--- title: "LLMrefs Alternatives: From Free Checks to Real AI Visibility Tracking | Kitbase Blog" description: "LLMrefs alternatives compared for teams who need stored history, repeated sampling and crawler data instead of one-off AI visibility checks." canonical: https://kitbase.dev/blog/llmrefs-alternatives --- **LLMrefs gives you a free multi-model visibility check derived from keywords, and people look for alternatives when a check stops being enough.** The upgrade you need is almost always sampling: repeated runs, stored history, and enough data to tell a real change from noise. Engine count is rarely the actual problem. We build Kitbase, one of the tools below. ## Other free and near-free options ### Hall The strongest free tier in the category, and unlike most it stores history and covers both halves of the problem: around eight surfaces on the answer side, plus server-side agent analytics for AI crawlers. It also shows the conversation context behind each citation rather than the bare URL. **Best for** anyone who wants free but needs the data to accumulate. ### Gumshoe Pay-per-conversation across roughly eleven models with a free entry point. Suits short prompt lists and occasional checks rather than continuous tracking. ### Waikay Very low entry pricing and a different angle: it flags hallucinations and knowledge gaps, not only whether you appear. **Best for** teams whose problem is AI getting facts wrong about them. ## The first real paid tier ### Rankscale The lowest serious paid price, credit-based, multi-engine, with AEO audits. This is the standard first upgrade from a free tool when all you need is a stored trend. **Best for** teams where budget is the binding constraint. ### ZipTie Narrower engine coverage, focused on Google AI Overviews plus ChatGPT and Perplexity, with reporting depth that exceeds its price. **Best for** categories that live in AI Overviews. ### Otterly.AI Broader base coverage than Rankscale plus a GEO content audit for crawlability and citation potential. **Best for** small teams wanting product rather than just a number. ## When you need diagnosis, not just tracking ### Kitbase Kitbase runs prompts against up to ten engines on a schedule, ChatGPT, Gemini, Perplexity, Claude, DeepSeek, GLM, Kimi, Google AI Overviews and AI Mode. Runs are resumable: completed provider calls are recorded and never re-executed, so an interrupted run picks up rather than re-paying for finished queries. Reporting separates the things that need different fixes: presence rate splits brand mentions from domain citations, share of voice is normalised across every tracked brand with a dense rank over time, cited sources go to the exact page and are classified as yours, a competitor's or third-party and tagged by source type, and each mention carries sentiment, recommendation status and list position. The layers past the answer: server-side [AI crawler detection](https://docs.kitbase.dev/crawler-detection) with identity verification and per-path crawl frequency, cookieless web analytics with funnels and retention on the same pipeline, backlink opportunities built 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 the $99 Starter plan tracks ChatGPT alone where LLMrefs checks up to eleven models for nothing. **Best for** teams who need to explain movement, not just observe it. ### Peec AI The mid-market standard for prompt discovery: demand-weighted suggestions rather than an empty text box, plus competitive benchmarking, multi-country tracking and Looker Studio export. No crawler data. **Best for** teams whose bottleneck is choosing what to track. ### Profound Widest engine coverage anywhere plus real prompt-volume data from actual AI conversations. Enterprise pricing. **Best for** contested categories with budget. ## At a glance | Tool | Stored history | Crawler data | Free tier | Price band | |---|---|---|---|---| | **LLMrefs** | Limited | No | Yes | Free → low | | **Hall** | Yes | Yes | Yes | Free → mid | | **Gumshoe** | Limited | No | Yes | Pay-per-use | | **Waikay** | Yes | No | No | Low | | **Rankscale** | Yes | No | No | Lowest paid | | **ZipTie** | Yes | No | No | Low-mid | | **Otterly.AI** | Yes | No | Trial | Entry-level | | **Kitbase** | Yes | Yes, verified | Trial | From $99/mo | | **Peec AI** | Yes | No | No | Mid-market | | **Profound** | Yes | Yes | No | Enterprise | Published prices in this category conflict across sources. Confirm on the vendor's own site. ## Upgrade for sampling, not for engines The most common mistake when leaving a free tool is buying breadth. More models on the comparison table feels like progress and usually isn't. What makes a free check unreliable is that the same prompt returns different brands on different runs. One reading tells you nothing about direction. Checking eleven models once has the same problem as checking one model once, multiplied. What fixes it is running the same prompts repeatedly, storing the results, and reading the trend. That's the thing worth paying for, and it's why the paid tiers cost what they do: every prompt × engine query is a paid API call to a model provider. When you compare tools, compare how much sampling your money buys, not how many logos appear on the coverage list. Once you have a trend, the next question is why it moves, and that needs data outside the answer layer. We covered the relationship in [does AI crawling predict citations](/blog/does-ai-crawling-predict-citations). ## FAQ **What is the best free LLMrefs alternative?** Hall, because its free tier stores history and includes crawler analytics. **Which alternative checks the most models?** Profound has the widest paid coverage. Among free and low-cost tools, LLMrefs and Gumshoe check the most models at once. **Do I need a paid tool for AI visibility?** Only once you need to prove change over time or explain it. For establishing that the channel exists, free tools are enough. **Which alternatives track AI crawlers?** Hall, Profound, Scrunch AI and Kitbase. Kitbase verifies crawler identity so spoofed user agents don't inflate counts. **Will my LLMrefs history transfer?** No. Run the new tool in parallel for a few weeks to build a baseline before relying on it. --- *Want repeated sampling, stored trends and the crawler data that explains them? [Start your free trial](https://app.kitbase.dev/signup/) — 7 days, no credit card required.*