Profound alternatives are easiest to compare when you start with the measurement problem, not a feature list. Define the engines, prompt volume, countries, languages, refresh cadence, reporting access, and operational owner you need. Then test whether each product can produce observations your team can review and use.
This comparison uses public product information researched on October 11, 2026. It verifies selected details for HubSpot AEO, HubSpot AI Search Grader, Semrush, Peec AI, and OtterlyAI. It does not establish a universal best tool or verify every claim about every competitor. AthenaHQ, Bluefish AI, Nightwatch, and Scrunch AI are included as candidates for further evaluation, not independently validated winners.
The main distinction is methodological: a one-time diagnostic, a vendor-generated prompt run, a regional prompt database, and a real-user conversation dataset are different sources of evidence. A useful buying decision therefore asks not only what a tool reports, but also what one row of its data represents.
How to compare Profound alternatives
Write a short buying requirement before opening a demo. Include required engines and model variants, monthly prompt volume, countries and languages, refresh cadence, export needs, retention, and the person responsible for acting on findings. Reject a plan that misses a must-have even when its headline price looks attractive.
| Decision need | Verified starting point | Confirm before purchase |
|---|---|---|
| Free baseline | HubSpot AI Search Grader provides a one-time diagnostic across ChatGPT, Perplexity, and Gemini. | It is a snapshot, not continuous monitoring. |
| Three-engine monitoring | HubSpot AEO documents prompt tracking across ChatGPT, Gemini, and Perplexity. | Beta status, plan-specific limits, and Marketing Hub capacity. |
| SEO plus AI visibility | Semrush AI Visibility Toolkit lists a $99 monthly starting price per domain. Semrush One combines SEO and AI Visibility features. | Domains, prompts, users, access, and included features. |
| Low published entry price | OtterlyAI Lite lists $29 per month and 15 prompts. | Whether add-on engines and prompt limits fit the use case. |
| Defined plan limits | Peec AI publishes prompt, model, project, geography, and reporting limits. | Current price and availability of the required models and access. |
What published plans verify, and what they do not
The plan details below were researched on October 11, 2026. Recheck prices, billing terms, add-ons, and entitlements before purchase because packaging can change.
- HubSpot AEO: HubSpot’s official product catalog lists the standalone product at $50 per month, or $45 per month billed annually, and labels it Beta. The listed standalone allowance is 25 tracked prompts, daily runs across ChatGPT, Gemini, and Perplexity, and 2,500 answers per month. Additional prompt packs are listed separately. HubSpot also says AEO is included with Marketing Hub Professional and Enterprise, but those limits differ from the standalone allowance.
- HubSpot AI Search Grader: The free tool provides a one-time snapshot across ChatGPT, Perplexity, and Gemini. It can help establish a baseline, but it is not a recurring monitoring feed.
- Semrush: The standalone AI Visibility Toolkit is listed at $99 per month per domain. Semrush One combines SEO and AI Visibility features and starts at $199 per month. Confirm plan-specific prompts, domains, users, access, and included features.
- OtterlyAI: Lite is listed at $29 per month with 15 prompts and daily tracking across ChatGPT, Google AI Overviews, Perplexity, and Microsoft Copilot. Unlimited team members are listed. Claude, Google AI Mode, and Gemini are presented as paid add-ons.
- Peec AI: The pricing page publishes plan limits. Starter lists 50 prompts, three selected models, one project, unlimited users, and daily tracking. Pro lists 150 prompts and two projects. Advanced lists 350 prompts, five projects, multi-country support, and Looker Studio integration. Enterprise lists custom prompt tracking, all available models, custom frequency, unlimited projects, API access, and SSO. Prices were not consistently exposed in the reviewed page rendering, so confirm price and terms directly.
- Profound: Current public pages reviewed did not verify the extracted comparison’s exact Starter and Growth prices, seat limits, or engine restrictions. Ask Profound for current written plan terms rather than relying on older comparison claims.
Compare total usable capacity, not just monthly price. Include prompts, answer volume, projects, users, add-on engines, countries, exports, retention, and billing period. Ask the vendor to confirm the complete entitlement for the exact plan and account.
A low monthly price can conceal a small prompt allowance or paid engine add-ons. Convert each shortlist option into usable capacity for your actual prompt set, markets, and reporting schedule before comparing totals.
Compare measurement methods before comparing visibility scores
A vendor-defined visibility score is meaningful only when its inputs and aggregation are understood. A prompt run is one captured response at a particular time. A brand mention is the brand appearing in an answer. A citation is a source attributed to that answer. Visibility and share of voice are aggregates whose meaning depends on the prompt set, engine, model, market, period, and calculation.
Semrush documents a prompt database, regional data, and a response-capture approach based on real requests rather than direct LLM API calls for some reports. That is useful methodology information, not an industry-wide standard. A Semrush database observation, a synthetic prompt run, and a real-user conversation dataset are not interchangeable.
A visibility score is interpretable only with its prompt set, engine, market, time window, capture method, and aggregation rules. For cross-tool comparisons, matched observations are more useful than matching score labels.
For a shortlist test, keep a versioned prompt set and run it in each trial or demo under the same engines and markets where possible. Record raw observations alongside each vendor’s aggregate. When wording changes materially, create a new prompt version instead of overwriting the old one. Otherwise, a change in wording can look like a change in visibility.
