concentration is a useful mental model for anyone measuring their own program, though it describes Peec's dataset and should not be treated as a target every brand should hit.

The limitations are mostly about disclosure rather than capability. Public material does not give a complete formula for Position. Unlimited users and unlimited workspaces do not mean unlimited prompt volume, because agency plans still run on credits, prompt estimates, project limits, and pitch-project limits. And the public statements about API access genuinely conflict, which is covered below.

Pricing and plan limits

This is the part of any peec vs profound evaluation where headline numbers mislead most reliably, because the two vendors do not price the same unit.

Profound publishes its plans clearly. Starter is $99 per month billed yearly, with 50 unique prompts, 1,500 responses per month, daily frequency, one language and one region, ChatGPT only, one seat, 100 Agent credits per month, and no export and no API. Growth is $399 per month billed yearly, with 100 unique prompts, 9,000 responses per month, three answer engines, three seats, 400 Agent credits, and CSV and JSON export but still no API. Enterprise is custom, and adds up to nine answer engines, multiple companies, custom prompt and response limits, SSO and SAML, SOC 2 compliance, dedicated Slack support, and a dedicated specialist with a 24-hour SLA. Both paid tiers advertise two months free on annual billing, and the page does not state the annual dollar totals separately.

Peec is harder to pin down, which buyers should know before they build a business case. The current public brand pricing page shows the plan structure across Starter, Pro, Advanced, and Enterprise, but the rendered pricing table does not reliably expose numeric prices. Separate vendor information dated July 2026 quotes brand prices of $95, $245, and $495 per month for Starter, Pro, and Advanced, with Enterprise custom, and prompt limits of 50, 150, and 350 respectively. Another vendor-authored page quotes different figures again. Those are dated vendor-stated numbers rather than a current price list, and anyone comparing them against Profound's published tiers should verify at the point of purchase.

Peec's agency track is the clearer of the two. Essential is $245 per month for 10,000 credits, roughly 111 prompts, with three projects and three client seats. Growth is $495 per month for 25,000 credits, roughly 277 prompts, with 10 projects and five client seats. Scale is $795 per month for 65,000 credits, roughly 722 prompts, with 25 projects and seven client seats. Comprehensive is custom with unlimited credits, prompts, projects, and client seats. One credit is one prompt run against one model for one day, so a single prompt tracked on one model for a month consumes 30 credits, and the minimum allocation per project is 900 credits. Annual billing saves approximately 15% on every tier.

The important discipline is not converting these into a single comparison number. Profound publishes unique-prompt counts and monthly response ceilings. Peec's agency plans are credit-based, where the same prompt costs more the more models and days it runs against. A team tracking 40 prompts across four engines daily is buying two different things from the two vendors, and only a workload model shows which is cheaper.

Citation depth compared

For the narrow question of which tool is better for tracking AI citations, Profound has the deeper published feature set. The distinguishing capabilities are the source taxonomy, competitor citation share broken out by platform, topic, and prompt, Watched Pages for individual URLs, and the ability to push raw citation data out through export or the API into whatever analysis the team already runs.

Peec covers the fundamentals of the same job and exposes them more simply. Sources can be inspected by prompt, domain, URL, date, and competitor, the retrieval and citation distinction is documented explicitly, and the Looker connector carries source URL classification through to a BI layer. For a team whose question is which sources are getting cited for the prompts we track and how our position is moving, that is sufficient and faster to adopt. Any peec ai alternative comparison should weigh that adoption speed against the analysis it gives up.

The verdict here is about workflow depth, not data quality. Neither vendor's public material supports a claim that its underlying citation capture is more accurate than the other's, and any comparison that reaches that conclusion is reading vendor language as evidence.

Agency delivery compared

The order reverses for agencies, which is why a peec vs profound shortlist often ends differently inside a client services business. Peec has the clearer public operating model for multi-client work: isolated projects, unlimited users with no per-seat pricing, branded client dashboards, read-only links, weekly automated reports, Slack summaries, Google Slides decks, CSV, and BI feeds. That set of features maps directly onto how agency retainers actually get delivered, and it is priced as a distinct product rather than as an enterprise upgrade.

