---
title: "Gumshoe Alternatives: Pay-Per-Use and Subscription AI Visibility Tools | Kitbase Blog"
description: "Gumshoe alternatives compared on pricing models, from pay-per-conversation and free tiers to subscriptions with stored history and crawler data."
canonical: https://kitbase.dev/blog/gumshoe-alternatives
---

**Gumshoe charges per conversation rather than per month, across around eleven models, with a free entry point.** That model fits occasional checking and stops fitting the moment you need a trend, because trends require repeated sampling and repeated sampling is exactly what per-use pricing makes you ration.

We build Kitbase, which is subscription-priced.

## Other pay-as-you-go and free options

### LLMrefs

Free tier generating conversational prompts and checking visibility across up to eleven models. Similar breadth to Gumshoe with a keyword-derived approach that suits SEO teams. Light on stored history. **Best for** wide spot checks.

### Hall

The strongest free tier in the category, and the important difference from both Gumshoe and LLMrefs is that it stores results over time. Around eight surfaces, conversation context behind each citation, plus server-side agent analytics for AI crawlers. **Best for** free tracking that accumulates.

### Rankscale

Credit-based rather than per-conversation, which is a middle ground: usage-linked but designed for continuous tracking. Lowest serious paid price in the category. **Best for** teams moving from ad hoc to regular.

## Subscriptions with real history

### Kitbase

Kitbase runs scheduled batches of prompt × engine queries against up to ten surfaces, ChatGPT, Gemini, Perplexity, Claude, DeepSeek, GLM, Kimi, Google AI Overviews and AI Mode, tiered by plan. 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, and cancelling keeps everything already done.

The reporting assumes you have history to read: presence rate splitting brand mentions from domain citations, share of voice normalised across every tracked brand with a dense rank over time, cited pages classified as yours, a competitor's or third-party and tagged by source type, per-mention sentiment, recommendation status and list position, and automatic detection of competitors the engines named that you don't track, with their history backfilled.

Underneath: 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 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 no pay-per-use option. Starter is $99 a month and tracks ChatGPT only. **Best for** teams who need a defensible trend.

### Peec AI

Demand-weighted prompt suggestions, competitive benchmarking, multi-country tracking and Looker Studio export. **Best for** marketing teams and agencies.

### Otterly.AI

Broad base coverage with a GEO content audit at entry-level pricing. **Best for** small teams wanting more product cheaply.

## At a glance

| Tool | Pricing model | Stored history | Crawler data | Models |
|---|---|---|---|---|
| **Gumshoe** | Per conversation, free entry | Limited | No | ~11 |
| **LLMrefs** | Free → low | Limited | No | Up to 11 |
| **Hall** | Free → mid | Yes | Yes | ~8 |
| **Rankscale** | Credit-based | Yes | No | Multi |
| **Otterly.AI** | Subscription | Yes | No | 4 + add-ons |
| **Kitbase** | Subscription | Yes | Yes, verified | Up to 10 |
| **Peec AI** | Subscription | Yes | No | Mid, add-ons |

Published pricing conflicts across sources. Confirm on the vendor's own site.

## Why per-use pricing fights the measurement

Every prompt sent to every engine is a paid API call, so any pricing model is ultimately metering the same thing. The difference is what the model encourages.

Per-use pricing encourages you to check less. That's fine for spot questions and actively harmful for measurement, because the same prompt to the same engine returns different brand lists on different runs. One reading tells you nothing about direction, and the natural instinct under per-use pricing is to run fewer checks and treat each one as more meaningful, which is exactly backwards.

Subscriptions encourage the opposite. A fixed monthly allowance makes daily sampling on a focused prompt set the obvious behaviour, and that's what produces a trend you can defend.

The practical rule: track fewer prompts more often rather than more prompts occasionally. Ten prompts sampled daily beats fifty sampled weekly, on every pricing model. Our [AI visibility metrics guide](/blog/ai-visibility-metrics) covers what to measure once the sampling is right.

## FAQ

**Is pay-per-conversation cheaper than a subscription?**
For occasional checks, yes. For continuous tracking it usually costs more and produces worse data, because it discourages the repeated sampling trends require.

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

**Which alternatives track AI crawlers?**
Hall, Kitbase, Profound and Scrunch AI. Kitbase verifies crawler identity against published ranges.

**How many models should I check?**
The ones your buyers use. Checking eleven models once is no more reliable than checking one model once.

**When should I move to a subscription?**
When someone needs to report a trend, or when you need to prove a change was caused by work you did.

---

*Want repeated sampling and a trend you can defend? [Start your free trial](https://app.kitbase.dev/signup/) — 7 days, no credit card required.*