Profound vs. AthenaHQ: How to Compare AEO Tools in 2026

Short answer: AthenaHQ is easier to budget for at entry level because its plan page lists Starter at $295 per month and includes 3,600 monthly credits. Profound may suit teams evaluating its dashboards and agent capabilities, but its live pricing page presents both Trial/Enterprise and Starter/Growth information. Neither platform is the automatic winner. Define the workload, engines, reporting grain, workflow, and procurement requirements first, then confirm the applicable plan and price in writing.

A team tracking 20 prompts across three models once a day can use AthenaHQ’s published definition of one credit per AI response to estimate about 1,800 monthly response checks. That is a capacity-planning estimate, not a guaranteed invoice. This comparison was checked against vendor pages on October 9, 2026. Prices, limits, models, integrations, and security details may change.

The useful comparison is therefore not a feature-count contest. It is a test of whether each product can support the measurement population, operating cadence, content workflow, data destination, and controls your team actually needs.

Compare the measurement, not just the feature list

Brand visibility describes how often a brand appears in tracked answer-engine responses. A citation is a source referenced in a response, and it may not mention the brand. Share of voice and citation share are aggregates calculated over a defined population. They are not individual prompt observations and should not be treated as interchangeable with mentions or citation rate.

Profound’s help documentation describes dashboard measures including Visibility Score, Share of Voice, Citation Share, Sentiment, and FactCheck. Its setup guide says to configure categories, topics, and prompts before building dashboards. AthenaHQ lists monitoring across 11 named Starter models: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Claude, Copilot, Grok, DeepSeek, Meta AI, and Mistral. These are vendor-listed coverage claims, not proof that every model behaves identically across regions, dates, or interfaces.

For a meaningful comparison, hold the prompt population, engine and model variant, region, language, competitor set, and aggregation period constant. If one changes, create a new population version and label the resulting data accordingly. HubSpot’s AI visibility documentation is a useful reference for the distinction between visibility, mentions, and citations.

This measurement contract is an internal reporting recommendation, not a vendor export schema. A cross-platform warehouse should use separate grains: one row per prompt definition, one per run observation, one per citation, and one per aggregate. A hypothetical observation and citation could be represented as follows:

{
  "run_observation": {
    "source_system": "vendor-name",
    "prompt_version_id": "prompt-v3",
    "engine": "ChatGPT",
    "model_variant": "vendor-reported variant",
    "region": "US",
    "language": "en",
    "run_started_at": "2026-10-09T12:00:00Z",
    "vendor_run_id": "illustrative-id-123",
    "response_status": "complete"
  },
  "citation_record": {
    "run_observation_id": "illustrative-id-123",
    "normalized_citation_url": "https://example.com/article",
    "citation_ordinal": 1
  }
}

The values and identifiers are illustrative. A run identity must distinguish prompt version, engine, model variant, region, language, and run time. A citation identity must also distinguish multiple citations in one response. For concurrent imports or retries, enforce a database uniqueness constraint and use an atomic upsert or equivalent transactional conflict handling. A read-then-create check is not safe as the sole deduplication mechanism.

Understand engine coverage and plan boundaries

Profound’s pricing page describes a seven-day Trial with 50 unique prompts per day across ChatGPT, Gemini, and Google AI Overviews. The page says Trial prompts come from a recommended set and cannot be customized. Its Enterprise plan lists up to nine answer engines: ChatGPT, Perplexity, Google AI Mode, Gemini, Copilot, DeepSeek, Claude, Google AI Overviews, and Exa Search. Confirm which engines are included in the proposed account configuration.

AthenaHQ lists the 11 named Starter models above and says additional models may be available on request. Before comparing coverage, identify which engines could change a business decision. Use referral data, server logs, CRM source records, customer interviews, or sales-team input to guide that selection. The largest advertised engine count is not itself a measure of monitoring quality. Record engine and model variant separately where the product exposes both.

Model the real cost of monitoring

AthenaHQ lists Starter at $295 per month, advertises annual billing at 17% off, and includes 3,600 monthly credits. The plan page defines one credit as one AI response. API access and extra credits are paid add-ons. The page also displays a $300 per month free-credit label whose meaning should be clarified before purchase. Estimate response-check demand with prompts per run × models per run × runs per period, then reserve capacity for other product use and billing behavior that has not been confirmed.

