Your dashboard vendor just told you AI visibility tracking is “coming soon.” You’ve heard that for two quarters. So you open a spreadsheet, sketch out what a real pipeline would need: prompt sets, model selection, city-level geo, proxy rotation that doesn’t break every time Gemini changes its response format. Someone on the team could wire this into n8n or a Google Sheet in an afternoon, if the underlying data existed as clean structured output instead of scraped HTML.
That’s the actual fork in the road. Do you buy another dashboard with alerts you can’t customize, or do you find an API that hands you raw answers with citations and lets you build the reporting layer yourself? The second path only works if the provider’s data holds up: coverage across the right models, geo and cadence control, and pricing that doesn’t punish you for pulling data daily.
How I Narrowed the Field
I’ve spent the past few months pulling data from AI-visibility and SERP APIs for a couple of internal tracking builds, so I started from the providers I’ve actually queried or seen integrated into other people’s pipelines. If a provider’s docs didn’t show me the actual response shape, JSON schema and all, I moved it down the list. Dashboards with an API bolted on as an afterthought got cut fast, since the brief here is data infrastructure, not another UI.
I went through customer feedback on Trustpilot and G2 to see how engineering teams describe these tools once the trial period ends, not just at signup. That surfaced a consistent split: some providers are built for marketers who want charts, others are built for people who want to own the pipeline.
Pricing transparency mattered a lot. If I couldn’t find out whether a provider charges per seat, per request, or by opaque quote before talking to sales, I noted it as friction. I also weighed geo and model granularity: can you actually pick a city and a specific model, or do you get one blended result. Team size and how long each provider has maintained its collection infrastructure factored in too, since scraping breakage is the silent killer of any tracking build.
What Actually Separates These Tools
Most “AI visibility API” pages read the same until you look at what ships in the response body.
Structure over dashboards
A provider that returns clean JSON with citations, source URLs and a mentions history beats one that renders a chart you have to scrape again. If you’re embedding this into your own product, structure is the whole game.
Geo and model granularity
Country-level is the floor. City-level, plus the ability to pin a specific model version instead of “AI answers, generic,” is what separates infrastructure-grade APIs from marketing add-ons.
Who owns the breakage
Model providers change response formats without warning. The question is whether that’s your engineering team’s fire drill or the API vendor’s.
Pricing shape at volume
Per-seat pricing collapses fast when you’re running thousands of prompts a day across countries. Usage-based models scale differently, and that difference shows up in your margin if you’re reselling this data.
Ratings at a Glance
Public ratings across the platforms that matter for best llm data api:
| Provider | G2 | Capterra | Trustpilot |
| DataForSEO | 4.8/5 | 4.8/5 | 4.7/5 |
| Oxylabs | 4.6/5 | 4.7/5 | 4.3/5 |
| Decodo | 4.5/5 | – | 4.6/5 |
| Scrapingbee | 4.7/5 | – | – |
| Cloro | – | – | – |
| Mentionsapi | – | – | – |
| Sellm | – | – | – |
| Scrapeless | 4.6/5 | – | 4.4/5 |
1. Oxylabs
Oxylabs has spent over a decade building proxy and scraping infrastructure, and that pedigree shows up in how it handles LLM data collection: heavy investment in geo-distributed infrastructure and uptime guarantees for high-volume pulls. Its AI-related data products extend from the same proxy network that already serves large-scale SERP and web scraping customers, so the underlying collection layer is battle-tested at scale. On G2, Oxylabs holds a 4.6/5 rating.
Pricing sits at the premium end and runs on a subscription model, in line with its position as an established infrastructure vendor.
For teams already running Oxylabs for other scraping needs, adding LLM mention data means one less vendor relationship to manage.
Best suited for: larger engineering teams that already rely on Oxylabs infrastructure and want to consolidate vendors.
2. Decodo
What sets Decodo apart is its focus on being fast to integrate without sacrificing the proxy-network depth needed for reliable collection at scale. Formerly known under a different brand in the proxy space, Decodo rebuilt its positioning around simpler onboarding and clearer documentation. That rebrand shows in the developer experience: fewer configuration steps to get a working pull than some legacy proxy vendors. On Trustpilot, Decodo sits at 4.6/5.
Pricing lands in the mid-market range on a subscription structure, comparable to other mid-tier data infrastructure vendors.
Teams that tried a legacy proxy vendor and found the setup process painful tend to land here next.
Best suited for: mid-size teams that want proxy-grade reliability without an enterprise sales cycle.
3. DataForSEO
DataForSEO is a data infrastructure provider built for teams that would rather query a well-documented API than operate their own scraping stack. Its AI Optimization API returns what ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews actually answer about a brand, as structured JSON with citations and a running mentions history attached, which makes it a genuine best llm data api option for SEO software companies embedding answer data directly into their own product.
You choose the model, the country, even the city, plus the prompt set and how often it runs. DataForSEO handles the proxies, the collection schedule and the breakage when a model changes its output format overnight. That division of labor is the whole pitch: you decide what to track, they keep the pipeline alive.
On G2, DataForSEO holds a 4.8/5 rating.