Profound Enterprise can serve an agency, particularly one running complex marketing infrastructure across multiple companies with governance requirements, and its multiple-companies support plus integration catalogue are genuinely stronger for that case. What the public pricing page does not disclose is a comparable fixed agency plan or the same level of agency-specific delivery detail, which means an agency evaluating Profound is entering a custom sales process rather than reading a price.

The API question cuts across both. Profound's position is consistent: unavailable on Starter and Growth, available on Enterprise, granted on request. Peec's public position is not consistent. Its API documentation describes a beta with access currently limited to Enterprise, while its agency page states API is available on Essential and above. That is a real conflict in the vendor's own material rather than an ambiguity in the reading, and it is worth resolving in writing with a sales representative before an API-dependent workflow is planned.

Platform coverage, plan by plan

Engine coverage is where a homepage list does the most damage to a buying decision. Profound's public product material names ChatGPT, Perplexity, Claude, Gemini, Grok, Microsoft Copilot, DeepSeek, and Google AI Overviews somewhere in the platform. Its pricing page then limits Starter to ChatGPT alone, Growth to three answer engines, and Enterprise to up to nine. The eight-engine list is the platform's ceiling, not any tier's entitlement.

Peec's brand tiers list six default models: ChatGPT, Google AI Overviews, Google AI Mode, Microsoft Copilot, Perplexity, and Gemini. Its agency plans list the same six default AI channels across every tier. Retrieved vendor information also quotes an additional-models add-on at $35, $85, and $165 per month on the brand Starter, Pro, and Advanced plans respectively.

Compared plan to plan rather than homepage to homepage, Peec offers wider default engine coverage at the entry level, and Profound reaches further at the top. A team that needs Google AI Mode and AI Overviews specifically should check that carefully, since AI Mode does not return a ranked list at all, and visibility there has to be measured through citation and mention frequency rather than a conventional position.

The measurement gap both trackers leave

Both products answer the same category of question: where does the brand appear, which sources win, and how is that changing. Neither of them writes the pages that would change the answer. That is not a criticism of either tool, because measurement and production are legitimately separate disciplines, but it is the step that a marketing lead has to plan for after the tracker decision is made. A citation gap identified on Monday still needs a brief, a draft, internal links, and a published page before it closes, and in most teams that work is where the program stalls rather than at the dashboard.

DeepSmith exists at that junction. It tracks AI search visibility across ten engines, attributing citations to the specific pages that earn them and the prompts driving each one, so a gap arrives already named rather than inferred from a chart.

The same platform then produces the article that answers the gap, grounded in stored product, persona and voice context, with internal links, metadata and a cover image built during writing rather than added afterward. Pro is $99 per month and covers ChatGPT, Grow at $199 adds Perplexity, Scale at $399 adds Gemini, and Enterprise covers all ten engines. Teams weighing a tracker against a track-and-produce platform can start a free trial and see real data and real drafts before paying.

Which should you choose

Choose Profound if the organization is an enterprise marketing, SEO, PR, or analytics team that needs source-category analysis, competitor citation share by topic and prompt, watched pages, multiple companies under one account, agent analytics, raw prompt-answer access, or a broad technical integration surface. The trade-off is higher pricing at the tier where the platform becomes useful, narrow lower-tier limits, custom enterprise access, and more implementation than a prompt dashboard requires.

Choose Peec if the team is a lean in-house group that wants daily monitoring across six named AI surfaces without a procurement cycle, or an agency that needs isolated client projects, unlimited users, branded client reporting, and Slack or deck automation. The trade-off is credit-based economics that reward careful workload modelling, incomplete public brand pricing, and conflicting public statements about API access.

Choose neither on a headline visibility score. The question that should decide a profound or peec evaluation is which operating problem the team actually has: citation-source diagnosis, enterprise data integration, prompt-level daily monitoring, or client reporting. Those four problems have different answers, and a visibility percentage does not distinguish between them. An ai visibility tracker comparison is only decidable once the operating problem is named.