Otterly vs Peec vs Profound vs Citerank: An Honest Comparison

Every comparison of AI citation tracking tools shares the same flaw: they are written by people who have never built one.

I have. Citerank is a platform I built to track, diagnose, and fix AI search visibility. That means I have spent real time understanding what it actually takes to query AI engines at scale, store the results reliably, and surface something a marketer can act on. I also know where every tool in this category hits its limits, including mine.

This comparison covers Otterly, Peec AI, Profound, and Citerank. I will give you verified pricing, a plain-English explanation of methodology differences, a cost-per-prompt breakdown no other article has run, and the one problem that all four tools share but none of them will tell you about.

How AI citation tracking actually works

Before you compare tools, you need to understand what they are actually doing. The mechanics matter because they determine how accurate the data is.

Every tool in this category runs the same basic loop: send a prompt to an AI engine, receive a response, parse that response for brand mentions and cited URLs, and store the result. Run that loop across many prompts on a schedule and you get visibility trends over time.

What differs is how each tool executes step one.

API polling means sending the prompt directly to the AI engine's API. Responses come back fast and at scale. The limitation is that API responses sometimes differ from what users see in the live product, particularly for engines like ChatGPT and Perplexity where the live UI renders differently than a raw API call.

Browser simulation means the tool spins up a real browser session, logs into the AI platform as a user would, submits the prompt, and captures the rendered response. This is slower and more expensive to operate, but it reflects what your actual customers see when they type a question.

Audit-based scoring is a third approach. Rather than polling AI engines with live prompts, it analyzes your site's signals (schema markup, entity clarity, crawler access, passage structure) and scores how well the AI should be able to read and cite you. This does not tell you current citation frequency, but it tells you the structural reasons behind it.

Otterly and Profound use API-based approaches. Peec uses browser simulation. Citerank combines audit-based scoring with citation tracking. Each approach involves a real trade-off.

What each tool actually tracks

A few things in that table are worth pausing on. Profound's Starter plan, the $99/month entry tier, tracks ChatGPT and nothing else. To track Perplexity or Google AI Overviews you need the Growth plan at $399/month. Most comparison articles mention the Starter price without flagging this clearly. Peec's base three engines are ChatGPT, Perplexity, and Google AI Overviews, and every other engine is a paid add-on billed monthly per model.

The diagnostic layer row is the one that matters most for most teams, and I will come back to it.

Pricing: what you actually pay

Pricing data verified as of August 2026. All tools update pricing without notice, confirm before purchasing.

Otterly

Engine add-ons: Gemini costs $9 (Lite), $59 (Standard), or $149 (Premium) per month. Claude costs $29 (Lite), $109 (Standard), or $439 (Premium) per month. Annual billing saves roughly 15 percent.

The Lite plan at $29/month is the most cited number in competitor articles. What they rarely mention: 15 prompts covers a very small brand. If you are tracking 10 competitors across five product categories, you are at 50+ prompts before you account for follow-up questions. At that scale the Standard plan at $189/month is the realistic entry point.

Profound

Profound's enterprise tier reportedly runs $2,000 to $5,000+ per month for full multi-engine tracking, though these figures are not published publicly.

The thing to register is the jump from Starter to Growth. You pay four times more to go from one engine to three. If your brief includes "track across ChatGPT, Perplexity, and AI Overviews", a completely standard requirement, you are at $399/month minimum on day one.

Profound closed a $96M Series C at a $1 billion valuation in early 2026. It is the best-funded pure-play in the category. That funding is visible in the feature depth and the Fortune 500 client roster, and also in the pricing.

Peec AI

Peec's three base engines are ChatGPT, Perplexity, and Google AI Overviews. Additional engines cost roughly $35/month (Starter) to $165/month (Advanced) per engine. All plans include unlimited seats, which is a genuine differentiator for agencies managing multiple client users.

Peec raised $29M and reached $4M+ ARR in its first ten months, which tells you something about real demand for browser-simulation-based tracking.

Citerank

The free tier is the most underreported fact in this category. Three full AI visibility audits per month, across six engines, with a fix stack, no credit card. Most teams that are just starting out do not need to spend money until they understand what they are looking at.

Cost-per-prompt math: what you actually pay to track one question

No other comparison in this space has run this calculation. Here it is.

To track 100 prompts across three AI engines per month:

At 100 prompts across three engines, Profound costs 5x what Citerank costs per data point. Otterly and Peec are closer to each other at around $0.82-$0.83, but both run three times the cost of Citerank at comparable volume.

This math changes as you scale. Peec's unlimited-seat model keeps total cost flat as you add team members. Profound's enterprise tier brings the per-prompt cost down significantly at thousands of prompts. For mid-market teams under 200 prompts per month, the numbers above are representative.

The prompt design problem: what no tool solves

This is the part that every comparison article skips, and it is the most important factor in whether any of these tools produces useful data.

Your citation tracking is only as good as the prompts you choose to track. Every tool in this category, including mine, takes a list of prompts you supply and tracks your brand's appearance in responses to those specific questions. None of them help you design that list.

This matters because AI engines respond very differently to slight changes in phrasing. "What is the best project management tool for remote teams?" produces different citations than "How do remote teams manage projects?" The same brand may appear in one and not the other. If you load your prompt set with branded or overly narrow questions, you will see inflated visibility numbers that do not reflect real user behavior.

