Best LLM Visibility Software: 14 Tools Compared for 2025

Each method has trade-offs. API monitoring provides the cleanest data but depends on platform cooperation. Synthetic queries offer broad coverage but may not perfectly replicate how real users phrase questions. User simulation generates authentic results but requires significant computational resources.

The best tracking tools blend methods to provide complete pictures of your AI search visibility across platforms and queries.

14 top LLM visibility tools compared

Now for the detailed analysis of each tool, including use cases and differentiators.

1. Profound

Profound is an enterprise-grade AI visibility platform built for large organizations with global operations.

Key strength: SOC 2 Type II compliance and multi-region support make this the default choice for Fortune 1000 companies with strict security and privacy requirements.

Best for: Enterprise organizations needing comprehensive visibility across markets, robust security, and white-glove support.

Pricing: Custom enterprise pricing based on query volume and platform coverage.

Unique features:

• White-glove onboarding
• Demographic filters for audience segmentation
• Advanced reporting with custom dashboards

Profound is overkill for startups or small teams, but it's a strong option when you need enterprise-level reliability and compliance for AI SEO initiatives.

2. Semrush Enterprise AIO

Semrush Enterprise AIO extends traditional SEO tracking into AI search visibility. It's a natural fit for teams already using Semrush.

Key strength: Integrates AI search tracking with existing SEO workflows, providing unified visibility across search engines and AI platforms.

Best for: Companies already using Semrush for SEO who want to add AI search monitoring without switching platforms.

Pricing: Add-on to enterprise plans, with costs varying based on existing contract and feature needs.

Unique features: Unified dashboard for search and AI visibility, allowing teams to compare performance across channels and identify shifts in demand as Google AI overviews gain prominence.

If you're not already a Semrush customer, the cost and complexity may not justify switching. But for existing users, this offers the smoothest path to AI search visibility monitoring.

3. Lunary

Lunary is an open-source observability platform designed for technical teams building or monitoring LLM applications.

Key strength: Model-agnostic tracking with a free tier makes this accessible for developers and small technical teams.

Best for:

• Developers
• Product teams
• Startups needing basic monitoring without enterprise pricing

Pricing: Free tier available; paid plans start at $29/month and scale based on usage.

Unique features:

• LangChain integration
• Prompt evaluation tools
• Developer-friendly APIs

Lunary won't work for non-technical marketing teams, but it's a strong option for product-led growth companies where engineering and marketing collaborate closely on AI SEO.

4. Langsmith

Langsmith is LangChain's integrated monitoring solution, purpose-built for teams building LLM applications.

Key strength: Deep integration with the LangChain ecosystem, offering trace analysis and debugging capabilities.

Best for: Teams already building LLM applications with LangChain who need monitoring as part of their development workflow.

Pricing: Usage-based pricing model that scales with application volume.

Unique features:

• Trace analysis
• Debugging capabilities
• Performance optimization tools

This is a developer tool, not a marketing tool. If your team builds LLM-powered features, Langsmith helps you track performance. For brand visibility monitoring in AI search, you'll need a different solution.

5. Trackerly.ai

Trackerly.ai tracks brand mentions across all major AI search platforms. The platform provides daily automated updates and multilingual support.

Key strength: Comprehensive platform coverage, including ChatGPT, Google Gemini, Claude, Perplexity, and DeepSeek in one dashboard.

Best for: Agencies and brands needing to track visibility across AI search engines without juggling separate tracking tools.

Pricing: Free 7-day trial with tiered plans based on prompt volume.

Unique features:

• Bulk prompt management
• Position tracking within AI search responses
• Source citation tracking
• No language limitations for prompts

Trackerly.ai's position tracking metric reveals where your brand appears within AI search outputs. Mentions at the top of responses drive more visibility than mentions buried at the bottom.

6. Brand Signal AI

Brand Signal AI helps marketers monitor brand mentions and prominence in AI model responses like ChatGPT and other chatbot platforms.

Key strength: Campaign-based monitoring that lets you organize prompts by initiative or product line.

Best for: Marketing teams tracking multiple products or campaigns that need organized monitoring across initiatives.

Pricing: Premium subscription model with campaign-based organization.

Unique features:

• Campaign structure for organizing prompts
• Prominence tracking
• Visibility trends over time

Brand Signal AI's campaign organization helps larger marketing teams coordinate monitoring efforts. This is invaluable if you're tracking across products, regions, or initiatives.

