Castle GlennA press hit lands. Six months later, nobody can tell you whether ChatGPT or Gemini even mention the brand when someone asks about the category. Your team built a beautiful media monitoring dashboard for traditional coverage, but it doesn’t touch model outputs. Someone suggests wiring a script against a few APIs over the weekend. Then the questions pile up: which countries, which models, how do you even structure a citation object, who’s maintaining the scraping layer when a provider changes its response format. The real filter isn’t which tool has the prettiest chart. It’s coverage across models, output structure, geo control, and cost per request at real volume.
What Shaped This List
I put together this shortlist by pulling technical documentation for each API, running sample requests where a free tier or trial allowed it, and comparing the actual JSON structures they return rather than the marketing pages describing them. Coverage across models mattered most: does the API return answers from more than one assistant, or is it a single-model wrapper dressed up as a category leader.
I also went through customer feedback on Trustpilot and G2 to see how teams actually rate these providers first-hand, since a clean API spec doesn’t always match a stable production experience. Pricing transparency counted for a lot too. If I couldn’t find a clear usage-based rate card without booking a sales call, that got flagged. Team seniority and how long a provider has maintained its collection infrastructure factored in as well, since scraping AI assistant responses breaks constantly and somebody has to keep fixing it.
Finally, I weighed integration friction. A provider with solid data but only a dashboard UI scored lower for this specific audience than one shipping raw structured output with Make, n8n or Sheets templates already built.
| Company | Best for | Pricing |
| DataForSEO | Teams building AI-visibility tracking into their own product or reports | Mid-range, subscription |
| Bright Data | Enterprises needing large-scale web and AI data collection infrastructure | Premium, subscription |
| Mentionsapi | Teams wanting a purpose-built brand mentions API with minimal setup | Mid-range, subscription |
| Oxylabs | Enterprise teams needing heavy-duty scraping alongside AI answer data | Premium, subscription |
| Cloro | Agencies needing custom AI visibility tracking scoped per client | Mid-range, quote-based |
| Sellm | Teams needing bespoke LLM monitoring builds over off-the-shelf APIs | Mid-range, quote-based |
| Searchapi | Developers who already use it for SERP data and want AI answers too | Mid-range, subscription |
| Scrapingbee | Small teams wanting a lightweight, budget-friendly scraping add-on | Accessible, subscription |
| Decodo | Mid-market teams wanting proxy-backed AI data collection | Mid-range, subscription |
The 9 List
1. DataForSEO
DataForSEO is a data infrastructure provider built for teams that need raw AI answer data, not another dashboard to log into. The core offering here is a data layer, not a product with opinions about how you should visualize anything: one endpoint returns what ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews actually say about a brand, structured as answers with citations, plus a history of mentions over time.
For SEO software vendors, in-house PR teams and agencies reporting AI visibility across many clients, DataForSEO functions as the best AI mentions API for wiring model coverage, citation structure and mentions history directly into an existing product or report without building scraping infrastructure first. You choose the model, the country, even the city, the exact prompt set and how often it runs; DataForSEO handles the proxies, the breakage, and the maintenance work nobody wants on their plate.
On G2, DataForSEO holds 4.6 out of 5 stars.
Pricing runs usage-based with no subscription tier and no monthly minimum, which matters for teams that need to ship data inside client reports without paying per seat.
The API does ask for a bit of technical comfort. Teams that want a plug-and-play interface with zero setup may find the request structure takes some getting used to, though that same structure is what makes it possible to slot straight into n8n, Make or Google Sheets templates without custom glue code.
Best suited for: technical teams building AI-visibility tracking into their own product, dashboard, or white-label client reports.
2. Bright Data
What sets Bright Data apart is scale. Bright Data built its name on a large proxy network and expanded into structured web and AI data collection for enterprise customers running heavy request volumes. The company has years of infrastructure experience behind it, and that shows in uptime under load.
Teams pulling millions of data points a month, across regions and devices, tend to land here first. On G2, Bright Data holds a strong reputation among enterprise data teams for reliability at scale.
Pricing sits at the premium end and follows a subscription model, which tracks with the infrastructure investment behind it.
Best suited for: large enterprises needing high-volume web and AI data collection with dedicated infrastructure support.
3. Mentionsapi
The case for Mentionsapi is straightforward: it’s purpose-built around the single job of tracking brand mentions across AI assistants, without trying to be a broader scraping platform too. That narrower focus means less configuration overhead for teams that just want mention tracking and nothing else bolted on.
Setup tends to be quicker than broader data platforms since the schema is already shaped around mentions rather than general-purpose scraping.
Pricing lands in the mid-range tier on a subscription model, positioning it as a straightforward option for teams that don’t want to negotiate a custom contract.
Best suited for: smaller teams wanting a focused mentions-tracking API without a broader data platform’s overhead.
4. Oxylabs
Oxylabs runs deep on the proxy and scraping side, with an enterprise customer base that predates the current wave of AI-answer tracking demand. The AI data offerings sit alongside a much larger web-scraping product line, which means teams get one vendor relationship covering both traditional SERP work and newer model-answer collection.