For example, 20 prompts × 3 models × 30 daily runs equals an illustrative 1,800 response checks per month. Confirm with AthenaHQ how retries, failed responses, prompt variations, and content-agent tasks consume credits before using that estimate as a budget commitment.

Profound says AI Marketer and Agent credit use varies by task complexity. Accounts may be configured for overage billing or usage suspension when an allowance is reached. Those credits should not be converted into prompt-equivalent units. The same Profound page displays Starter and Growth price blocks alongside Trial/Enterprise packaging, so request a written offer that identifies the applicable structure.

Assess the path from measurement to action

Compare the operating loop your team will actually run: measure responses and sources, verify a meaningful gap, update owned content or facts, then monitor again. Ask each vendor to demonstrate the loop using one of your prompts and a real content owner, not just a feature tour.

Profound help material documents configuring categories, topics, and prompts, creating dashboards, filtering and sharing them, and exporting dashboards as PDF. The pricing page lists CSV and JSON exports and API access for Enterprise. A PDF dashboard is not the same as response-level or citation-level data, and the reviewed public material does not establish a complete export schema.

AthenaHQ lists content optimization and self-learning content improvement on Starter. Its Enterprise plan lists features including a Recommendation Engine, Citation Engine, Knowledge Base and claim review, and Oracle discrepancy detection. The public plan page does not document their schemas, algorithms, confidence scores, or workflow states. Confirm which features are included in the quoted plan and ask the vendor to demonstrate the handoff from finding to approved action.

A practical demonstration should show the prompt, answer, citation or source URL, proposed action, assigned content owner, approval point, and follow-up monitoring result. AI may summarize response themes or suggest content changes. Deterministic checks should handle URL normalization, required fields, allowed status values, and duplicate prevention. A person should approve edits to product claims, pricing, or other customer-facing facts. For teams designing bounded agent tasks and human review, see AI agent implementation.

Check export, integration, and security requirements

Profound lists Enterprise CSV and JSON exports and API access. AthenaHQ lists CSV export and API access as an add-on or Enterprise capability. The reviewed public pages did not establish API endpoints, schemas, rate limits, stable record identifiers, or specific data flows. Treat a warehouse or CRM connection as custom integration work until the vendor confirms the access path and data contract. A native turnkey CRM connector was not verified.

Before buying, request a sample export or current API documentation. Check whether it includes response-level records, citations, model identifiers, timestamps, run status, and stable identifiers. If your team needs to move approved findings into a CRM, define the target fields and approval step first. Store the raw vendor payload separately from normalized reporting tables. Do not write AI-generated claims directly into customer or account records without source evidence and human review. CRM systems consulting can help scope a data flow after vendor access and fields are confirmed.

AthenaHQ’s Trust Center publicly lists GDPR and SOC 2 Type I and Type II categories. Profound has a Trust Center, but the reviewed public material did not establish report scope or audit period. For either platform, procurement should review the actual current reports and confirm covered services, audit period, subprocessors, retention, access controls, and contractual scope. A trust-center label alone does not answer those questions.

Use a buyer scorecard to make the final call

Score both platforms against the work your team must do, not against a generic feature count. Run the same representative prompt set and time window where both plans permit it. Record differences in model availability, region, language, and plan limits rather than treating unlike data as a direct performance contest.

Decision area	Profound	AthenaHQ
Entry and price	Live pricing shows Trial/Enterprise and Starter/Growth presentations. Confirm the active offer and applicable quote.	Starter price and monthly credit allowance are published. Confirm annual checkout, the free-credit label, and usage details.
Monitoring scope	Trial: three engines and recommended, non-custom prompts. Enterprise: up to nine listed engines.	Starter lists 11 named models. Confirm model, region, and language availability for the account.
Action workflow	Documented dashboards and Enterprise exports/API. Confirm raw-data access and agent entitlements.	Starter lists content optimization. Confirm Enterprise recommendation, citation, and claim-review features.
Data and procurement	Confirm export fields, API access, and current security-document scope.	Confirm CSV/API terms, data fields, integrations, and current security-document scope.

Choose AthenaHQ first if a published entry-level price, response-based credit allowance, and Starter content features align with your workload. Evaluate Profound if its dashboard and Agent workflow match your team’s needs, but resolve the conflicting pricing presentations before comparing total cost. If either platform cannot demonstrate the required data grain, workflow, or governance controls, keep the decision open rather than inferring capability from marketing language.