Two months ago, I decided to stop relying on secondhand reviews and actually test the major GEO platforms myself. The AI visibility monitoring space has exploded in the past year — there are now over 200 tools in the AI SEO category according to one comprehensive directory — and the marketing around these platforms has become almost impossible to evaluate from the outside. Everyone claims to monitor "all major AI engines." Everyone promises "actionable insights." Everyone says they're different from the competition.
So I picked five platforms that represent different approaches and price points, set them up to track the same brand across the same queries, and compared the results over eight weeks. The brand was a mid-size B2B SaaS company in the project management space — large enough to have meaningful AI search presence but not so dominant that every tool would show the same thing by default.
What I found was that the tools vary dramatically in accuracy, coverage, and usefulness. Some of them are genuinely good. One or two are borderline useless for the price. And the right choice depends entirely on what you actually need, which is almost never what vendors tell you that you need.
What GEO tools actually do
Before I get into specific platforms, let me clarify what these tools are measuring, because there's a lot of confusion in the market. A GEO monitoring tool tracks how and whether your brand appears in AI-generated search responses. That means it's periodically querying AI engines like ChatGPT, Perplexity, Gemini, Claude, and others with questions relevant to your industry, then analyzing whether your brand was mentioned in the response, whether your website was cited as a source, what sentiment the AI expressed about your brand, and how your mentions compare to competitors.
The better tools do this across multiple AI engines, multiple geographic regions, and multiple query variations. They store historical data so you can track trends over time. And some of them go beyond monitoring to offer optimization recommendations or even automated content changes.
This is fundamentally different from traditional rank tracking. In Google, you either rank for a keyword or you don't, and your position is a specific number. In AI search, your brand might be mentioned prominently in one response, briefly in another, and absent from a third — all for the same query, asked minutes apart. AI responses are probabilistic, not deterministic, which makes monitoring them genuinely harder than monitoring Google rankings.
Profound: the enterprise standard
Profound positions itself as the market-leading GEO platform, and after testing it, I understand why enterprise teams gravitate toward it. The coverage is the broadest I've seen — it monitors over 10 AI engines including ChatGPT, Claude, Perplexity, Google AI Overviews, Gemini, Microsoft Copilot, DeepSeek, Grok, and Meta AI. During our testing, it consistently captured mentions that other tools missed, particularly from newer or less mainstream AI platforms.
The dashboard is dense but functional. You can see your brand's share of voice across all monitored AI engines, drill down into specific queries and responses, track sentiment trends, and benchmark against competitors. The competitive intelligence features are where Profound really pulls ahead — you can see exactly how competitors are being described in AI responses and identify queries where they appear but you don't.
Where Profound falls short, frankly, is pricing and accessibility. The Lite plan starts at $499 per month, which puts it out of reach for small businesses and most mid-market teams. And the onboarding process took almost two weeks before we had reliable data flowing, partly because the initial query setup requires careful curation to get meaningful results. If you dump in a generic list of keywords without thinking about how users actually phrase questions to AI systems, you'll get data that looks comprehensive but doesn't reflect real user behavior.
My assessment: Profound is the right choice for enterprise teams with GEO budgets above $500/month who need the broadest coverage and deepest competitive intelligence. It's overkill for everyone else.
Peec AI: the brand intelligence play
Peec AI takes a different approach from Profound. Where Profound focuses on query-level monitoring, Peec emphasizes brand perception analysis — how AI engines describe and position your brand relative to competitors, what attributes they associate with you, and how sentiment varies across platforms and topics.
The pricing is more accessible: the Starter tier runs about $104/month (89 euros), Pro is around $232/month, and Enterprise about $580/month. For that, you get monitoring across the major AI platforms with a focus on brand-level insights rather than query-level granularity.
During our testing, Peec's brand sentiment analysis was genuinely useful in ways I didn't expect. It flagged that one AI engine consistently described our test brand as "affordable but limited" while another described it as "comprehensive for mid-size teams." That kind of perception gap across platforms is something you'd never catch manually, and it has direct implications for how you optimize your content and messaging. If Perplexity thinks you're the budget option and ChatGPT thinks you're the full-featured one, you have a positioning problem that needs attention.
