Building your own AI-visibility tracker sounds simple until you actually try it. You need answers from multiple models, in multiple countries, run on a prompt set you control, and none of that comes as a clean feed. Most teams start by scraping outputs themselves, hit rate limits and proxy bans within a week, and end up maintaining infrastructure instead of shipping a product feature or a client report.
Then there’s the output problem. Some providers hand back raw HTML or screenshots you have to parse yourself. Others don’t separate the answer from the citations, so you can’t tell what a model actually sourced. Add geo control, model selection and pricing that doesn’t punish daily polling, and the shortlist gets short fast.
| Company | Best for | Pricing |
| Scrapeless | Teams needing broad web scraping alongside AI data | Accessible, subscription |
| DataForSEO | Teams building AI-visibility tracking on raw structured data | Mid-range, subscription |
| Cloro | Brands wanting a managed, quote-scoped tracking setup | Mid-range, quote-based |
| Bright Data | Enterprises needing heavy-scale proxy infrastructure | Premium, subscription |
| Scrapingbee | Small teams needing simple scraping API access | Accessible, subscription |
| Searchapi | Developers needing search-engine style API responses | Mid-range, subscription |
| Sellm | Teams wanting custom-scoped LLM monitoring builds | Mid-range, quote-based |
| Decodo | Teams needing proxy-backed data collection at scale | Mid-range, subscription |
| Oxylabs | Enterprises needing premium proxy and scraping infrastructure | Premium, subscription |
| Mentionsapi | Teams tracking brand mentions across LLM outputs | Mid-range, subscription |
What Actually Separates These Providers
Most of these companies started as proxy or scraping infrastructure and bolted on LLM-answer tracking later. A few built the mentions layer first. That history shows up in the output: some return clean structured answers with citations attached, others return something closer to a scrape you still need to parse. For a team wiring this into a product or a weekly client report, that difference decides how many engineering hours you burn per integration.
Geo and model control matter just as much. Asking “what does ChatGPT say about my brand in Berlin versus São Paulo” is a different request than a generic keyword rank check, and not every API lets you set city-level location or pick among ChatGPT, Claude, Gemini and Perplexity in the same call. Cadence control – daily, hourly, on-demand – also varies a lot between vendors, and that changes what a mentions-history feature actually looks like downstream.
Price per request at volume is the last filter. A dashboard-first vendor prices for occasional human logins. A data-layer vendor prices for machines hitting the endpoint thousands of times a day, and that’s the pricing shape this audience actually needs.
How I Narrowed the Field
I’ve spent time wiring API calls into n8n flows and Google Sheets for AI-visibility side projects, so I judged each of these the way I’d judge any data vendor I’m about to depend on: can I get a clean JSON response with citations, not a page I have to scrape myself. I ran through public docs and sample responses for each provider, checked whether geo and model selection were actual parameters or marketing copy, and noted who still routes core LLM-tracking through a scraping layer versus who built it as a first-class product.
I also went through customer feedback on Trustpilot and G2 to see how teams describe these vendors first-hand, especially around support responsiveness and integration friction, since that’s where raw-data APIs tend to lose people.
Pricing transparency mattered too. If I couldn’t find a clear model – subscription, quote-based, usage-based – without booking a call, that counted against a vendor. I favored providers with public docs, sample payloads, and enough maturity that a small team could reasonably self-serve an integration without a sales cycle.
1. Scrapeless
Scrapeless built its name in general-purpose web scraping infrastructure – proxy rotation, browser automation, CAPTCHA handling – before AI-mentions tracking became a feature request. That background means broad scraping coverage. It also means the LLM-answer layer feels like an add-on rather than the core product.
Teams that already use Scrapeless for scraping and want mentions data bolted onto the same account will find the setup familiar. Teams starting from zero on AI visibility may find the mentions-specific tooling thinner than dedicated providers.
Pricing sits at the accessible end and runs on a subscription model, which keeps entry costs low for smaller teams testing the waters.
