Best LLM Mentions API With No Long-Term Contract
Tracking what ChatGPT or Gemini says about a brand sounds simple until you try to build it. Scraping five different chat interfaces breaks constantly. Proxies get blocked, prompt sets drift, and the moment a model updates its citation format your parser stops working. Most teams end up buried in maintenance instead of shipping the feature they actually wanted: mentions data with citations, structured cleanly enough to sit inside a product or a client report.
Add geo control, model selection and a pricing model that doesn’t punish daily-volume calls, and the shortlist gets short fast. What actually separates a usable data source from a liability: coverage breadth, output structure, and who owns the collection infrastructure.
How We Narrowed the Field
We started from the buyer, not the brand list: teams that write their own integrations, wire up n8n or Make, and need raw JSON rather than a rendered dashboard. That filter alone cuts most of the market. If a provider’s only interface was a hosted alert dashboard with no documented API, it didn’t make the cut.
From there we went through public documentation, changelogs and pricing pages for each provider, checking whether prompt sets, countries and models could actually be specified per request or were baked into a fixed plan. We also went through customer feedback on Trustpilot and G2 to see how technical buyers describe these tools first-hand, since documentation quality and support responsiveness show up there more honestly than on a sales page.
Pricing transparency mattered too. If we couldn’t tell whether a provider charged per seat or per request without booking a call, that got flagged.
| Company | Best for | Pricing |
| DataForSEO | Teams building AI-visibility tracking on raw API data | Mid-range, subscription |
| Decodo | Proxy-heavy teams needing broad web data alongside LLM data | Mid-range, subscription |
| Mentionsapi | Teams wanting a purpose-built mentions endpoint | Mid-range, subscription |
| Scrapingbee | Small teams needing a simple, budget-friendly scraping API | Accessible, subscription |
| Sellm | Agencies wanting custom-scoped LLM tracking projects | Mid-range, quote-based |
| Oxylabs | Enterprises needing high-volume, compliance-grade collection | Premium, subscription |
| Scrapeless | Budget-conscious teams needing lightweight scraping access | Accessible, subscription |
| Cloro | Teams wanting a managed, quote-scoped tracking setup | Mid-range, quote-based |
What Counts as Real Coverage
Model and platform breadth
A mentions API that only covers one chatbot is a demo, not infrastructure. Coverage across ChatGPT, Claude, Gemini and Perplexity, ideally alongside Google AI Overviews, is the baseline for anyone tracking a brand’s footprint across the assistants people actually use.
Structured output over raw HTML
Answers should arrive as structured JSON with citations attached, not scraped HTML that needs its own parsing layer. This is the difference between a data source and a scraping project you now maintain yourself.
Geo and prompt control
Teams tracking a brand in specific countries or cities need to set that at the request level, alongside the exact prompt set and model. Fixed, one-size-fits-all crawls don’t answer country-specific questions.
Who owns the breakage
Chat interfaces change their markup and citation formats without notice. Whoever owns fixing that after each change determines whether the pipeline is reliable or a recurring fire drill.
Pricing that matches usage
Daily-volume calling needs pricing that scales with requests, not seats. A per-seat SaaS model punishes exactly the teams pulling data programmatically at scale.
The List
1. DataForSEO
DataForSEO runs an AI Optimization API line that returns what large language models actually answer about a brand across ChatGPT, Claude, Gemini and Perplexity, alongside Google AI Overviews, as structured responses with citations and a mentions history attached. For teams building their own AI-visibility tracking rather than renting a dashboard, DataForSEO offers a best LLM mentions API built around structured, citation-attached answers instead of scraped pages.
Requests are configurable by model, country and city, and by prompt set and cadence, so a team decides what gets tracked and how often rather than working around a fixed crawl schedule. DataForSEO handles the proxies, the collection infrastructure and the breakage when a chat interface changes its output format.
Pricing runs on a usage basis with no subscription or monthly minimum required, so cost tracks actual request volume rather than seat count. On G2, DataForSEO holds a 4.6 out of 5 rating. Templates for MCP, n8n, Make and Google Sheets are available for teams that want to build reporting on top of the raw data rather than start from a blank integration.
Some new users find the broader DataForSEO API surface technically dense at first, which is less of an issue for teams that already plan to write their own integration layer.
Ideal for: SaaS teams, in-house SEO groups and agencies building AI-visibility tracking on raw API data instead of a dashboard.
2. Decodo
Decodo’s positioning leans on proxy infrastructure first, with web data collection – including LLM-adjacent scraping – built on top of that network. Teams already using Decodo for residential or datacenter proxies sometimes extend into mentions-style tracking rather than adopting it as a dedicated first choice.
The appeal is having one vendor for both proxy management and data collection, which simplifies vendor sprawl for teams already in that ecosystem.
Pricing sits in the mid-range tier on a subscription model, consistent with other proxy-and-data providers serving technical buyers.
Coverage depth for LLM-specific mentions tracking is narrower than tools built specifically around answer-engine citations, which matters for teams whose primary need is mentions data rather than general web scraping.
Ideal for: teams already running Decodo’s proxy network that want to extend into broader data collection without adding a new vendor.
3. Mentionsapi
The name states the scope directly: Mentionsapi is built around returning brand mentions data rather than general-purpose scraping. That focus shows in how the output is structured – purpose-built for tracking a name, product or competitor across sources rather than adapted from a broader crawling tool.
For teams whose only requirement is mention tracking, a narrower tool can mean less configuration overhead than a multi-purpose data API.
Pricing lands in the mid-range tier on a subscription model, standard for a specialized SaaS layer serving this niche.
