---
title: "AI Monitor Alternatives: 8 Tools for Tracking Brand Inclusion in AI | Kitbase Blog"
description: "AI Monitor alternatives compared on inclusion tracking, competitor replacement, engine coverage, crawler data and price."
canonical: https://kitbase.dev/blog/ai-monitor-alternatives
---

**AI Monitor isolates one question: are you in the AI answer, did you drop out, and who took your place.** Alternatives either cover the same ground more cheaply, cover more engines, or answer the question AI Monitor doesn't, which is why the change happened.

We build Kitbase, which is in the third group.

## Cheaper coverage of the same job

### Hall

Free tier with stored history, around eight surfaces, conversation context behind each citation, and server-side agent analytics. Tracking inclusion over time is exactly what the free tier is good for. **Best for** teams who want trends without spend.

### Rankscale

Lowest serious paid price, credit-based, multi-engine, with AEO audits attached. **Best for** teams who need a stored inclusion trend and nothing more.

### LLMrefs

Free-to-low checks across up to eleven models. Light on history and competitive depth. **Best for** breadth on no budget.

### Waikay

Low entry pricing, and it flags hallucinations and knowledge gaps alongside presence. Different question, useful if being described wrongly matters as much as being absent. **Best for** accuracy problems.

## More engines

### Profound

The widest list available, including Copilot, Grok, Meta AI and DeepSeek, plus agent analytics and prompt-volume data from real AI conversations. Enterprise pricing. **Best for** contested categories with budget.

### Trakkr

Around eight platforms including Grok, Meta AI and DeepSeek, at mid-market pricing. **Best for** teams wanting breadth without an enterprise contract.

### Superlines

Ten or more surfaces with tiered selection, so you pay for the engines you care about. **Best for** non-standard coverage needs.

## Why the number moved

### Kitbase

Kitbase reports inclusion with more resolution than a single metric: brand mentions and domain citations counted separately, share of voice normalised across every tracked brand with a dense rank over time, per-mention sentiment, recommendation status and list position, and automatic detection of competitors the engines named that you don't track yet, with their history backfilled. That last feature covers the replacement case directly.

The reason to pay for it over a pure inclusion tracker is the surrounding data.

**Crawl.** Server-side [AI crawler detection](https://docs.kitbase.dev/crawler-detection) resolves forwarded requests to named crawlers and verifies each against published identity, with per-path crawl frequency. When you drop out of an answer, the first thing to rule out is that the supporting page stopped being fetched.

**Sources.** Cited domains and exact pages, classified as yours, a competitor's or third-party and tagged by source type. When a competitor replaces you, this shows what they were cited from, which is the difference between "they wrote a better page" and "they got listed on a review site you're absent from."

**Consequence.** Cookieless web analytics on the same events pipeline, with funnels, journeys, retention and rage-click detection, so an inclusion can be valued rather than just counted.

Plus backlink opportunities from the domains AI cites where you're absent, a link marketplace, a site audit and keyword research.

**The honest limitation:** no free tier, and Starter tracks ChatGPT only at $99 a month. **Best for** teams who need to act on the change, not just see it.

### Peec AI

Demand-weighted prompt suggestions, competitive benchmarking and multi-country tracking. Good at telling you which prompts to watch in the first place, which is upstream of inclusion. **Best for** marketing teams and agencies.

## At a glance

| Tool | Inclusion focus | Replacement tracking | Crawler data | Price band |
|---|---|---|---|---|
| **AI Monitor** | Primary metric | Yes, core | No | Mid-market |
| **Hall** | Yes | Via competitor data | Yes | Free → mid |
| **Rankscale** | Yes | Basic | No | Lowest paid |
| **LLMrefs** | Yes | No | No | Free → low |
| **Waikay** | Yes, plus accuracy | Yes | No | Low |
| **Profound** | Yes | Yes | Yes | Enterprise |
| **Trakkr** | Yes | Yes | No | Mid-market |
| **Kitbase** | Split into mentions and citations | Yes, auto-detected with backfill | Yes, verified | From $99/mo |
| **Peec AI** | Yes | Yes | No | Mid-market |

Published prices in this category conflict across sources. Confirm on the vendor's site.

## Making a single metric trustworthy

An inclusion metric is only as good as the sampling behind it, and this is where narrow tools succeed or fail.

The same prompt to the same engine returns different brand lists on different runs. So "we dropped out" can mean you genuinely lost ground, or that this run sampled the tail of a distribution you were always in 40% of the time. Without repeated runs you cannot tell, and acting on a single reading wastes a content cycle.

Three habits fix most of it. Sample the same prompts on a schedule rather than ad hoc. Read the rate across runs rather than the latest answer. And treat a change as real only when it holds across engines, since an engine-specific shift usually reflects that engine's retrieval rather than anything about you.

Once a change is confirmed, the next question is causal, and that needs crawl and citation-source data. We covered the link in [does AI crawling predict citations](/blog/does-ai-crawling-predict-citations).

## FAQ

**What is the best free AI Monitor alternative?**
Hall, because its free tier stores history and includes crawler analytics.

**Which alternative tracks the most engines?**
Profound, followed by Superlines and Trakkr.

**Do any alternatives explain why I dropped out of an answer?**
Kitbase pairs the drop with crawl data and cited-source mapping, which usually identifies the cause. Profound and Hall cover the crawler half.

**Is inclusion the right metric for AI visibility?**
It's the clearest single one. Splitting it into mentions versus citations is more useful, because they need different fixes.

**How often should I sample?**
Daily on a focused prompt set beats weekly on a large one. Consistency matters more than volume for detecting real change.

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*Want to know why you dropped out, and what the inclusion was worth? [Start your free trial](https://app.kitbase.dev/signup/) — 7 days, no credit card required.*