Peec vs AthenaHQ: Which AEO Analytics Platform Wins?

The Peec vs AthenaHQ decision looks like a feature comparison and is really a decision about scope. Both products measure how AI engines answer questions about a brand, and both report mentions, citations, sentiment, competitors, and the sources those answers draw on. They diverge on what a team is expected to do with that data. Peec organizes it into clean, repeatable reporting that an agency can put in front of a client. AthenaHQ builds a wider intelligence layer around it, estimating which prompts carry demand and recommending which content gaps to close first.

Neither product is universally better, so the useful question is which one matches the work a team is actually behind on. This comparison weighs the two on analytics, model coverage, reporting, integrations, and pricing, using each vendor's published materials as of the research date. Read as an AthenaHQ alternative comparison, it also covers a third position that neither occupies: an AEO analytics platform that carries its own findings into finished content. Pricing pages for both vendors are dynamic and have rendered inconsistently between crawls, so treat every figure here as a starting point and confirm it live.

Peec vs AthenaHQ at a glance

Peec: prompt tracking built for agency reporting

Peec describes itself as an AI search analytics product for marketing teams and agencies, and its metric vocabulary is deliberately small. Three numbers carry the product. Visibility is how often the brand is named in AI responses, expressed as a Visibility Score, the percentage of responses that mention the brand. Position measures where the brand ranks against others when it does appear. Sentiment records whether the description is positive, neutral, or negative. Sources are the websites, articles, and other content the engines reference.

The workflow is prompt-first. A team writes or imports the questions it wants tracked, groups them with tags or categories, names the competitors that matter, and then watches visibility, position, sentiment, and source patterns move over time. That structure answers a narrow set of questions well: how often the brand is mentioned, which sources sit behind those mentions, which competitor appears instead, and how the engines describe the brand.

Peec's public product positioning centers on ChatGPT, Perplexity, and Gemini. Current plan materials also expose AI Mode, AI Overviews, Microsoft Copilot, and other selectable models, though the allocation varies by plan and the pages do not render every value consistently. A buyer comparing engine coverage should confirm the exact allocation at purchase rather than working from a headline count.

Where Peec is genuinely strong

The reporting layer is the strongest evidence in Peec's favor, and it is unusually specific for this category. Clean CSV exports handle ad hoc analysis and client delivery. A Looker Studio community connector lets an agency build one dashboard template, clone it per client, and share read-only views without giving every client a login. API access moves the data into BigQuery, Tableau, Power BI, or an internal dashboard.

The Peec MCP integration goes further. It can loop through client projects, pull visibility, sentiment, and share of voice for the last seven days, compare the result against the prior period, and produce a plain-language Slack summary along with a one-slide-per-client deck. MCP connections are documented for Claude, Cursor, n8n, and Make. Agency materials describe no per-seat fees and place reporting, Looker Studio, CSV, API, and MCP access inside the dedicated agency plans.

For an agency whose deliverable is a monthly visibility report across a dozen client brands, that combination is close to a purpose-built answer.

Peec pricing

Peec's brand plans start at $95 per month for Starter, with $80 per month reported for annual billing. Pro is $245 per month, or $205 annually, and Advanced is $495 per month, or $420 annually. Official product instructions report 50 prompts and one project on Starter, 150 prompts and two projects on Pro, and 350 prompts on Advanced. An Enterprise tier is reported from $499 per month with custom terms. The pricing page states that price is driven by tracked prompts and models analyzed, with per-prompt discounts at higher tiers.

Agency plans run on credits rather than prompt counts. Essential is $205 per month for 10,000 credits, Growth is $420 per month for 25,000, Scale is $675 per month for 65,000, and Comprehensive is custom with unlimited credits. Peec illustrates those allowances as roughly 111, 277, and 722 prompts, but that translation is conditional. A credit is an allocation slot set by prompts, models, and frequency, not a consumable balance. Peec's own example is that one prompt against one model for a month costs 30 credits, so three models cost 90 credits per prompt per month. Doubling the model count halves the prompt capacity.

The boundary

Peec's center of gravity is analytics and reporting. The product identifies visibility patterns and the sources behind them, and it automates the reporting motion around them. Published materials do not establish a native article writer, a content calendar, scheduled article generation, a CMS publishing pipeline, or built-in multi-channel repurposing. AthenaHQ's own comparison page characterizes Peec as more dashboard-led, requiring teams to interpret and act manually. That is a vendor positioning claim rather than an independent test, and it should be read as such, but the underlying scope distinction holds up against Peec's own materials.

A Peec buyer should therefore check, for each optimization they expect to run, whether it is a recommendation, an export, an MCP automation, or a task that lands back on a person.

AthenaHQ: brand intelligence with wider model coverage

AthenaHQ positions itself as an AEO and GEO command center rather than a mention tracker. The published scope covers prompt and response analysis, source and citation analysis, competitor insights, mention and share-of-voice reporting, sentiment, citation gaps, hallucination detection, persona targeting by buyer role, and multi-region tracking described across more than 60 countries and languages. Executive dashboards and ROI-oriented reporting sit on top.