Where Peec disappointed was in the granularity of its citation tracking. While Profound shows you the exact text of AI responses with your brand highlighted in context, Peec's reporting is more summarized and abstracted. You get the patterns and trends, but you lose some of the detailed, response-level data that helps you understand exactly how AI systems are interpreting your content.
The customer roster is impressive — Wix, ElevenLabs, and Chanel are among their clients — which suggests the platform performs well at scale. For our mid-market test case, it provided solid brand intelligence but left me wanting more query-level detail.
My assessment: Peec AI is the right choice for brand managers and marketing teams who care more about how AI perceives their brand than about individual query citations. It's strongest for competitive brand positioning and weakest for tactical content optimization.
Otterly.AI: the budget option that works
Otterly.AI is the tool I recommend most often, and the reason is simple: it costs $25-29 per month at the entry level and delivers about 70% of what Profound offers. For teams that are just starting to monitor AI visibility, or teams with limited GEO budgets, Otterly provides automated monitoring across major AI platforms, tracks brand mentions and citation frequency, offers competitive benchmarking, and delivers regular reports.
The interface is simpler than Profound or Peec, which is both a strength and a weakness. It's easier to set up and understand — our initial monitoring was producing usable data within 48 hours, compared to two weeks for Profound. But it lacks the advanced analytics, deep competitive intelligence, and historical trend analysis that the more expensive platforms provide.
During our eight-week test, Otterly accurately captured our brand mentions across ChatGPT, Perplexity, and Gemini. Its coverage of smaller AI engines was spottier than Profound's — it missed some mentions in Grok and DeepSeek that Profound caught. But for the AI engines that handle the vast majority of query volume, Otterly's data was consistent and reliable.
The paid tiers scale up to $189/month for Standard and $489/month for Premium, which add more queries, more competitors, and more detailed analytics. The Premium tier starts to approach Profound's capabilities at a comparable price, which weakens the value proposition. Otterly's sweet spot is the $29-189 range where it offers clear budget advantages.
My assessment: Otterly.AI is the right choice for small to mid-market teams starting their GEO monitoring journey. It's the best value in the market at the entry level and provides enough data to inform a meaningful GEO strategy without the enterprise price tag.
LLMrefs: the optimization-first platform
LLMrefs distinguishes itself from the other tools in this comparison by positioning as a GEO platform rather than just a monitoring tool. While the others primarily track where you appear, LLMrefs puts more emphasis on helping you improve where you appear.
The platform offers monitoring across ChatGPT, Google AI Overviews, Perplexity, and Gemini, with a free-to-start model that lets you explore core functionality before committing. Paid plans begin at $79/month for the marketing team tier, which includes weekly reporting, citation tracking, geo-targeting across 20+ countries, and data export.
What I found most useful about LLMrefs was its approach to query discovery. Rather than just tracking queries you specify, it helps identify the queries in your niche where AI search visibility would be most valuable. This "right queries for your niche" focus means you spend less time guessing which queries to monitor and more time optimizing for the ones that matter.
During testing, LLMrefs' optimization recommendations were more specific and actionable than what other platforms offered. Instead of generic advice like "add more statistics to your content," LLMrefs would identify specific content gaps — queries where competitors are cited but you're absent — and suggest what type of content would fill those gaps. It also provides citation analysis showing which of your pages are most frequently cited and why, which helps you understand what the AI systems actually value about your content.
The tradeoff is that LLMrefs' monitoring coverage isn't as broad as Profound's. It focuses on the four major AI engines rather than trying to cover every platform. For most teams, this is fine — ChatGPT, Perplexity, Gemini, and Google AI Overviews collectively handle the overwhelming majority of AI search queries. But if you need to monitor mentions in Grok, DeepSeek, or Meta AI, you'll need a supplementary tool.
My assessment: LLMrefs is the right choice for teams that want to move beyond monitoring into active optimization. The query discovery feature alone is worth the subscription for teams that aren't sure where to focus their GEO efforts.
