AI Brand Monitoring Platforms Compared by Number of Prompts Tracked in 2026

Key takeaways

• Entry-tier plans from most AI brand monitoring vendors cap tracking between 15 and 50 prompts, with mid-tier plans landing around 100 to 200 and enterprise tiers going custom or unlimited.
• "Prompts tracked" is not a standardized unit. Several vendors (Athena HQ, Peec AI's agency tier, Ahrefs Brand Radar) bill by credits or checks, where one credit equals one AI response for one model at one check-in, so the same nominal prompt count can deliver very different real coverage.
• LLMrefs is the outlier on raw prompt volume: $79/month for 500 prompts across every supported engine, no seat limits.
• Most practitioners recommend starting with 20 to 50 prompts per brand and expanding gradually rather than chasing the highest headline number, because prompt tracking has no search volume data and results drift between runs.
• Before comparing vendors on prompt count alone, multiply prompts x engines x check frequency. That's the number that actually determines what you'll pay and what you'll see.

Why prompt count is the wrong first question (and the right second one)

Every AI brand monitoring vendor puts a prompt number on its pricing page, and every buyer immediately uses it to rank vendors. That's understandable. It's also a little bit of a trap.

Here's the thing: a "prompt" on one platform is not the same unit as a "prompt" on another. Some vendors run your prompt against every engine on your plan, every day, no extra charge. Others meter by credits, where checking one prompt on one model on one day burns exactly one credit, and checking that same prompt across five models burns five. The headline number on the pricing page tells you almost nothing until you know the engine count and refresh frequency hiding behind it.

With that caveat locked in, let's actually compare the numbers, because they still matter; they just need context.

Prompt limits by vendor, tier by tier

Below is what the major AI visibility platforms publish on their pricing pages as of September 2026. Where a vendor uses credits instead of flat prompt counts, I've noted the effective math.

A few things jump out once the numbers are lined up side by side.

First, Otterly.AI's Lite plan at 15 prompts is genuinely the cheapest way to get a toe in the water, and it tracks four core engines from day one, which is unusually generous for an entry tier. Most competitors gate engine access behind higher tiers.

Second, Profound's Starter plan sounds comparable to KIME or Peec AI at 50 prompts, until you notice it only covers ChatGPT. If you need Perplexity or Google AI Overviews visibility, you're paying for the Growth tier at $399/month for just 100 prompts across three engines. That's a steep jump for what looks like a modest prompt increase.

Third, LLMrefs stands alone at 500 prompts for $79/month, tracking every supported engine with no seat limits. On paper that's the best prompt-per-dollar ratio in the category, and it works out to roughly $0.158 per prompt per weekly refresh cycle. The tradeoff is a single flat tier, so there's no room to negotiate more seats or custom SLAs if you outgrow it.

The credits-vs-prompts trap

This is the part that trips up most buyers, so it's worth spelling out with real numbers.

Athena HQ's Starter plan advertises 3,600 credits a month across 9 models. One credit equals one AI response. If you track 30 prompts and refresh daily across 4 models, that's 30 x 4 x 30 days = 3,600 credits, exactly the monthly allotment. But if you want the full 9-model coverage the plan advertises, running the same 30 prompts daily across all 9 models burns through the allotment in about 13 days. You'd need to drop to roughly 13 prompts to sustain daily checks across all 9 engines for a full month.

Ahrefs Brand Radar works the same way: "one check equals one prompt on one platform in one location." Ahrefs' own review coverage notes that tracking just 10 prompts across 6 LLMs in 3 locations can exhaust a plan's checks allotment in under two weeks.

Peec AI's agency tier does something similar with credits, where most customers end up tracking three models at roughly 90 credits per prompt per month. Its Essential agency plan (10,000 credits) works out to about 111 prompts a month in practice, not the round number you might assume from the price tag.

The practical takeaway: before comparing any two vendors on "prompts included," multiply prompts x engines x check frequency for both, then compare that product. A 50-prompt plan checking 4 engines daily delivers more real coverage than a 200-prompt plan checking 2 engines weekly.

How many prompts should you actually track?

Setting vendor pricing aside for a second, the more useful question is how much buyer-journey coverage you need, and the expert guidance here is more conservative than most marketing pages suggest.

SE Ranking's research notes that prompt tracking has no search volume data and no static ranking positions, unlike keyword tracking, so quality of prompt selection matters more than raw quantity. Their recommended starting point is 20 to 40 prompts, run across 2 to 3 models, tracked for at least 30 days before drawing conclusions, because prompt-tracking costs scale directly with volume and results are directional rather than definitive. SE Ranking's own volatility testing on local AI Mode queries found only 35% of domains repeat across runs of the same prompt, meaning two-thirds of citations can vanish between checks.

