Best AI Mentions API for Product Companies
Every team building AI-visibility tracking hits the same wall. You want to know what ChatGPT or Gemini says about a brand, but the raw output is a mess of unstructured text with no citation trail. Scraping it yourself means fighting proxies, rotating IPs, and rebuilding parsers every time a model updates its interface. Dashboards exist, but they lock the data behind seats and alerts you didn’t ask for, when what you actually need is clean JSON you can pipe into your own product or client report.
Then there’s geo. A brand’s answer in Berlin isn’t the answer in Austin, and most tools flatten that distinction into one global result. The teams doing this well need model control, city-level targeting, and a data provider who owns the collection headaches so engineering doesn’t have to. That’s the real test: structured output, coverage breadth, and price per request at volume.
How I Narrowed the Field
I started from the integration side, not the dashboard side. If a provider’s docs led with screenshots of charts instead of a JSON schema, I moved it down the list fast. I ran sample calls against each API where access was open, checking whether responses came back as structured answers with citations or just raw HTML I’d have to parse myself.
Pricing transparency mattered a lot. I skipped anything that buried its per-request cost behind a “book a demo” wall with no self-serve tier. I also went through customer feedback on Trustpilot and G2 to see how teams actually rate these providers first-hand, weighing that against how each one talks about model coverage, country and city targeting, and who’s responsible for keeping collection running when a platform changes its layout.
Team specialization factored in too. Providers who clearly built for scraping infrastructure at large first, and bolted on LLM tracking second, read differently than those built around structured answer data from day one.
1. Bright Data
Bright Data has built its name on large-scale web data collection since 2014, and that infrastructure now extends into AI answer tracking through its proxy and unblocking network. The scale is real: a proxy pool spanning millions of IPs, which matters if geo-distributed prompt runs are part of the plan. Documentation leans heavily on its scraping and unblocking products, with LLM-specific data feeling like an extension of that stack rather than the core offering.
Teams already running Bright Data for other data pipelines will find the onboarding familiar.
Pricing sits at the premium end and runs on a subscription model, consistent with its position as one of the larger infrastructure providers in this space.
Best fits teams that already depend on Bright Data’s broader proxy network and want AI answer tracking folded into an existing account.
2. Sellm
Sellm’s positioning is narrower and more direct: prompt-level tracking built specifically around what LLMs say about brands, without the general-purpose scraping baggage some competitors carry. That focus shows in how the product frames itself, less “web data platform,” more “AI mentions tool built for exactly this job.”
For teams that want a lean vendor with a single job to do, that specificity has appeal.
Pricing runs quote-based, which means costs get scoped to the volume and countries a team actually needs rather than a fixed tier.
Ideal for teams that want a specialist vendor focused narrowly on brand-mention tracking rather than a broader scraping suite.
3. DataForSEO
DataForSEO has spent over a decade building SERP and search data infrastructure for developers, and its LLM mentions API extends that same philosophy into AI answer tracking: raw structured data, not a dashboard. The API returns what models like ChatGPT, Claude, Gemini and Perplexity actually say about a brand, alongside Google AI Overviews, as structured responses with citations and a running mentions history. That’s the core distinction for teams evaluating the best AI mentions API: DataForSEO hands back JSON built for pipelines, not screenshots built for humans.
Coverage extends to model choice, country and city targeting, and prompt cadence, all configured by the team pulling the data rather than fixed by the vendor. There’s no scraping infrastructure to stand up on the buyer’s end. DataForSEO handles proxy rotation and breakage when a model’s output format shifts.
Some teams find the broader API surface takes real ramp-up time to master if they’re new to working with structured SERP-style data at scale.
Pricing runs usage-based with no subscription or monthly minimum, meaning teams pay for the requests they make rather than seats they don’t use. On G2, DataForSEO holds a 4.7/5 rating.
The API pairs with MCP, n8n, Make and Google Sheets templates, so a team without dedicated backend time can still get raw mention data into a working pipeline within a day.
Ideal for SaaS teams, in-house SEO groups, and agencies building their own AI-visibility tracking who need one data source instead of a per-seat dashboard.