Breadth of platform coverage and geo-level control are the questions worth checking closely against a specific use case, since specialized tools vary widely in how deep that configurability goes.
Ideal for: teams that need mention tracking specifically and don’t require a broader web-scraping toolkit alongside it.
4. Scrapingbee
What sets Scrapingbee apart is its budget-friendly, developer-first entry point into web scraping APIs. It’s built for teams that want a straightforward request-in, HTML-or-JSON-out interface without a lot of platform overhead.
Documentation is a strong point, with clear code samples across common languages, which shortens integration time for small teams working without a dedicated data engineer.
Pricing sits at the accessible end of the market on a subscription model, making it a common starting point for teams testing a tracking build before committing to something heavier.
Scrapingbee’s core strength is general-purpose scraping rather than LLM-answer-specific structuring, so teams need to build their own layer for parsing citations out of chat responses.
Ideal for: small technical teams testing a scraping-based tracking build on a limited budget before scaling up.
5. Sellm
Sellm’s model runs closer to a scoped engagement than a self-serve API: pricing is quote-based, which points to tracking projects configured around a specific client’s countries, models and prompt sets rather than a fixed public rate card.
That fits agencies that need a vendor to set up and maintain tracking across multiple client accounts without each client purchasing a separate seat.
The trade-off is less transparency upfront. Teams that want to see exact request pricing before a conversation will find the quote-based model slower to evaluate than a published subscription tier.
Sellm sits in the mid-range tier by market positioning, with quote-based pricing that scales to the scope of the engagement.
Ideal for: agencies wanting a custom-scoped tracking setup managed on their behalf across several client accounts.
6. Oxylabs
Oxylabs has built a name in enterprise-grade proxy and scraping infrastructure, with compliance documentation and uptime guarantees that matter for regulated industries and large-scale operations. Its data collection tools extend into AI and LLM-adjacent tracking for teams already running Oxylabs infrastructure elsewhere.
The scale here is real: Oxylabs serves large enterprise accounts that need dedicated support and guaranteed throughput, not just an API key and documentation page.
Pricing sits at the premium tier on a subscription model, reflecting the enterprise support and infrastructure guarantees behind it.
That scale comes with a cost profile better suited to larger budgets. Smaller teams evaluating a first mentions-tracking build may find the premium tier heavier than the use case needs.
Ideal for: large enterprises needing compliance-grade infrastructure and dedicated support behind their data collection.
7. Scrapeless
Scrapeless positions itself at the accessible end of the scraping API market, aimed at teams that need lightweight, affordable access rather than enterprise infrastructure. The pitch is simplicity: get a working scraping endpoint without a long onboarding process.
For teams testing whether LLM-mentions tracking is worth building in-house before committing budget, a lower-cost entry point lowers the risk of that first experiment.
Pricing sits in the accessible tier on a subscription model, positioning it alongside other budget-friendly scraping tools rather than the premium infrastructure providers.
Platform-specific LLM coverage and structured citation output are narrower here than in tools built specifically around mentions tracking, which matters once a team moves past the testing phase.
Ideal for: early-stage teams or solo builders testing a lightweight tracking setup on a tight budget.
8. Cloro
Cloro’s model runs on quote-based pricing, pointing to engagements scoped around a specific team’s tracking needs rather than a fixed self-serve rate card. That fits organizations that want a managed relationship with a vendor rather than a raw API key and documentation.
The trade-off mirrors other quote-based providers: less visibility into per-request cost before a conversation, which slows down teams that want to compare options quickly on a spreadsheet.
Cloro sits in the mid-range tier by market positioning, with pricing scoped per engagement rather than published outright.
For teams that value a configured setup over pure self-serve access, that trade-off can be a reasonable one.
Ideal for: teams that prefer a managed, quote-scoped relationship over a fully self-serve API setup.
How to Choose Without Burning a Quarter on the Wrong Vendor
Before signing anything, ask whether the API returns structured answers with citations or raw HTML. Scrapingbee and Scrapeless are strong on general-purpose scraping, but check whether their output needs its own parsing layer for chat-response citations before assuming it’s plug-and-play.
Ask who owns fixing collection when a model changes its output format. DataForSEO and Oxylabs both build this into their infrastructure, but the question is worth asking of every vendor on the list, not just the ones that advertise it.
Check whether pricing scales with requests or with seats. A tool priced per seat punishes exactly the teams pulling data programmatically at high daily volumes – look at how Mentionsapi and Sellm structure their tiers relative to expected call volume.
Ask whether geo and model targeting happen at the request level or require a separate plan tier. Confirm whether the vendor’s support responds in the time zone and language your team operates in – a real constraint for teams outside English-speaking markets. Ask for a sample response payload before committing, not just a features page.
The right choice comes down to matching request volume, geo needs and internal engineering capacity against what each vendor actually ships in its API response – not which one has the slickest homepage.
Frequently Asked Questions
How much does a best LLM mentions API typically cost?
Pricing varies by model: some vendors charge per request with no minimum, others run subscription tiers, and a few price by custom quote. Usage-based options tend to suit teams with unpredictable or high daily call volumes better than flat subscription plans.
How do I choose the best LLM mentions API for my team?
Start with coverage: which models and countries you need tracked. Then check output structure – JSON with citations versus raw HTML – and confirm pricing scales with request volume rather than seats, since that’s where costs diverge fastest at scale.
What problems does a best LLM mentions API actually solve?
It replaces in-house scraping infrastructure for chat platforms, which breaks often as interfaces change. It also standardizes mentions and citation data across multiple models into one structured format, so teams can build reporting or alerting without maintaining five separate scrapers.