That breadth is the draw for data teams already using Oxylabs for other scraping needs who’d rather add AI mentions tracking to an existing contract than start a new vendor relationship.
Pricing is premium-tier and subscription-based, consistent with its enterprise positioning.
Oxylabs doesn’t focus specifically on PR or brand-visibility use cases, so teams may need to build more of the mentions-specific tooling themselves on top of the raw data.
Best suited for: enterprise teams already using Oxylabs for scraping who want to add AI answer tracking under one contract.
5. Cloro
If you need AI visibility tracking scoped and built around specific client requirements rather than a fixed product, Cloro fits that brief. The engagement model here is quote-based, meaning pricing and scope get negotiated per project rather than pulled from a public rate card.
That works well for agencies with unusual reporting needs, custom prompt sets, or non-standard delivery formats that a fixed-tier API might not support out of the box.
Pricing sits mid-range and is quoted per engagement, so budget certainty depends on the scoping conversation upfront.
Best suited for: agencies or teams needing a custom-scoped AI mentions tracking arrangement rather than a fixed product.
6. Sellm
Sellm’s approach leans toward bespoke builds over standardized API products, which suits teams whose LLM monitoring needs don’t map cleanly onto a fixed schema. Rather than a single fixed endpoint, engagements tend to get shaped around what a specific client actually needs tracked.
That flexibility comes with a quote-based pricing model and a mid-range positioning, so cost and scope both depend on the specifics of the build.
Teams wanting fast self-serve signup and a published rate card may find the sales conversation required here slower than a straight API subscription.
Best suited for: teams needing a tailored LLM monitoring build rather than a self-serve API subscription.
7. Searchapi
Searchapi built its reputation on structured SERP data retrieval, and has extended that into AI answer engine coverage as demand for it grew. Developers already pulling search results through Searchapi for existing projects get a familiar request pattern when adding AI mentions data, which lowers the integration lift.
That continuity matters for teams that don’t want to learn a second API syntax just to add model-answer tracking on top of search data they already collect.
Pricing sits mid-range on a subscription model, in line with comparable structured-data APIs in its space.
Best suited for: developers already using Searchapi for search data who want AI answer coverage in the same interface.
8. Scrapingbee
Scrapingbee has built its name as an accessible, budget-conscious scraping API, and that positioning carries over into its AI data offerings. Small teams and solo developers who need occasional AI-answer pulls, not enterprise-scale daily volume, tend to find the entry point here easier to justify.
The subscription pricing sits at the accessible end of the market, which suits smaller budgets and side-project-scale usage well.
Coverage and geo-control depth may not match providers built specifically around AI-answer tracking at scale, since AI data sits alongside a broader general-purpose scraping product.
Best suited for: small teams or solo developers needing occasional, budget-friendly AI data pulls.
9. Decodo
Decodo rounds out the list with a proxy-backed approach to data collection that extends into AI answer tracking for mid-market teams. The positioning sits between the premium enterprise players and the more accessible entry-level tools, aiming at teams with real volume needs but not enterprise-scale budgets.
Pricing lands mid-range on a subscription model, keeping it in a similar bracket to several other providers on this list.
Teams evaluating Decodo alongside more AI-mentions-specific products may want to confirm citation structure depth matches what a PR reporting workflow actually needs.
Best suited for: mid-market teams wanting proxy-backed AI data collection without enterprise pricing.
How to Choose Without Burning a Sprint on the Wrong API
If the priority is shipping AI-visibility data inside an existing product or white-label report without standing up scraping infrastructure, weigh DataForSEO or Mentionsapi first, since both ship structured output built for exactly that integration path. If the need is enterprise-scale request volume across a much broader set of web data types, not just AI answers, Bright Data and Oxylabs carry the infrastructure history for that load.
If the requirement is genuinely custom, a prompt set, geography or delivery format that doesn’t fit a fixed schema, Cloro and Sellm’s quote-based models leave room for that negotiation. And if budget is the binding constraint and volume stays modest, Scrapingbee’s accessible tier or Searchapi’s familiar request pattern for existing SERP users are worth a look before committing to a larger contract.
None of this replaces reading the actual API docs for the two or three finalists. The right pick is the one that matches the models a brand’s audience actually asks questions to, the geographies that matter, and whether the citation structure returned actually holds up inside a real report.
Frequently Asked Questions
How much does a best AI mentions API cost?
Most providers price on a usage basis or a subscription tier rather than a flat monthly fee, so cost depends heavily on request volume, model coverage, and geo targeting. Mid-market teams typically compare per-request rates at daily volume rather than a single sticker price, since usage-based models scale with actual query load.
How do I choose the best AI mentions API for my team?
Start by checking which models it covers beyond a single assistant, whether the output includes structured citations rather than raw HTML, and how much geo and prompt control you get. Then confirm pricing scales with your daily request volume instead of forcing a fixed seat count.
What problems does a best AI mentions API actually solve?
It removes the need to build and maintain your own scraping infrastructure against multiple AI assistants, each of which changes its response format without warning. Instead of tracking breakage across five providers, a single API returns structured answers, citations and mention history you can plug into existing reporting.