Two named components define the intelligence framing. Ask Athena is an agentic copilot that answers natural-language questions using the account's own data, including competitive benchmarks, citation tracking, sentiment scores, and stored buyer personas. AthenaHQ Content is a recommendation engine that identifies citation gaps and maps fixes to the passages and sources the models actually use.

The effect is a product that tries to connect four things: what happened, why it happened, what to prioritize next, and how the result relates to a business outcome. For an enterprise marketing leader preparing board reporting or allocating budget across regions and business units, that chain matters more than a cleaner dashboard.

Model coverage and demand estimation

All AthenaHQ plans are described as including ChatGPT, Perplexity, Gemini, Google AI Overviews, and Microsoft Copilot. Paid plans add Google AI Mode, Claude, Grok, DeepSeek, and Meta AI. The vendor's materials variously describe this as 8-plus, 10, 10-plus, or 11-plus models, so the named list is more reliable than any single count, and availability still depends on plan, add-ons, and current configuration.

The Query Volume Estimation Model, or QVEM, is the capability with no established equivalent on the Peec side. It estimates absolute volume, meaning the number of queries per month, and relative volume, and AthenaHQ describes it as a way to forecast prompt trends and prioritize higher-demand questions. Two qualifications matter. QVEM is an estimate, not observed AI-search volume, and the published materials do not establish a validated accuracy figure. Estimated demand improves prioritization when a team has more candidate prompts than tracking budget; it introduces model uncertainty that a buyer should understand before treating it as a census.

Integrations and attribution

AthenaHQ's named integrations are the widest in this comparison. Google Analytics 4 connects AI visibility to traffic and conversions. Google Search Console supports comparing SEO and AEO performance side by side. Google Ads appears as an attribution and data source. Shopify covers publishing and AI-search revenue attribution. Webflow, Wix, Framer, WordPress, and Payload CMS handle content connections. Looker Studio, scheduled Slack reports, and enterprise BI support for Tableau, Power BI, and Looker complete the reporting surface.

The honest formulation is that AthenaHQ has a broader intelligence-to-action and attribution story than Peec. The qualification is that not every capability sits on every plan. API access, extra credits, enterprise BI, security controls, and parts of the optimization and content feature set are gated or sold as add-ons.

AthenaHQ pricing

The Essential plan is free and includes $25 in credit plus 300 credits, covering ChatGPT, Perplexity, Gemini, Google AI Overviews, and Copilot with unlimited members. Starter is $295 per month with $300 in free credit and 3,600 credits monthly, unlimited seats, and visibility across the wider model set, with API access and additional credits as paid add-ons. Enterprise is custom and adds a knowledge base, claim review, discrepancy detection, SSO, audit logging, and multi-region support.

One credit equals one AI response. That rule is what makes capacity harder to reason about than a prompt count: a prompt run across five models in three locations every day consumes credits for every resulting response, not once per question. A current pricing-page rendering includes a confusing fragment about responses counting four times, which contradicts the plans page, so the multiplier is worth confirming in the live calculator before committing to a tier.

The boundary

AthenaHQ is more expensive at the published entry paid tier than Peec's brand entry tier, and the credit system makes capacity less intuitive. The free Essential plan is genuinely useful for testing but 300 credits do not sustain a program. Starter's 3,600 credits go quickly across many prompts, several models, multiple locations, or daily cadence. The broader scope also carries more configuration work, which is a real cost for a small team without an analyst.

DeepSmith: analytics wired to content production

Peec and AthenaHQ are both answers to the measurement question. DeepSmith answers a different one. It is an AI search analytics and content production platform in one: see where a brand appears in AI answers, find the gaps, and close them with on-brand content from the same data. The distinction is not that DeepSmith has more dashboards. It is that the dashboard is the front half of a workflow that ends in a published article.

The analytics half covers the same ground a buyer would expect from this comparison. Mention rate, citation rate, share of voice, sentiment, and visibility trend, with per-platform breakdowns. Per-prompt mention and citation rates with full answer history. A Pages view showing which owned pages AI actually cites, each page's share of total citations, and the prompts driving them. Competitor citations showing who wins, on which exact pages, for which prompts, and by platform. Discover Prompts generates a starter tracking set from stored product, persona, and buyer-stage context, which shortens the setup step that both other platforms leave to the buyer.

The production half is what neither competitor centers. Content Map crawls the brand's site and its competitors' sites, classifies every page onto a shared topic taxonomy and funnel stage, and exposes coverage gaps, untapped topics, and per-topic depth, re-checking sitemaps every 24 hours. Opportunity Agents read that data and return ideas with the evidence attached, so an idea arrives carrying the competitor page winning the citations and the gap on the brand's own site. Content Studio moves that idea from New Ideas to Planned Content to Produced Content, with the Writer producing a researched, brand-grounded article complete with internal and external links, a cover image, and publish-ready metadata. Autowrite runs the same sequence on a scheduled date with nobody in the app. Produced Content publishes to WordPress, Webflow, Strapi, Sanity, Contentful, or a webhook, and the Apps Library turns the finished article into channel-native versions for LinkedIn, X, Medium, Substack, and newsletters. Deep IQ stores the positioning, products, personas, brand voice, visual guidelines, and content types every one of those steps runs against.

