Best AI Visibility APIs in 2026: Depth, Limits and Access Compared

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Every AI visibility platform says it has an API. That claim covers everything from a single endpoint returning a score to a full data surface you can build a warehouse pipeline on top of.

The difference only shows up when you try to use it. Can you get the actual answer text a model produced, or only the fact that you were mentioned? Can you pull citations at URL level, or only a domain leaderboard? Can you get CSV directly, or do you write the flattening code yourself? Can you subscribe to events, or do you poll? Is the API on your plan at all?

So we counted. Where a platform publishes an OpenAPI specification we counted paths and operations from the specification itself rather than from a documentation page, which is the only way to get a number nobody argues with.

The best AI visibility APIs, ranked

1. LLM Pulse, for depth. The largest published surface we could verify, and the only one that reaches answer text, individual cited URLs and first-party Search Console data in the same API.
2. Otterly.AI, for a clean small API. Twenty-four well-documented paths, a public specification, and an account endpoint that tells an integration its own remaining budget.
3. Profound, for enterprise breadth. Agents, knowledge bases, shopping and YouTube data, if you are on the plan that includes API access.
4. SE Ranking, if you want AI search and traditional SEO in one API rather than a dedicated tool.

The rest of this post is how we got there, and what to check on any platform you are comparing.

How we counted

A published OpenAPI file lists every path and every HTTP method on it. Fetch the JSON, count the paths, count the operations, read the tags. Anyone can reproduce the result in about a minute.

Where no specification is published we fell back to the documentation and said so. Where neither exists we left the row empty rather than inventing a number. All figures below were checked on 12 August 2026.

Quick comparison

Depth is about how far down the data goes

Counting operations is a proxy. What it is measuring is whether the API stops at the summary layer.

Most AI visibility APIs give you a visibility number, a share of voice figure and a citation list. That is enough to fill a chart and not much else. The questions that actually change what you do live one or two levels below.

The LLM Pulse API is organised into 21 resource groups, and the interesting ones are the deep ones.

• Answers. The full text of what each model actually said, rather than a flag saying you appeared in it. You can read the answer, see where your brand sat in it, and see every source the model pulled from.
• Query fan-out. The sub-queries each model issued behind your prompt, either as one row per distinct sub-query with its share, or one row per prompt. This is the layer where you find the phrasing models actually search for, which is rarely the phrasing you wrote.
• Citation intelligence. Citations grouped by domain, individual cited URLs addressed by hash, every occurrence of a URL across your prompt set, the cached content of the cited page, and a breakdown of which brands get mentioned when a given domain is cited. That last one answers “who wins when this publisher is the source”, which is the actual input to a placement strategy.
• AI model insights. Per-model summaries, position distributions and Google AI Overview results, because a brand that looks healthy on average is often invisible on one engine.
• Search Console. Impressions, clicks, CTR, position, top queries and pages, joinable to the AI visibility data on the same project. Very few competitors offer first-party search data at all.
• AI and agent traffic. Human referral visits arriving from AI assistants, and separately the AI crawler hits recorded against your own server logs.
• Recommendations, technical GEO reports, intelligence tasks and annotations, all readable and creatable, so an automation can request work and collect the result.

For comparison, the Otterly.AI specification is a clean, well-documented 24 paths across engines, workspaces, tags, prompts, brand reports, audits and accounts. It reaches AI responses per prompt and citation history per URL, which is real depth for its size, and it stops there.

Profound’s API is the broadest of the competitors on paper, reaching agents, knowledge bases, shopping data, YouTube channels and videos, query fanouts and fact-check claims. They do not publish a specification you can count, and the access question below applies.

The two questions that decide whether you can use it

Is it on your plan?

This is where most API comparisons quietly fall apart. Profound’s pricing page lists API access against Enterprise, with its two self-serve tiers showing none. Searchable begins API access on its 400 dollar tier. Hall places it on Enterprise. Athena lists it from its 295 dollar starting plan. Otterly.AI includes it on its middle and upper tiers. SE Ranking includes API and MCP across its plans.

On LLM Pulse the REST API, the CLI, the MCP server and the Data Studio connector are available on self-serve plans rather than behind an enterprise contract and a sales call. Check the current pricing page for which tier you need, as with every platform in this list.

How hard will it throttle you?

Rate limits get skipped in comparisons and then decide your architecture.

Profound documents a default of 600 requests per hour per API key. LLM Pulse allows 300 requests per minute, with a tighter 60 per minute on writes. That is 30 times the hourly headroom, which is the difference between a backfill that finishes overnight and one that takes a fortnight.

