Search-Visibility.AI vs heeb | LLM Mentions API vs SiteSignal
A side-by-side comparison of Search-Visibility.AI vs heeb | LLM Mentions API vs SiteSignal — pricing, ratings, strengths and weaknesses — to help you pick.
Search-Visibility.AI отслеживает, как модели ИИ, такие как ChatGPT и Gemini, упоминают ваш бренд.
- ЦеныFree · $29/month
- Рейтинг⭐ 5.0/5
- API—
- Открытый код—
Плюсы
- Tracks brand visibility across multiple major AI models simultaneously
- Reveals brand mention frequency and contextual details over time
- Helps identify brand perception shifts in AI-generated responses
- Free trial available to test features before purchasing
Минусы
- Does not provide user engagement or impression volume metrics
- Limited to monitoring specific AI models with possible coverage gaps
- Requires ongoing monitoring to identify meaningful visibility trends
heeb — это LLM Mentions API, который объединяет мониторинг бренда по нескольким ИИ-моделям в едином структурированном интерфейсе.
- ЦеныFree · $10/unit
- Рейтинг⭐ 5.0/5
- API—
- Открытый код—
Плюсы
- Unified interface eliminates querying multiple LLMs separately
- Covers major AI models in a single structured request
- Provides standardized mention data for comparative analysis
- Enables scalable brand monitoring across AI platforms
Минусы
- Limited to specific LLM partners; coverage gaps with newer models
- API pricing and rate limits may impact large-scale monitoring
- Mention analysis depends on underlying model accuracy and bias
SiteSignal отслеживает восприятие вашего бренда в результатах поиска на основе ИИ и выявляет негативные источники цитирования.
- ЦеныFree · $49/year
- Рейтинг⭐ 4.9/5
- API—
- Открытый код—
Плюсы
- Tracks brand sentiment specifically within AI search results
- Identifies which citations drive negative mentions
- Provides actionable insights for reputation management
- Real-time monitoring across AI-generated responses
Минусы
- Limited to monitoring your own domain mentions
- Effectiveness depends on AI system coverage and indexing
- May require ongoing optimization of tracking parameters