Aleyda Solis recommends 30 to 50 commercially relevant prompts as a minimum viable prompt library. Growth advisor Kevin Indig suggests roughly 15 prompts per persona for teams building persona-based coverage.

On the other end of the spectrum, more mature programs push further. Cognizo's guidance frames it this way: every tracked prompt represents a buyer conversation happening right now, and a narrow B2B product should track at least 100 to 150 prompts to get a representative picture across awareness, comparison, and purchase-intent stages, while a broad consumer brand should track 200 or more.

Both camps agree on one thing: start with your highest-intent prompts (brand-specific and comparison queries first, then informational and transactional) rather than trying to track everything at once. Five prompt types worth covering across a mature program: informational, comparative, instructional, brand-specific, and transactional. Most brands over-index on comparative prompts ("best X tool") and skip the other four categories entirely.

What matters more than the prompt count

A vendor that tracks 500 prompts but only monitors raw mentions isn't more useful than one tracking 50 prompts with citation tracing, sentiment, and content recommendations attached. Prompt volume is one input. What the platform does with the data matters more.

This is where the category splits into two camps: pure monitoring tools that answer "was my brand mentioned," and platforms that trace why you were or weren't cited, then help fix it. Promptwatch sits firmly in the second camp. Instead of stopping at a mention count, it layers in AI crawler logs showing exactly when ChatGPT, Claude, and Perplexity's bots visited your pages and what they read, citation analytics broken into 22 content types across 20 source types, and Content Agents that plan, write, and publish GEO-optimized pages straight to your CMS. On the prompt side specifically, its Professional tier includes 150 prompts and 18,000 responses a month across every major model, plus query fan-out data showing how a single prompt splits into sub-queries, which is a level of granularity most flat-prompt-count competitors don't expose at all.

Promptwatch

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Worth noting on the data side: Promptwatch's own research shows ChatGPT's fanout behavior has changed a lot this year. The average queries-per-response fell from 2.15 in early December to exactly 1.0 by April 2026, and fanout query length shrank from roughly 117 characters to about 53, meaning ChatGPT is now searching with short, keyword-like queries rather than full sentences (https://promptwatch.com/data/chatgpt-query-fanouts). That matters for prompt design: your tracked prompts should mirror that terse, keyword-like pattern, not conversational phrasing, if you want them to reflect what AI engines are actually searching for. It also matters because ChatGPT's citation inventory per response averages around five sources total, versus roughly ten for Google AI Overviews and Perplexity (https://promptwatch.com/data/average-sources-per-response), so narrow, single-intent pages have a real structural advantage over broad ones when the citation slot count is that tight.

Side-by-side: prompt count vs. what you get for it

A practical way to shop by prompt count

Rather than sorting vendors by the biggest number on the pricing page, work backward from your actual prompt library.

First, build your prompt list before you buy anything. Aim for 30 to 50 prompts split across brand, comparison, informational, instructional, and transactional intents. This is the number most experts converge on as a workable starting point, and it's small enough that almost every vendor's entry tier can accommodate it.

Second, decide how many engines actually matter to your buyers. If your audience lives in ChatGPT and Google AI Overviews, don't pay for nine-engine coverage you'll never look at. If you're an enterprise brand that needs Grok, Meta AI, and Google AI Mode too, that narrows your options fast, since only a handful of vendors (KIME Enterprise, Scrunch Enterprise, Athena HQ Starter, Promptwatch) cover that breadth.

Third, multiply your prompt count by engine count by refresh frequency, then compare that single number across vendor shortlists rather than comparing headline prompt figures directly. A 50-prompt plan on 4 engines checked daily is roughly 6,000 checks a month; line that up against whatever credit or response allotment each vendor actually offers.

Fourth, budget for growth. Nearly every review of every vendor in this category flags the same complaint: prompt-tracking limits force hard prioritization calls, and teams that start small almost always want more coverage within two or three months once they see what's showing up (or not showing up) in the data.

If you want to see how the wider GEO software market stacks up beyond prompt counts, the directory at bestgeosoftware.com is a reasonable place to browse the full field, and the AI rank tracking category specifically is covered at ai-rank-tools.com.

The bottom line

Prompt count is a useful filter, but it's the third or fourth thing to check, not the first. Check what a vendor actually means by "prompt," whether it's metering by credits, how many engines are included at that tier, and how often it refreshes. Then size your own prompt library to 30-50 high-intent queries before you even look at pricing pages. Most teams overspend on volume they don't use and underspend on the citation tracing and action-taking features that turn a monitoring dashboard into something that actually moves your AI visibility numbers.