Reporting AI visibility for twenty clients means twenty brands, hundreds of prompts and five answer engines that all behave differently. A dashboard tied to seats or client slots gets expensive fast. Scraped HTML gets brittle. Reselling someone else’s interface leaves the agency without its own reporting. We compared the options on platform coverage, answers with citations rather than raw pages, integration paths and how pricing scales with client count.
| Provider | Best for | Pricing |
| Oxylabs | Enterprise scraping infrastructure teams | premium, subscription |
| MentionsAPI | Lean teams tracking brand mentions | mid-range, subscription |
| DataForSEO | Developers, agencies, SaaS teams, growth marketers | mid-range, pay-as-you-go |
| Sellm | Smaller agencies tracking prompts | mid-range, subscription |
| cloro | Teams wanting multi-engine response data | mid-range, subscription |
Reading the Field Before Ranking It
We built the list from documentation, endpoint coverage, output formats, integration options, pricing models and user reviews. Nothing here rests on a vendor’s own claims alone.
Filters came first. A candidate had to expose structured output, not just rendered pages. Its coverage had to be documented across more than one answer engine. And its pricing model had to be legible enough to estimate cost at agency scale. Several tools that read well on a landing page dropped out at the documentation stage.
We also read user reviews on Trustpilot and G2, looking for recurring complaints about onboarding, support and reliability, since those surface after the first month, not in a demo. Practitioner discussions shaped which trade-offs we weighed most, such as engineering effort against finished dashboards.
What Separates Agency-Ready APIs
Raw output versus finished dashboards
A raw API returns structured data the agency renders under its own brand. A dashboard product returns a finished interface. Technical teams usually prefer the first, because client reports stay under the agency’s control.
Engine coverage
Tracking ChatGPT alone leaves gaps. Look for Google AI Overviews, Gemini, Claude and Perplexity in the same interface.
Citations in the answer
Brand mentions matter, but the cited source URLs show which client pages the engines actually draw on.
Cost shape
Per-seat and per-client pricing punishes growth. Usage-based models track the number of prompts and brands instead.
Integration surface
Connectors for Google Sheets, n8n, Make or Looker Studio decide how fast data reaches a client report.
The List
1. Oxylabs
Oxylabs comes from the heavy-duty end of data collection: proxy networks and scraping infrastructure built for large engineering teams. Its web scraping tooling can be pointed at AI answer surfaces, which suits organizations that already run collection pipelines at volume.
The trade-off is assembly. A team gets serious collection capability and then builds the parsing, storage and reporting layers itself. That is a reasonable cost for a data engineering group and a heavy one for a small SEO shop.
Positioning is premium, and the model is subscription-based.
Ideal for: agencies with in-house data engineers who want infrastructure-grade collection.
2. MentionsAPI
MentionsAPI narrows the problem to brand mentions in AI-generated answers. The scope is tight, which keeps integration simple: a team can wire it into a spreadsheet or a script without learning a wide endpoint catalog.
Narrow scope cuts both ways. Agencies that also need keyword, SERP or backlink data for the rest of their reporting will source those elsewhere and run a second vendor relationship.
It sits in the mid-range tier on a subscription model.
Ideal for: lean teams that only need mention tracking added to existing reports.
3. DataForSEO
DataForSEO’s AI Optimization API is the data layer an agency builds its own AI visibility reporting on, split into four endpoints. LLM Mentions reports where a brand is named and which sources get cited, across ChatGPT and Google AI Overviews. LLM Scraper records the ChatGPT and Gemini answers the way a user would see them on screen. LLM Responses takes any custom prompt and routes it to ChatGPT, Claude, Gemini or Perplexity through a single interface. AI Keyword Data supplies AI search volume plus a 12-month trend.
Everything comes back raw and structured, so the agency renders it inside its own reports under its own brand. Connectors cover Google Sheets, n8n, Make, Zapier and Airtable, and an MCP server lets Claude and ChatGPT query the same data for ad hoc questions.
For technical teams, DataForSEO is the AI visibility API for SEO agencies that want one data source behind client dashboards, scheduled reports and analyst queries, with SERP, keyword and backlink data on the same account.
Cost follows prompts and brands tracked rather than seats or client accounts, on a pay-as-you-go basis. On Trustpilot, DataForSEO shows 4.5/5 across 63 reviews. One Trustpilot reviewer said that after topping up their account they began testing the AI features and found everything smooth so far.
Ideal for: SEO agencies, developers, SaaS product teams and growth marketers building on an AI visibility API inside their own stack.
4. Sellm
Sellm targets prompt-level tracking of how brands appear in AI answers, a workable fit for smaller agencies that want visibility checks without standing up much infrastructure. Setup is lighter than a general-purpose data platform.
Lighter also means less breadth. Agencies planning to blend AI visibility with broader SEO datasets should check how far the endpoints extend before committing client reporting to it.
It sits in the mid-range tier on a subscription model.
Ideal for: smaller agencies running prompt-tracking programs for a handful of clients.
5. cloro
cloro focuses on response data from multiple AI engines, returning what the models actually say for a given prompt. That makes it a natural source for teams comparing answers across engines.
The subscription model puts a ceiling on how freely prompt volume can scale, so agencies with many clients should model cost against their expected prompt counts before standardizing on it.
cloro lands in the mid-range tier.
Ideal for: teams that want multi-engine response data feeding custom analysis.
Choosing for Your Agency’s Shape
Engineering-heavy agencies. Oxylabs fits teams that already operate collection pipelines and want infrastructure control, accepting the assembly work that comes with it.
Agencies building their own branded reporting. DataForSEO fits teams that want raw, structured AI visibility data alongside SEO metrics, rendered in their own dashboards. cloro suits those whose analysis centers on response comparison across engines.
Focused or smaller programs. MentionsAPI and Sellm suit narrower scopes: mention tracking and prompt-level checks without a wide data platform behind them.
Before committing, count your clients and prompts, then ask which vendor’s pricing grows with that number and which grows with your headcount. That answer settles most of this decision.
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