Pricing is fixed capacity rather than credits. Pro is $99 per month, or $80 billed annually, with 20 articles, 50 tracked prompts, 5 seats, and ChatGPT. Grow is $199, or $160 annually, with 40 articles, 100 prompts, 7 seats, and Perplexity added. Scale is $399, or $299 annually, with 90 articles, 200 prompts, 10 seats, and Gemini added. Enterprise is custom and covers all ten named engines. A 7-day free trial runs on real data and real drafts, with no long-term contract.

Two scope differences are worth stating plainly. DeepSmith's entry tiers cover fewer engines than AthenaHQ's stated all-plan coverage, and DeepSmith does not claim AthenaHQ's GA4, Search Console, Shopify, or Google Ads attribution chain. Neither bites the marketing lead whose problem sits after the dashboard, because tracked-prompt capacity on every tier is paired with article capacity on the same plan, and because ChatGPT is where most buyer research begins. For a buyer who must prove AI-sourced revenue inside an enterprise BI stack, AthenaHQ is the narrower and better recommendation. DeepSmith also does not present agency credit plans or cloneable client dashboards as its central proposition, so agencies whose product is the report itself should look at Peec first.

Peec vs AthenaHQ across the axes that decide it

The AthenaHQ vs Peec question does not resolve into one verdict. It resolves into six, and a team's answer depends on which of them it is being judged on.

Analytics clarity goes to Peec. Visibility, Position, and Sentiment is a vocabulary a client understands on the first call, and the prompt-and-source model keeps the reporting loop concrete. AthenaHQ's metric set is broader and correspondingly heavier to configure and interpret, which is a cost a small team pays every month.

Model breadth goes to AthenaHQ. Five named platforms on the free tier and five more on paid plans is the widest published range in the comparison. The adjacent limit is that every added model multiplies credit consumption against a fixed allowance, and the vendor's own pages disagree on the total count, so breadth on the plan page is not the same as breadth a team can afford to run daily.

Agency reporting goes to Peec, clearly. Dedicated agency plans, no-per-seat-fee positioning, Looker Studio templates, CSV, API, MCP automation, and client-facing dashboards match an agency motion feature for feature. AthenaHQ has Slack, Looker Studio, API, and executive dashboards, but no equivalently specific cloneable multi-client workflow or published agency price table.

Brand intelligence and prioritization go to AthenaHQ. QVEM, persona and region dimensions, citation-gap analysis, Ask Athena, and hallucination detection form a genuine intelligence layer rather than a reporting one. The counterweight is that QVEM produces estimated demand, and a prioritization built on estimates deserves a lighter grip than one built on observed behavior.

Attribution and enterprise connections go to AthenaHQ. GA4, Search Console, Google Ads, Shopify, and enterprise BI give it the strongest stated path from AI visibility to revenue reporting. DeepSmith does not claim those specialized integrations, and its advantage on this axis is different in kind: its production workflow starts from the same visibility evidence rather than a separate export.

Price predictability is mixed, and slightly favors Peec. Peec exposes prompt-based brand plans and credit-based agency plans, though dynamic pages have rendered inconsistently and capacity still moves with models and cadence. AthenaHQ's free tier lowers the trial barrier, but per-response credits make monthly cost a function of prompts, models, locations, and frequency all at once. DeepSmith publishes fixed prompt, article, seat, and engine capacity per plan, which is the easiest of the three to model in a budget.

Content action goes to DeepSmith. AthenaHQ recommends optimizations and Peec automates reporting on them, but neither published materials establish a path from evidence to a scheduled, brand-grounded, publish-ready article and out to a CMS. That gap is the reason this comparison has a third entry at all.

Which should you choose?

Choose Peec if the deliverable is the report. Agencies and boutique AEO specialists that sell recurring visibility reporting across many client projects get dedicated agency plans, cloneable Looker Studio dashboards, CSV and API access, MCP-driven Slack summaries, and no per-seat fees. In-house teams that want a small, legible metric set and a low entry price fit here too.

Choose AthenaHQ if the requirement is breadth and evidence for someone above you. Enterprise and commercial teams that need wide model coverage, estimated prompt demand, persona and geographic segmentation, hallucination detection, executive dashboards, and a documented path to revenue attribution through GA4, Search Console, or Shopify will find the strongest published match here. The free Essential tier makes the evaluation cheap to start.

Choose DeepSmith if the measurement is not the bottleneck. Teams that already know which prompts they lose, and are stalled on producing the content that would win them, get the tracking plus Content Map gap analysis, evidence-backed Opportunity Agents, a Writer that produces publish-ready articles, Autowrite scheduling, CMS publishing, and channel repurposing on one plan and one set of stored brand context.

The three-way summary is straightforward. Peec is the better AEO reporting tool for agencies. AthenaHQ is the better AEO intelligence suite for enterprise breadth and attribution. DeepSmith is the better choice for a content team that needs analytics to drive publishing.

Teams that want to see their own visibility data and a finished article from it in the same week can start a DeepSmith free trial, which runs for seven days on real data with no long-term contract.