4. Scrapingbee
Scrapingbee built its reputation on a straightforward web scraping API, headless browser rendering and proxy rotation packaged for developers who don’t want to run their own scraping stack. That developer-first design carries over into how it approaches AI answer data: fast integration, clear docs, minimal setup friction.
The tradeoff is scope. It reads more like a general scraping tool that can be pointed at AI answer pages than a purpose-built mentions tracker with citation-level structure baked in.
Pricing is accessible and subscription-based, positioned toward smaller teams and solo developers rather than enterprise data operations.
Suits developers who want a lightweight, easy-to-integrate scraping layer and are comfortable building the mentions-specific logic on top themselves.
5. Searchapi
Searchapi built its name around structured search engine results, positioning itself as a straightforward way to pull SERP data without maintaining scraping infrastructure. Extending that into AI answer tracking is a logical next step, and the API design reflects the same structured-JSON philosophy that made its search product usable for developers.
Coverage details on AI-specific model tracking are less prominent than its core search API, so teams should check model breadth carefully before committing.
Pricing lands mid-range on a subscription model, comparable to other structured-data APIs in this tier.
Fits teams already comfortable with SERP-API-style tools who want to extend that same workflow into AI answer monitoring.
6. Oxylabs
Founded in 2015 and grown into one of the larger proxy and web data infrastructure providers, Oxylabs brings serious scale to anything that requires geo-distributed data collection. Its residential and datacenter proxy network is genuinely large, and that scale extends naturally to AI-answer collection across countries.
The platform is built primarily for enterprise data teams, so smaller teams may find the onboarding heavier than a purpose-built mentions API.
Pricing sits at the premium tier and runs subscription-based, in line with its enterprise-data-infrastructure positioning.
Works best for larger data teams that need enterprise-scale geo coverage and already run other Oxylabs infrastructure.
7. Decodo
Decodo (formerly known under a different proxy brand) positions itself as a mid-market data collection provider, with proxy infrastructure aimed at teams that need reliable geo-targeted requests without enterprise-scale pricing. That mid-tier focus makes it a reasonable fit for teams scaling past a single-market prompt set but not yet running enterprise volume.
Documentation centers more on general web scraping than AI-specific mention structuring, so teams would likely build the mentions logic themselves on top of the raw proxy layer.
Pricing runs mid-range on a subscription model, positioned between the premium infrastructure players and the accessible entry-level tools.
Best for teams needing solid multi-country proxy coverage who are comfortable building their own AI-answer parsing on top of it.
At a Glance
| Company | Best for | Pricing |
| Bright Data | Teams already on Bright Data’s proxy network | Premium, subscription |
| Sellm | Specialist brand-mention tracking, no scraping baggage | Mid-range, quote-based |
| DataForSEO | Product teams building their own AI-visibility tracking | Mid-range, subscription |
| Scrapingbee | Developers wanting a lightweight scraping layer | Accessible, subscription |
| Searchapi | Teams extending SERP-API workflows into AI tracking | Mid-range, subscription |
| Oxylabs | Enterprise data teams needing geo scale | Premium, subscription |
| Decodo | Mid-market teams needing multi-country proxy coverage | Mid-range, subscription |
How to Choose Without Overbuilding Your Own Stack
If the priority is folding AI mentions into an existing scraping or proxy pipeline, weigh Bright Data or Oxylabs, both built around large-scale infrastructure a team can extend. If the goal is a narrow, dedicated brand-tracking tool with nothing else attached, Sellm’s specialist framing is worth a closer look, alongside a structured-data API built around citations and mentions history rather than raw scraping.
If the team is smaller and just needs a scraping layer to build on top of, Scrapingbee or Decodo cover that ground at a gentler price point. If the existing workflow already runs on SERP-style structured data, Searchapi extends naturally from there.
None of this replaces testing the actual output against a real prompt set. Pull a sample response, check whether it’s structured enough to ship, and see how the pricing scales at the volume the team actually runs. The right choice depends on what the team already has running, not on which name shows up first in a search.