A practical shortlist by team need
- Free baseline: Start with HubSpot AI Search Grader and record the assessment date. Use the result to define monitoring requirements, not as a time series.
- Documented three-engine monitoring: Assess HubSpot AEO if ChatGPT, Gemini, and Perplexity cover the requirement. Account for Beta status and confirm the limits for the standalone or Marketing Hub edition.
- SEO and AI visibility together: Compare Semrush One with the standalone AI Visibility Toolkit. Choose based on domain count, prompt capacity, access, and included features.
- Low published entry price: Evaluate OtterlyAI Lite against the 15-prompt limit and paid add-on treatment for Claude, Google AI Mode, and Gemini.
- Structured prompt and project limits: Inspect Peec AI’s current plan matrix, then confirm price and availability for the exact models, geography, reporting, and API or export access required.
- Additional candidates: Ask AthenaHQ, Bluefish AI, Nightwatch, and Scrunch AI for current documentation covering engines, pricing, regional limits, exports, API terms, and retention. Their public positioning can guide a demo, but the reviewed evidence does not verify detailed plan claims.
Limit the shortlist to three tools. Give each the same prompt set and target markets, then record coverage gaps, repeatability, report usability, and export quality. A feature count is not a substitute for a result the team can reproduce and use.
Design an auditable AI visibility data flow
The following is a proposed, vendor-neutral operating design, not a vendor-provided integration or schema. Begin with a vendor-supported report, export, or documented API available on the selected plan. Public product pages reviewed do not establish complete API contracts or direct CRM write paths for the compared tools. Peec lists API access for Enterprise, but public endpoint details and response fields were not verified.
At the raw level, one row should mean one observation: one prompt and version, engine and model variant, market, language, run identity, and timestamp. A citation row should represent one citation occurrence or a deliberately defined normalized citation, with that choice documented. A reported visibility or share-of-voice value belongs to a defined period, prompt set, engine set, geography, and weighting method. A CRM contact or deal event is a separate business record and must not be confused with either an observation or an aggregate.
The following is an illustrative observation record, not a vendor schema. The run ID distinguishes repeated executions, while prompt version distinguishes materially changed wording:
{
"tenant_id": "tenant_01",
"project_id": "project_01",
"prompt_id": "prompt_014",
"prompt_version": 2,
"engine": "Perplexity",
"model_variant": "reported_variant",
"country": "US",
"language": "en",
"run_timestamp": "2026-10-11T09:00:00Z",
"run_id": "vendor_run_014",
"source_record_id": "source_run_014",
"methodology_version": "method_03"
}A canonical observation key should include tenant, project, prompt ID and version, engine, model variant, country, language, run identity, and timestamp or another source-level execution identifier. Do not rely on prompt text plus date. Multiple engines, repeated runs, regions, or model variants can collide. The database must enforce uniqueness and perform an atomic upsert when workers can run concurrently.
For citations, use a child key such as observation ID plus citation ordinal, or observation ID plus normalized URL when repeated identical URLs are intentionally collapsed. Preserve the original citation URL and ordinal when the answer can contain the same domain more than once. For period aggregates, use a separate key such as project, period start, period end, prompt-set version, engine set, geography, and methodology version.
Put a human review gate between findings and business action
Use deterministic rules for required fields, URL normalization, allowed engine names, country and language codes, approved domains, and duplicate detection. These checks are predictable and should not be delegated to a language model. AI can assist with bounded labels such as sentiment, topic, or citation type, but preserve the answer excerpt or a compliant answer hash and mark the label provisional.
An ambiguous brand match should remain in an analytics review queue. The analyst checks the answer and citation, records the reviewer and decision, and only then approves a normalized finding for stakeholder reporting. Missing provenance, conflicting records, low-confidence classifications, or unsupported attribution claims should route to the same queue rather than trigger a CRM update.
Keep observation capture separate from CRM updates. A visibility movement alone does not establish revenue impact. A revenue claim needs an attributable chain from an observation to a cited or linked page, a measured interaction, and a lead, account, or opportunity identifier. Do not send confidential deal notes or customer PII to an AI classifier unless the vendor’s data-processing terms and account configuration permit it.
Teams designing ownership, field mapping, and review steps may find CRM systems consulting relevant. For HubSpot process design, see HubSpot systems consulting. These are consulting services, not promises of a prebuilt connector to an AEO vendor.
Questions to resolve before buying
Ask vendors to answer these questions for the exact plan under consideration. Save the answers in the procurement record rather than relying on a demo screen.
- Which engines, model variants, Google AI surfaces, countries, and languages are included in this plan?
- Are prompts vendor-generated, supplied by our team, based on real-user inputs, or a combination? How are repeated runs counted?
- What are the prompt, answer, project, user, retention, and add-on limits?
- Can the vendor provide a sample export with field definitions, source identifiers, and update cadence?
- Is API access available on this plan? Request documentation or a written specification for authentication, rate limits, and response fields.
- Who reviews ambiguous findings, and what evidence supports any traffic or revenue attribution claim?
Choose the tool whose documented coverage and data grain fit the team’s decision, then run a small matched test before committing. A sound shortlist makes the method, plan limits, source identity, and operational owner explicit. That is what allows a monitoring result to be reviewed without mistaking it for a business outcome.