An integration that can see its own budget is easier to build than one that discovers the ceiling by hitting it. Otterly.AI does this well: their account endpoint returns the plan, the billing period and current usage against every limit. LLM Pulse now returns the same thing, and every response also carries the standard rate-limit headers, so a client can pace itself without asking.

Those headers are worth checking on any platform you compare. Without them a client either backs off blindly or not at all, and you find out which at the worst possible moment.

The parts that save you the most code

CSV and flat output on the endpoint itself

Nested JSON is correct and inconvenient. Every read endpoint on the LLM Pulse API accepts an output parameter that returns either a rectangular columns-and-rows structure or CSV directly, so Excel, Sheets, Tableau and warehouse loaders can consume it without anyone writing code to walk the nesting. Total counts and pagination move into response headers, because a CSV body cannot carry them.

It is the difference between a two-line load script and an afternoon of parsing, and almost nobody in the category ships it.

Webhooks instead of polling

Seven event types fire from LLM Pulse: a brand mention, a competitor mention, a citation, a completed prompt execution, a detected negative sentiment, a finished recommendations run and a finished intelligence task. Payloads are signed so you can verify them, and the same events drive the Zapier, Make and n8n connectors.

The alternative is a cron job asking “anything new?” every fifteen minutes, which is what you write when the API has no event layer. None of the competitor APIs we checked documents outbound webhooks.

Clients you do not have to write

LLM Pulse publishes 12 official SDKs: TypeScript, Python, Ruby, Go, Rust, Java, Kotlin, C#, PHP, Swift, Dart and R, plus a command line tool. The nearest competitor publishes two.

This matters less to a Python data team than to everyone else. If your stack is Kotlin or C#, the difference between a maintained client and a hand-rolled HTTP wrapper is a week.

Machine-readable documentation

The LLM Pulse API publishes OpenAPI 3.0.3 and Swagger 2.0 in lockstep, a generated Postman collection, and llms.txt files so an AI coding assistant can read the API surface before writing your integration. Otterly.AI publishes an OpenAPI specification and a per-endpoint markdown index, which is the same instinct.

The rest of the category mostly ships a documentation site and nothing a machine can parse.

Where our API is not the deepest

One gap we would rather name ourselves than have you discover in week two.

Reads run ahead of writes. Sixty-one of our operations read data and twenty-four write it, so an integration that pulls analytics has more to work with than one that manages prompts, competitors and collections programmatically. If your plan is to drive the whole product from code rather than to feed a warehouse, check the write list against your workflow before you commit.

Our official CLI and SDKs also trail the published specification. They are generated from the OpenAPI document on their own release cycle, so a client you install today may not yet expose the newest endpoints even though the API serves them and the specification documents them. Calling those endpoints directly over HTTP always works.

Several gaps in this comparison closed while we were writing it, which is the honest reason they read as solved above rather than pending. The API now returns rate-limit headers on every response, an account endpoint reports your remaining quota, and query fan-out, the sub-queries a model actually issued behind your prompt, became a first-class endpoint instead of a page you had to read by eye. The larger one was coverage: reputation monitoring, owned media and app store tracking, Reddit intelligence, shopping results and AI paid ads were all in the product with no API path, and all six now have one. Every item on that list existed as a competitor advantage or an honest weakness when the research started, which is a decent argument for doing this kind of comparison honestly.

What to check before you commit

• Ask for the OpenAPI specification. If there isn’t one, you will be reverse-engineering behaviour from examples.
• Check the plan, not the feature page. API access sits behind an Enterprise line more often than the marketing suggests.
• Find the documented rate limit and multiply it out against your actual backfill.
• Test whether you can reach the answer text and individual cited URLs. Every platform gives you aggregates. The layer underneath is where they differ.
• Look for webhooks, CSV output and rate-limit headers. Each absence is code you will write and maintain.
• Check the SDK for your language, or budget for building and maintaining one.

Summary

On the numbers we could verify in August 2026, LLM Pulse publishes the deepest AI visibility API: 82 paths, 91 operations, 24 resource groups, CSV output on read endpoints, signed webhooks across 7 event types, 12 SDKs, published rate-limit headers, and a budget 30 times the documented competitor figure. It reaches answer text, individual cited URLs and their cached content, per-model position distributions and first-party Search Console data, which is the layer where the decisions actually get made.

The gaps are real and named above. The plan question is the one to settle first on any platform you are evaluating, because the deepest API in the world is worth nothing on a tier that does not include it.