---
title: "Trakkr vs LLMrefs: Paid Breadth vs Free Multi-Model Checks | Kitbase Blog"
description: "Trakkr vs LLMrefs compared on engine coverage, stored history, pricing and whether a free multi-model checker is enough for real tracking."
canonical: https://kitbase.dev/blog/trakkr-vs-llmrefs
---

**Trakkr and LLMrefs both cover a lot of models, and one of them is free.** That makes the comparison less about coverage than about whether you need a stored trend or a spot check.

Disclosure: we build Kitbase, a competing tool, mentioned once at the end.

## The short version

Pick **LLMrefs** if you want to check visibility across many models at no cost, and you're not yet running a programme.

Pick **Trakkr** if you need continuous tracking with stored history across a broad engine set, including Grok and Meta AI.

## Engine coverage

Trakkr covers around eight platforms: ChatGPT, Claude, Perplexity, Gemini, Grok, Meta AI, DeepSeek and Google AI Overviews. That includes Grok and Meta AI, which most tools skip entirely.

LLMrefs checks up to eleven models with keyword-derived conversational prompts.

On raw model count LLMrefs is comparable or higher. On the specific surfaces that matter commercially, Trakkr's list is more deliberately chosen: Meta AI's distribution across Facebook, Instagram and WhatsApp matters for consumer brands, and few tools cover it.

**Roughly even on count.** Trakkr wins on commercially relevant surfaces.

## Stored history

The real difference, and it decides the comparison for most teams.

Trakkr is a subscription tracker: it runs on a schedule, stores results, and shows trends over time.

LLMrefs is oriented around checks. Its free tier lets you see visibility across many models now; it's lighter on accumulated history and competitive trend analysis.

The same prompt to the same model returns different brand lists on different runs, so a single check tells you nothing about direction. You cannot tell improvement from noise, which means you cannot tell whether your work is doing anything. Checking eleven models once has the same problem as checking one model once, multiplied. We covered why in [AI answers are non-deterministic](/blog/ai-answers-non-determinism).

**Trakkr wins**, decisively for anyone measuring progress.

## Price

LLMrefs has a free tier with low-cost paid plans. Trakkr is a mid-market subscription.

**LLMrefs wins on price**, obviously.

## Side by side

| | Trakkr | LLMrefs |
|---|---|---|
| Models / engines | ~8 incl. Grok, Meta AI, DeepSeek | Up to 11 |
| Stored history and trends | Yes | Limited |
| Competitive tracking | Yes | Limited |
| Prompt generation | Manual | Keyword-derived, automated |
| Free tier | No | Yes |
| AI crawler tracking | No | No |
| Price band | Mid-market | Free → low |

## Which to choose

Use LLMrefs first. It costs nothing, it takes minutes, and it answers the question that should come before any purchase: does anyone in your category get named by AI models, and are you among them?

If the answer is that your category barely registers in AI answers, you've saved yourself a subscription and learned something useful.

If the answer is that competitors are being recommended and you aren't, you need a trend rather than a snapshot, and that's a subscription. Trakkr is a reasonable choice at that point, particularly if Grok or Meta AI matter to your audience, since few tools cover them.

Two things worth knowing before either purchase. Neither tracks AI crawlers, so if you're absent from answers you won't be able to tell whether the cause is your content or the engines never fetching your pages. And engine count is a weaker signal than it appears: more surfaces means more paid API calls per run, and coverage you don't act on is a recurring cost. Track the surfaces your buyers actually use, sampled often, rather than the longest available list sampled rarely.

Kitbase covers ten answer surfaces with stored history and verified crawler data, doesn't cover Grok or Meta AI, and has no free tier. Covered in [Kitbase vs Trakkr](/blog/kitbase-vs-trakkr) and [Kitbase vs LLMrefs](/blog/kitbase-vs-llmrefs).

## FAQ

**Is LLMrefs free?**
It has a free tier with low-cost paid plans above it. Expect caps on prompts and frequency.

**Which covers Grok and Meta AI?**
Trakkr covers both. Among broader tools, Profound does too.

**Why does stored history matter?**
Because AI answers vary between identical runs. Without repeated sampling and stored results you can't distinguish a real change from noise.

**Do either track AI crawlers?**
No. Neither reports server-side crawler activity.

**How many models should I track?**
The ones your buyers use, sampled frequently. Adding surfaces multiplies API costs without improving decisions if nobody in your market uses them.

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*Want stored trends across ten surfaces plus verified crawler data? [Start your free trial](https://app.kitbase.dev/signup/) — 7 days, no credit card required.*