7. Peec AI

Peec AI focuses on real-time monitoring for agencies and mid-size brands tracking AI search visibility.

Key strength: User-friendly dashboards and customizable alerts make this platform accessible for marketing teams without deep technical expertise.

Best for: Agencies managing multiple clients and mid-size brands needing clear reporting on AI search performance.

Pricing: Transparent tiered pricing based on query volume and platforms monitored.

Unique features:

• Customizable alerts
• Client reporting templates
• Multi-account management

Peec AI strikes a balance between functionality and usability, making it a solid choice for teams wanting comprehensive AI search monitoring without enterprise complexity.

8. PromptWatch

PromptWatch specializes in prompt-level monitoring and optimization, helping teams understand which queries drive visibility in AI search.

Key strength: Granular prompt performance tracking that reveals exactly which queries generate brand mentions.

Best for:

• Prompt engineers
• QA teams
• Content strategists needing detailed query analysis

Pricing: Developer-friendly pricing with transparent tiers.

Unique features: A/B testing for prompts allows teams to experiment with different query phrasings and optimize for visibility in AI search results.

This tool is more specialized than others on this list. It works best when paired with broader tracking tools, providing depth where others offer breadth.

9. Geo Suite

Geo Suite provides comprehensive generative engine optimization (GEO) capabilities beyond basic monitoring.

Key strength: Full GEO platform that not only monitors visibility but also provides recommendations for improving it across AI search engines.

Best for: SEO teams expanding into AI SEO optimization that want actionable recommendations alongside monitoring data.

Pricing: Professional pricing tiers based on features and query volume.

Unique features:

• GEO recommendations
• Content optimization suggestions
• Competitive analysis

Geo Suite's strength is optimization guidance. If you want a tool that tells you what to fix in your AI SEO strategy, not just what's broken, this one's worth evaluating.

10. SearchAtlas AI Visibility

SearchAtlas AI Visibility emphasizes historical trend analysis to help teams understand how AI search visibility changes over time.

Key strength: Long-term visibility tracking that reveals patterns and predicts future trends in AI search.

Best for: SEO and SEM managers wanting to correlate AI search visibility with broader search performance.

Pricing: Competitive mid-market pricing with transparent tiers.

Unique features:

• Historical data analysis
• Trend predictions
• Correlation with traditional search metrics

SearchAtlas works well for teams thinking strategically about content and wanting to understand how AI search visibility evolves alongside traditional SERP performance.

11. MentionedBy AI

MentionedBy AI monitors brand visibility across 20+ AI models with competitive benchmarking.

Key strength: Extensive model coverage including base models, search models, and premium models with competitive comparison features.

Best for: Brands focused on competitive intelligence that need to track their position against rivals across many AI search platforms.

Pricing: Tiered plans from $89/month (Starter) to custom enterprise pricing, with weekly or daily updates depending on plan.

Unique features:

• Competitive benchmarking against up to 6 competitors
• AEO (Answer Engine Optimization) analytics
• Sentiment insights

MentionedBy AI's broad model coverage helps teams understand visibility patterns across the entire AI search landscape rather than focusing on a few dominant platforms.

12. Truera LLM

Truera LLM focuses on explainability and compliance, making it ideal for regulated industries.

Key strength: AI explainability features that help teams understand why models generate specific responses.

Best for: Regulated industries like finance, healthcare, and legal services where compliance matters.

Pricing: Premium enterprise pricing reflecting specialized compliance features.

Unique features:

• Compliance reporting
• Risk assessment
• Detailed explainability for AI-generated content

Truera is expensive, but if you're in a regulated industry, the compliance features justify the investment for AI SEO monitoring.

13. W&B LLM Monitor

W&B LLM Monitor provides research-grade monitoring tools for teams needing deep analytics.

Key strength: Deep analytics and experimentation capabilities designed for ML researchers and data scientists.

Best for:

• ML researchers
• Data scientists
• Technical teams building sophisticated LLM applications

Pricing: Academic and enterprise tiers with flexible licensing.

Unique features:

• Experiment tracking
• Model comparison
• Detailed performance analytics

This is overkill for most marketing teams. But if you have data scientists who want to analyze AI search visibility alongside model performance, W&B offers unmatched depth.