The questions you track should come from actual user queries. That means:

1. Pull the actual questions people type into AI engines for your category. Tools like Peec's strategy recommendations can surface some of these, but they are not comprehensive.
2. Use fan-out query research, map out how a real user's conversation branches across follow-up questions after the first response.
3. Include competitive prompts, questions where you should appear but currently do not.

A 50-prompt list built from real user intent research will produce more useful data than a 200-prompt list built from keyword brainstorming. None of the four tools in this comparison help you build that list. That design work happens before you open any of them.

The measurement trap: what visibility data cannot tell you

Every tool covered here measures the same thing: whether your brand appears in AI responses, how often, in what position, and with what sentiment. That data is useful. It is not sufficient.

Here is what it cannot tell you:

Why you are not cited. If your citation rate is 12 percent on a tracked prompt set, you know you appear in 12 out of 100 responses. You do not know whether that is because AI crawlers cannot access your site, because your schema markup is absent, because your content lacks direct answers, because your entity is not recognized in the knowledge graph, or because a competitor simply has more authoritative sources pointing at them. The monitoring data identifies the symptom. The diagnosis requires a separate audit.

Whether your content will be cited on new prompts. A citation tracker shows historical data for the prompts you are already tracking. It does not tell you whether a new prompt, one you have not set up yet, would cite you. Structural audit scores are a better leading indicator for new coverage.

Conversion impact. AI-referred traffic converts at 2.4x the rate of standard organic search in some studies, but connecting citation data to pipeline requires instrumentation on your side: UTM parameters on cited URLs, CRM tracking tied to referral source, and conversion definitions that your monitoring tool knows nothing about. None of the four tools in this comparison close that loop automatically.

This is not a criticism of any specific platform. It is a category limitation. The monitoring layer and the diagnostic/optimization layer are separate problems. Citerank attempts to connect them in one interface, the "Why Am I Not Cited" tool runs a structural audit alongside citation tracking, but even that is not a complete answer-to-pipeline attribution system.

Who should use which tool

Use Otterly if: You are a solo marketer or a small team starting out with AI visibility. The $29/month Lite tier gives you a real baseline for minimal spend, and the UX is the most accessible of the four. Accept that 15 prompts is a very small window and that multi-engine tracking requires add-on budget.

Use Profound if: You are a Fortune 500 marketing team with a dedicated AI search owner and a budget that treats $399/month as a rounding error. Profound's 1.5B+ prompt dataset for demand research is genuinely differentiated and has no equivalent in this comparison. If you need to understand what users are actually asking AI in your category, not just track your own brand, Profound is the only tool with that data layer.

Use Peec AI if: You are an agency or mid-market brand where data fidelity matters more than price per prompt. Browser simulation is the most accurate way to capture what users actually see. Unlimited seats at any tier is the right economics for agency use. Budget in euros and confirm current pricing directly, it has changed frequently.

Use Citerank if: You want to start free and understand what is structurally wrong with your AI visibility before you start tracking prompts. The free AI Visibility Score runs a full 35-signal audit across six engines and gives you a prioritized fix stack. The paid tiers add citation tracking, brand monitoring, and for agencies, white-label PDF reports and WordPress deployment of fixes. The cost-per-prompt math is the most favorable of any tool in this comparison.

The honest limitation of building in this category

I want to say something I am not legally required to say: building an accurate AI citation tracker is harder than it looks, for everyone in this space.

AI engines are not static databases. They update their models, change their retrieval logic, modify their UI, and rate-limit API access, often without announcement. A tool that was accurately capturing Perplexity citations in March may be seeing degraded data in August because Perplexity changed something in how they handle API versus browser requests. Every team in this category is continuously chasing those changes.

That means the pricing you pay is partly paying for the infrastructure cost of staying current. When a tool seems too cheap, it is worth asking whether the data quality is keeping pace with engine changes. When a tool seems expensive, ask whether the premium is going into actual data quality or into sales and marketing.

No one in this space has perfect data. The question is whether the trend data, your citation rate over time, your share of voice versus competitors, is directionally reliable. For all four tools covered here, the answer is generally yes on the directional metrics. Treat the absolute numbers with appropriate skepticism and focus on relative changes.

What to do this week

1. Run the free audit first. Before you spend on any monitoring tool, use the Free AI Visibility Score to understand your structural baseline. If your site has fundamental issues with AI crawler access or schema markup, citation tracking will show you bad numbers but not tell you why. Fix the foundations before you instrument the metrics.

2. Design your prompt list before you pick a tool. Thirty well-chosen prompts that map to real user intent will outperform three hundred generic keyword-style prompts. Draft that list first. It will also help you estimate how many prompts you actually need, which determines which pricing tier makes sense.

3. Start with one engine. Tracking six engines across 100 prompts from day one produces a lot of data and not much clarity. Start with the engine your audience uses most, likely ChatGPT or Perplexity for B2B, Google AI Overviews for consumer, and add engines once you have a working process for acting on the data.

4. Define what action you will take at each citation rate. If your citation rate drops below 20 percent, what changes? If a competitor surges to 60 percent on your key prompts, who gets notified and what do they do? The monitoring tool is the easy part. The response process is the part most teams skip.

Frequently asked questions