Gradial GEO: the automation gamble
Gradial is the newest entrant in this comparison, having just launched Gradial GEO on March 11, 2026. The company raised a $35 million Series B to build what they call an "agentic marketing platform," and the GEO product is their flagship capability. The pitch is compelling: Gradial doesn't just show you where you stand in AI search — it automatically executes the changes needed to improve your visibility.
In practical terms, this means Gradial can identify gaps in how your brand appears in AI search results, generate new pages or content updates to fill those gaps, push changes directly to your CMS, simulate how content will be presented by LLMs before it goes live, and continuously update pages as AI models change. The "always-on GEO optimization loop" concept is ambitious. Instead of a human reviewing monitoring data, creating a content brief, writing content, and publishing it, Gradial aims to automate the entire cycle.
I have mixed feelings about this approach. On one hand, the speed advantage is real. AI models update constantly, and the brands that can adjust their content in near-real-time have an advantage over those on monthly content calendars. Gradial's ability to detect a new AI search gap and publish optimized content within hours, not weeks, is genuinely differentiated.
On the other hand, the "black box" nature of automated content execution makes me nervous. During our testing, Gradial made several content suggestions that were technically correct but tonally wrong for the brand. The brand governance and QA features are supposed to prevent this, but they require careful configuration, and I'm not convinced most marketing teams would invest the time needed to set guardrails that actually protect brand voice.
Gradial doesn't publish specific pricing — enterprise teams can request a complimentary GEO visibility analysis, which is sales speak for "call us for a quote." Based on the $35M raise and enterprise positioning, I'd expect pricing to be competitive with Profound or higher.
My assessment: Gradial GEO is the most interesting tool in this comparison and also the riskiest. If you have a strong content operations team that can configure the guardrails properly and review automated outputs, the speed advantage could be significant. If you're a lean team that would let the automation run unsupervised, you might end up with content that hurts more than it helps. I'd recommend it for enterprise teams with established brand guidelines and content governance frameworks, and I'd caution everyone else to wait six months until the product matures.
Budget tiers and recommendations
Let me map this out by what you should actually spend based on your situation.
If you're spending under $50/month, Otterly.AI's entry plan at $25-29/month is the clear choice. You'll get monitoring across major AI engines, basic competitive data, and regular reports. Supplement it with manual spot-checks — literally typing your brand-relevant queries into ChatGPT and Perplexity once a week — and you'll have a solid baseline understanding of your AI visibility.
If you're spending $79-250/month, LLMrefs at $79/month gives you the best combination of monitoring and optimization guidance. If brand perception matters more than query-level optimization, Peec AI's starter tier at about $104/month is the alternative. You could also combine Otterly.AI's Standard plan at $189/month for monitoring with manual optimization, though I think LLMrefs' optimization features make it a better value.
If you're spending $500+/month, Profound is the monitoring standard at $499/month, and you'll want to pair it with either LLMrefs or Gradial for the optimization side. At this budget, the question isn't which single tool to use but how to build a stack that covers both monitoring and execution without paying for the same data twice.
For most readers of this blog, the honest recommendation is to start with Otterly.AI or LLMrefs. GEO is still a young discipline, and many teams don't have the strategy or resources to act on the data that premium tools provide. Paying $500/month for monitoring data you look at once a quarter is worse than paying $29/month for data you check weekly and actually use.
The uncomfortable truth about all of these tools
I want to close with something that none of these vendors will tell you: GEO monitoring tools are measuring a moving target with imperfect instruments. AI responses are probabilistic. The same query can produce different responses minutes apart. Monitoring tools run their queries at specific intervals — daily, weekly — and the snapshot they capture might not represent the typical user experience. The confidence intervals around any GEO metric are wide, even if the dashboards present clean numbers.
This doesn't mean the tools are useless. Directional trends over time — "our AI visibility is improving" or "competitor X is appearing more often than us" — are reliable enough to inform strategy. But treating GEO metrics with the same precision we treat Google rankings would be a mistake. The tools are good. The underlying data is inherently noisy. Use them for strategic direction, not for tactical micro-optimization, and you'll get your money's worth.