14. Hall

Hall offers a self-serve platform with a free tier, making it accessible for startups and small teams.

Key strength: Accessible pricing with a free option that lets teams start monitoring without upfront investment.

Best for:

• Startups
• Small teams
• Companies testing AI search visibility monitoring before committing budget

Pricing: Freemium model with paid tiers adding features and query volume.

Unique features:

• Self-service setup
• Basic tracking
• Straightforward reporting

Hall won't scale to enterprise needs, but it's a smart entry point for teams wanting to understand AI search visibility before investing in more sophisticated tracking tools.How to choose the best AI visibility products for your business

We recommend this decision framework for choosing the right tool based on your needs and limitations.

1. Define your objectives

Start by clarifying your AI visibility goals. Are you focused on brand awareness, lead generation, competitive intelligence, content optimization, or compliance?

Ask yourself:

• What AI platforms do your customers use most?
• How critical is real-time monitoring for your business?
• Do you need historical data for trend analysis?
• What’s your team’s technical capability?

Your answers will narrow down your options. If your buyers mostly use ChatGPT, you don’t need a tool that monitors 14 different AI platforms. If your team lacks technical expertise, cross out developer-focused tools.

Consider your compliance and security requirements, too. Regulated industries need tools with strong data governance. Startups can focus on features over compliance certifications.

2. Evaluate technical integrations

Think about how the tool integrates with your existing systems. Check API availability and limitations—some platforms restrict automation querying, which can limit functionality.

Assess data export capabilities. You’ll want to analyze AI visibility alongside other marketing metrics, so easy exports to your BI tools or data warehouse are important.

Verify compatibility with your existing martech stack. If you use Semrush for SEO, adding their AIO module is simpler than learning a new platform. If you use Salesforce, tools that integrate with CRM data help connect AI visibility to your pipeline.

Decide on your custom reporting requirements. Some teams need white-labeled reports for executives, while others want raw data for analysis.

3. Check reporting and analytics features

Look for dashboard customization options that let you cover the metrics your team cares about. Generic dashboards rarely provide the insights you need.

Evaluate alert configuration flexibility. Can you set up alerts for specific competitors, sentiment thresholds, or mention patterns? Flexible alerts help you respond quickly to changes.

Consider multi-user access and permissions. Enterprise teams need role-based access, and small teams often prioritize simplicity over specific permissions.

4. Consider pricing and scalability

Calculate the total cost of ownership, not just base pricing. Factor in implementation time, training costs, and ongoing management effort.

Assess scaling costs as usage grows. Usage-based pricing gets expensive as you add queries, platforms, or users.

Review contract terms and flexibility. Annual contracts lock you in, but month-to-month plans give you flexibility to test and adjust.

Make a plan for measuring ROI. Great tools translate better AI visibility into business gains, like a bigger pipeline or lower customer acquisition costs.

Steps to implement an LLM visibility strategy

Ready to put your LLM visibility strategy in place? Follow these steps:

1. Identify priority platforms

Rank AI platforms by analyzing where your audience searches. Survey customers, review sales call recordings, or review support tickets to understand which AI tools buyers use.

Then find out where your competitors are. If they dominate ChatGPT but ignore Perplexity, you might find opportunities in underserved platforms. Consider platform growth paths—platforms gaining market share deserve attention, even if they’re not dominant today.

And be sure to align platform priorities with your business goals. Brand awareness efforts might target broad platforms, while enterprise sales focus on research-heavy platforms like Perplexity.

2. Set up monitoring and auditing

Before using any tool, do an initial baseline audit. Document current visibility to measure improvement.

Your team should also know how to use the tool, even if it’s intuitive. All platforms need training so teams can get the most value from them. As you’re learning to use the tool, document everything. Query sets, alert configurations, and reporting processes shouldn’t all live in one person’s head.

3. Establish KPIs and baselines

Choose meaningful metrics that tie back to your business. Share-of-voice matters more than raw mention counts if you’re in a competitive market. Set realistic targets based on your audit and resources. Doubling AI visibility in 30 days is unrealistic, but 20% improvement in 90 days is achievable with focused effort.

Your measurement cadences should balance insight with efficiency. With monthly analysis, you can spot trends as they happen, but with weekly reporting, you get more timely feedback.

KPIs and business goals must go hand in hand. If your goal is to grow your pipeline, track mentions in response to bottom-funnel queries, not just total visibility.