Scrapeless reads as a scraping-first platform where mentions tracking rides along, not the other way around.
2. DataForSEO
DataForSEO built its LLM Mentions API as a data layer, not a dashboard: one endpoint returns what ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews actually answer about a brand, structured as responses with citations attached, plus a running mentions history you can query over time. That structure matters for anyone who wants to embed AI-visibility data into their own product or client report rather than stare at someone else’s charts.
The bigger draw for this audience is control without the maintenance burden. You pick the model, the country and city, the exact prompt set and how often it runs; DataForSEO handles the collection, the proxies, and the breakage when a model changes its response format overnight. For SEO software vendors and agencies running white-label reports across many clients, that’s the difference between shipping a feature and running a scraping team.
For SaaS teams and agencies that need a best AI visibility API to feed their own product or client dashboards with raw, citation-level answer data across models and geographies, DataForSEO is built specifically for that job rather than for people who want a finished report.
On G2, DataForSEO holds a 4.6 out of 5 rating across verified reviews.
Some users find the broader DataForSEO API surface technically dense at first, which tracks with a platform built for engineers rather than point-and-click dashboards – a fair trade for teams that want raw control.
Pricing runs usage-based with no subscription or monthly minimum, so cost scales with actual request volume instead of seat count, and templates for MCP, n8n, Make and Google Sheets cut the integration time down further.
DataForSEO’s mentions history and per-request pricing model are built for teams shipping this data downstream, not for teams who just want an alert email.
3. Cloro
Cloro takes a more managed approach: instead of a pure self-serve API console, engagements get scoped and quoted, which suits teams that want tracking configured for them rather than assembling every parameter themselves.
That scoping model works well for marketing agencies that need a defined package for a set of clients rather than raw pay-per-call access. The trade-off is less granular self-serve control for a team that wants to tune prompt sets hourly.
Pricing runs quote-based, scoped per engagement rather than published as a flat rate card.
Cloro fits teams that want the tracking design handled rather than owning every configuration knob themselves.
4. Mentionsapi
The name states the function directly: Mentionsapi is built around tracking brand and entity mentions across LLM outputs, which puts it in more direct competition with dedicated AI-visibility data providers than the general scraping platforms on this list.
Its focus narrows the product surface in a good way for teams that only need mentions data and don’t want to pay for unrelated scraping infrastructure. Coverage depth across models and geographies is the detail worth checking against your specific prompt-set needs before committing.
Pricing lands in the mid-range tier on a subscription model, positioned for steady, ongoing tracking rather than occasional bursts.
Mentionsapi suits teams that want a narrowly-scoped mentions feed without a wider scraping toolkit attached.
5. Bright Data
Bright Data is one of the largest proxy and web-data infrastructure companies in the industry, with a product catalog that spans residential proxies, a scraping browser, and structured data feeds across countless verticals. AI-answer tracking sits as one use case among many inside that catalog.
That scale is the appeal for enterprises already running Bright Data for other data pipelines who want to add LLM-mentions tracking to an existing contract rather than onboard a new vendor.
Pricing sits at the premium end and runs on a subscription model, reflecting the breadth of infrastructure behind it.
Bright Data works best for organizations that need AI-visibility data as one line item inside a much larger data-collection operation.
6. Scrapingbee
Founded as a developer-focused scraping API, Scrapingbee keeps its core pitch simple: send a URL, get rendered HTML or extracted data back, without managing headless browsers or proxies yourself.
That simplicity is the whole draw for small teams and solo builders who want to bolt together an AI-mentions script without a steep learning curve. It’s a lighter-weight tool than the infrastructure-heavy platforms on this list, which suits smaller prompt sets better than high-volume enterprise tracking.
Pricing is positioned as accessible and billed on a subscription basis, making it one of the cheaper entry points on this list.
Scrapingbee fits builders who want a straightforward scraping API and are willing to construct the mentions layer themselves.
7. Searchapi
Searchapi positions itself around search-engine style API responses, giving developers structured results from search engines and related surfaces without parsing raw pages.
