Best LLM Analytics Platforms for Brand Visibility in 2026
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The LLM analytics platforms, reviewed
5 tools- 01RankryDefined metrics, MCP access
- 02Peec AIAnalytics teams
- 03Otterly AIDeveloper access from Standard
- 04SearchableAnalytics plus action
- 05ZipTiePublished scoring
Checked October 25, 2026
In short
An LLM analytics platform is a tool that turns AI answers into brand metrics: how often large language models name you, where they rank you, and how that differs by model and over time. Here we compare Rankry, Peec AI, Otterly AI, Searchable and ZipTie on the metrics they report and how you get the data out. Rankry defines six core metrics across nine AI engines and lets Claude Code, Cursor and compatible MCP clients query them through its MCP server.
Side by side
How do LLM analytics platforms compare?
| Tool | Best for | Getting data out | Entry price | Free trial |
|---|---|---|---|---|
| Rankry | Defined metrics, MCP access | CSV export; MCP server with 21 tools | From $79/mo (annual) | 7-day Base trial, no card |
| Peec AI | Analytics teams | CSV exports and MCP; Looker Studio on Advanced; API on Enterprise | $95/mo (Starter, 50 prompts, 3 models) | Yes (length not public) |
| Otterly AI | Developer access from Standard | API and MCP from Standard | $29/mo (Lite, 15 prompts) | 7 days, no card |
| Searchable | Analytics plus action | MCP on every plan; API on Enterprise tiers | $125/mo (Pro, 100 prompts per model) | 14 days, no card |
| ZipTie | Published scoring | API and MCP as an add-on | Pay per check ($0.01); presets from $25.65/mo | 7 days, no card |
Competitor details verified on each vendor’s own website on October 25, 2026. “Not public” means we could not find it stated there. Rankry makes this page and lists itself first.
Criteria
Which metrics should an LLM analytics platform report?
Analytics buyers care about definitions and data access more than dashboards. We read each vendor’s site for the metrics it names and the ways it lets data leave the tool.
- Clear definitions: what counts as a mention, a position, a share
- Breakdowns by model, by topic and by period
- History you can open run by run
- The raw answer behind each number
- Competitor benchmarks on the same prompts
- Ways to get data out: CSV, API, BI connectors, MCP
- How the tool handles answers that change from run to run
The tools
The LLM analytics platforms, reviewed
- 01Rankry
Defined metrics, MCP access
Rankry reports Visibility (the share of answers that name you), Position (your average rank when named), Share of voice (you against the competitors you track), Sentiment (Positive, Neutral or Negative per answer, from labelled claims), Diversity (how many engines name you) and a Rankry Score that combines them. A Model comparison table shows each metric per engine, Visibility by topic splits it by prompt category, and periods run from 7 days to 12 months within your plan’s history. Data leaves as CSV or through an MCP server with 21 tools. Plans run from $79/month billed annually to $249/month, with a 7-day free trial.
Try Rankry free - 02
Peec AI
Analytics teams
Peec AI is the most analytics-shaped tool in this list. It tracks visibility, position, sentiment and share of voice daily, classifies prompts as branded or non-branded and by intent, and breaks results down by model, topic and country. Data leaves through CSV exports and MCP, with a Looker Studio connector on Advanced and the API on Enterprise.
Rankry vs Peec AI - 03
Otterly AI
Developer access from Standard
Otterly AI gives developers a public API and an MCP server from its Standard plan. Reports cover citations and competitor gaps across ChatGPT, Perplexity, Google AI Overviews and Copilot, with other engines as add-ons.
Rankry vs Otterly AI - 04
Searchable
Analytics plus action
Searchable puts as much weight on acting as on analysis: tracking sits next to content generation, site audits and an in-app agent. For data access it lists MCP on every plan and API access on its Enterprise tiers, plus GA4 and Search Console connections.
Rankry vs Searchable - 05
ZipTie
Published scoring
ZipTie reports citation rate, mention rate and sentiment, and publishes the formula behind its AI Success Score, which combines them. It stores the full answer and cited sources per check; API and MCP access is a $10 monthly add-on.
Rankry vs ZipTie
Choosing
How do I pick an LLM analytics platform?
If you report in your own BI stack
Choose a platform with a connector or API. Peec AI lists Looker Studio on Advanced; Otterly AI’s API starts on Standard.
If you want answers, not exports
Rankry Brain answers plain questions about your data with numbers and charts, and shows which tools it used to check. The same data is open to Claude Code, Cursor and compatible MCP clients.
If metric definitions matter
Ask how visibility is computed. In Rankry, Visibility counts whether an answer names you, regardless of position, and Share of voice compares you only with the competitors you track.
If you compare models
Look for a per-model table. Rankry’s Model comparison shows visibility, position, sentiment and diversity for each engine side by side.
Explore
Related
What is an LLM analytics platform?
An LLM analytics platform measures how large language models mention, rank, cite and describe a brand, and reports those as repeatable metrics per model and over time. It is the analytics layer for AI answers, the way web analytics is for site visits.
How is LLM analytics different from SEO analytics?
SEO analytics measures rankings, clicks and traffic on search results pages. LLM analytics measures what AI models say inside their answers, including answers that never send a click. The two overlap, which is why Rankry also connects Google Search Console.
Which metrics matter most in LLM analytics?
Start with visibility (how often you are named), position (where you are listed when named), share of voice (you against competitors), sentiment (how you are described) and citations (which sources the answers rely on). Track them per model, because engines often disagree.
Why do LLM analytics numbers change between runs?
AI models write each answer fresh, and web search results change, so the same prompt can name different brands from one run to the next. Read trends across several runs. Rankry asks each prompt once per engine per run and shows the run-by-run history behind every average.
Can I get LLM analytics into Looker Studio or my BI tool?
Peec AI lists a Looker Studio connector on Advanced. Rankry exports reports as CSV and exposes your data through its MCP server; it has no Looker Studio connector. Otterly AI and ZipTie offer APIs, on Standard and as an add-on respectively.
Can I query my AI visibility data from Claude or ChatGPT?
With Rankry, yes. Its MCP server works with Claude Code, Cursor and compatible MCP clients through an API key, offers 21 tools, and is read-only except for running a PageSpeed audit. Searchable, Peec AI, Otterly AI and ZipTie also list MCP access on their own terms.
How far back does LLM analytics history go?
In Rankry, history follows your plan: 90 days by default, with presets up to 12 months on plans that allow it. Peec AI shows the last 100 chats per prompt in the app.
What is share of voice in LLM analytics?
Share of voice is your share of brand mentions compared with the competitors you track, across the same prompts and engines. In Rankry it covers you plus your tracked competitors only, and the Compete page breaks it down per engine.
See where your brand stands
Track your brand in ChatGPT, Claude, Gemini, Perplexity, Grok, Meta Muse, Google’s AI surfaces and Copilot, then fix what keeps AI from naming you.
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