4. Build a response plan for updates

As you receive alerts, monitor and respond to them quickly. Sudden drops in visibility or negative sentiment shifts need quick action. Be sure you have content update workflows for responding to opportunities. If AI mentions competitors instead of you, update your content to fill in the gaps.

Finally, develop competitive response strategies. When your competitors gain visibility, figure out why and change your approach. Regular performance reviews help you assess what’s working and what needs adjustment. A monthly cadence forces you to stay proactive rather than reactive.

How to improve LLM visibility after monitoring

These tactics can help you improve AI visibility based on what you observe:

• Optimize content for AI consumption. AI platforms prefer clear, well-structured content with authoritative sources. Break complex topics into scannable sections, use descriptive headers, and cite credible sources.
• Build topical authority in your niche. Full coverage of related topics signals expertise to AI platforms. Create clusters of related content that position your brand as the go-to resource.
• Increase citation likelihood by creating reference-worthy content. Original research, data-driven insights, and detailed guides earn citations from AI platforms more often than promotional content.
• Address negative sentiment proactively. If AI platforms cite outdated or inaccurate information, update your content and engage with the sources AI references to correct the record.

Here are some other things you can do to ensure AI pays attention to your content:

• Structured data implementation: Schema markup helps AI understand your content structure and authority. Organization, article, and FAQ schema improve how AI reads your pages.
• Authoritative content creation: Publish research, case studies, and thought leadership that AI can recognize as expertise and that other sites can cite.
• Strategic partnerships: Get more mentions through collaborations with publications and platforms AI frequently cites. Guest posts, data partnerships, and research collaborations all boost visibility.
• Thought leadership: Showcase your execs as industry experts through speaking engagements, podcast appearances, and bylined articles. Personal brands often drive company mentions in AI responses.

Real-world growth tactics to increase brand presence

If you want to make your brand more visible, it comes down to execution. Some strategies have been shown to bring improvements over time:

• Create AI-optimized content hubs. Build resource centers that go deep into topics. AI platforms like these sites.
• Develop definitive guides that AI references. Long-form, authoritative guides are reference material for AI platforms. Update them regularly to maintain accuracy and relevance.
• Build relationships with sources often cited by AI. If your monitoring shows that AI platforms always cite certain publications, pitch those publications with data and insights.
• Optimize for featured snippets. Many AI platforms draw from featured snippets in traditional search results. Structured answers, clear definitions, and concise explanations improve both.

What does this all look like in practice? Runpod, a GPU cloud computing platform for AI model deployment, turned ChatGPT into a top acquisition channel. They started monitoring their AI visibility and fixing technical issues that prevented proper citation. As a result, they grew to 40 new customers per day from ChatGPT with an 8% conversion rate.

Moving forward with sustainable LLM visibility

AI visibility is becoming as important as traditional search visibility. Over time, AI platforms develop preferences for sources and brands. If you know where you stand early, you can take advantage of opportunities to optimize your presence.

But monitoring alone isn’t enough. You have to act on those insights, producing better content, securing stronger partnerships, and optimizing consistently.

If you’re ready to build a comprehensive AI visibility strategy, Ten Speed can help. Our team has extensive technical experience with proven content strategies. We help B2B SaaS companies optimize for AI platforms while building sustainable organic growth across channels.

Book a call with our team today to review your company’s growth goals and get a customized proposal!

Frequently asked questions about LLM visibility software

What are the privacy concerns in storing LLM monitoring data?

Most enterprise LLM monitoring tools follow SOC 2 and GDPR standards, storing only aggregated response data without personal information. But you should verify each platform's specific data retention and privacy policies beforehand. Ask vendors about data storage locations, retention periods, and whether they use your data to train models.

How can I measure direct ROI from these tools?  

Track pipeline influence by monitoring which prospects mention AI-sourced information during sales calls. Correlate improvements in AI visibility with increases in branded search traffic and direct website visits. The strongest ROI signal comes from connecting visibility improvements to pipeline growth, which requires tagging opportunities by source and analyzing patterns over time.

Are there regulations that limit how I collect LLM data?  

Current regulations don't specifically restrict LLM monitoring, but platforms' terms of service may limit automated querying. Choose tools that have compliant relationships with AI providers to avoid service disruptions. Some platforms offer official monitoring APIs, while others use workarounds that may face restrictions as AI providers update their policies.