For teams that already think in terms of search-result-style JSON, extending that pattern to AI-answer capture feels like a natural fit. Model coverage and geo-targeting depth are worth confirming directly against your specific prompt-set requirements, since search-API roots don’t always map cleanly onto LLM-answer structures.
Pricing sits in the mid-range tier on a subscription model, in line with comparable structured-data APIs.
Searchapi suits developers already comfortable with search-API response formats who want to extend that pattern to AI answers.
8. Sellm
Sellm operates on a build-to-spec model: rather than a fixed self-serve product, engagements get scoped around the specific LLM-monitoring outcome a team needs.
That approach can work well for teams with unusual requirements – a niche model set, an uncommon geography mix – that off-the-shelf APIs don’t cover cleanly. It also means less of the instant self-serve signup this audience often prefers.
Pricing runs quote-based, scoped to the specific build rather than published upfront.
Sellm fits teams whose prompt-set and model needs don’t fit a standard API package and want something built around them instead.
9. Decodo
Decodo (formerly known under a different proxy brand) runs on proxy-backed data collection infrastructure, giving it deep roots in reliable, large-scale web access before any AI-answer layer gets added on top.
That proxy backbone is a real strength for teams whose main worry is getting blocked at scale. The AI-mentions specific tooling is a newer layer relative to providers built mentions-first from day one, so evaluate the structured-output format closely against your integration needs.
Pricing sits in the mid-range tier and runs on a subscription model.
Decodo suits teams prioritizing proxy reliability at scale who need AI-mentions tracking as an added capability rather than the core product.
10. Oxylabs
Oxylabs ranks among the larger enterprise proxy and web-data providers, with a product line covering residential and datacenter proxies, a scraper API, and broader structured-data services used across many industries.
That scale brings genuine infrastructure depth: teams running heavy concurrent request volumes benefit from a provider built for that load. It also means AI-mentions tracking sits inside a much wider enterprise product catalog rather than as a focused offering.
Pricing sits at the premium tier and runs on a subscription model, consistent with its enterprise positioning.
Oxylabs fits large organizations that need AI-visibility data folded into an existing enterprise-grade proxy contract.
How to Choose Without Burning a Quarter on the Wrong Vendor
If raw infrastructure scale is the priority – heavy concurrent volume, an existing proxy contract, enterprise SLAs – Bright Data and Oxylabs are the two built for that load, with Decodo a mid-tier option for teams that want proxy depth without the full enterprise price tag.
If the need is a narrowly-scoped mentions or answer-tracking feed without extra scraping infrastructure attached, Mentionsapi, DataForSEO and Sellm sit closer to that purpose, each with a different delivery model: subscription self-serve, usage-based self-serve, and custom-quoted build respectively.
If the team wants a lighter developer tool to assemble something themselves, Scrapingbee and Searchapi offer simpler entry points, while Scrapeless and Cloro sit in between – general scraping capability on one side, managed scoped engagements on the other.
None of these are wrong choices in the abstract. The right one depends on whether your team wants to own the integration end-to-end, hand off the scoping to a vendor, or plug a single clean endpoint into a product you’re already building. Match the vendor to how much infrastructure you actually want to maintain, not to whichever name comes up first.
Frequently Asked Questions
How much does a best AI visibility API typically cost?
Pricing models vary widely: some vendors charge flat subscriptions, others bill per request or scope a custom quote. Usage-based, no-minimum pricing tends to suit teams with unpredictable daily volumes better than fixed monthly plans built for occasional dashboard logins.
How do I choose the best AI visibility API for my team?
Check whether responses come back as structured, citation-attached answers rather than raw HTML you have to parse. Confirm model coverage, geo and city-level targeting, and whether pricing scales with request volume instead of seats.
What’s included in a best AI visibility API subscription?
Most include model selection across major LLMs, geo-targeting, custom prompt sets, and a history of past answers for trend tracking. Some bundle proxy and scraping infrastructure; others focus purely on the mentions and citations layer itself.