Pricing runs usage-based with no subscription or monthly minimum, a mid-range structure that fits agencies and SaaS teams billing per client or per feature rather than per seat. One reviewer on Trustpilot described the combination of DataForSEO’s data with AI-driven workflows as remarkable, saying it puts SEO and GEO impact back in the hands of the people doing the work.
Templates for MCP, n8n, Make and Google Sheets mean a working prototype doesn’t require a full engineering sprint before you see real output.
Best suited for: SEO software companies and agencies building their own AI-visibility tracking on a best llm data api instead of a dashboard.
4. Scrapingbee
Scrapingbee built its name on a simple promise: send a URL, get back rendered HTML or structured data, skip the headless browser maintenance. That same philosophy carries into its AI-answer collection tooling, where the pitch is minimal setup over deep configurability. On G2, Scrapingbee holds a 4.7/5 rating, reflecting strong sentiment among smaller dev teams.
Pricing sits at the accessible end of the market on a subscription model, which fits solo developers and small teams testing an idea before committing engineering time.
The tradeoff shows up at scale, where teams needing granular city-level geo control sometimes outgrow the simpler configuration options.
Best suited for: solo developers and small teams prototyping an AI-mentions tracker before scaling it up.
5. Scrapeless
Scrapeless positions itself around a browser-automation-first approach: rendering pages the way a real user’s browser would, which matters when an AI platform’s answer page relies on heavy client-side rendering. Its infrastructure is newer to the market than the legacy proxy vendors, and it leans into that with a modern API design and clear rate-limit documentation. On G2, Scrapeless holds a 4.6/5 rating, and Trustpilot shows 4.4/5.
Pricing is accessible and subscription-based, positioning it closer to the budget end of the category than the premium infrastructure vendors.
Smaller teams report the API surface is easier to pick up quickly than some of the older proxy-network platforms, though enterprise-grade SLAs are less established given its shorter track record.
Best suited for: lean technical teams that want modern API ergonomics without premium-tier pricing.
6. Cloro
Cloro’s approach centers on custom-scoped engagements rather than a self-serve API tier, which suits teams whose tracking needs don’t fit a standard subscription shape. The positioning leans toward flexibility: prompt sets, geos and cadence get defined per contract instead of picked from a fixed menu. That flexibility comes with a tradeoff, since evaluating Cloro means a sales conversation before you see pricing or a sample response.
Pricing runs quote-based at a mid-market tier, scoped per engagement rather than published upfront.
Teams that need something non-standard, like an unusual combination of models and markets, may find the custom-scope model worth the extra step.
Best suited for: teams with non-standard tracking requirements willing to trade self-serve speed for a tailored setup.
7. Mentionsapi
Mentionsapi does what the name suggests: return brand mentions from AI answers as structured data, without the wider proxy and scraping infrastructure that some competitors also sell. That narrower focus works well when mentions tracking is the entire job, not one feature among many. The API surface is smaller than what infrastructure-first vendors expose, which some teams read as simplicity and others read as a ceiling.
Pricing sits in the mid-range tier on a subscription model, positioned similarly to other specialized mention-tracking tools.
Teams evaluating it against a broader data-infrastructure vendor should weigh whether they’ll eventually need more than mentions alone, like full SERP or web-scraping data under the same contract.
Best suited for: teams that need mentions tracking specifically and don’t plan to expand into broader scraping infrastructure.
8. Sellm
Sellm’s pitch is narrower still: a focused tool for tracking how LLMs represent a brand, built for teams that want a lighter footprint than a full data-infrastructure platform. Pricing is quote-based, which puts it in the same custom-engagement category as Cloro rather than the self-serve subscription tier most of this list occupies. That structure suits teams comfortable negotiating scope before committing.
Documentation and community presence are thinner than the more established infrastructure vendors on this list, a natural byproduct of a smaller, more specialized offering.
Pricing runs at a mid-market tier on a quote basis, scoped case by case rather than published as a fixed rate.
Best suited for: smaller teams open to a custom-scoped engagement over a self-serve subscription.
How to Choose Without Wasting a Quarter on the Wrong API
Ask what response format you actually get back. If a “demo” only shows you a chart, that’s a dashboard wearing an API’s clothes. Providers like DataForSEO or Oxylabs that show you a raw JSON schema up front are telling you something about how seriously they treat the integration side.
Ask who eats the cost when a model changes its answer format. Decodo and Scrapeless both lean on rebuilt or newer infrastructure that claims to handle this; ask for specifics, not reassurance.
Ask whether pricing scales with your actual usage or with your headcount. A per-seat model punishes agencies reporting to a dozen clients; a usage-based model, the kind DataForSEO runs, doesn’t care how many people log in.
Ask how granular the geo and model controls really go. Country-only is fine for a pilot, useless for a client who needs city-level answers in three markets.
Ask what happens with a genuinely non-standard request. That’s where Cloro or Sellm’s quote-based flexibility might beat a rigid self-serve tier, and where a narrower tool like Mentionsapi might not stretch far enough.
None of these questions have a universal answer. The right pick is the one that matches your prompt volume, your geo spread, and how much infrastructure your own team actually wants to own.
