Scrunch vs. Semrush: Which tracks AI visibility better?
HubSpot compares Scrunch and Semrush for tracking AI answer engine visibility, highlighting their distinct approaches and practical differences for brands.
HubSpot’s guide evaluates Scrunch and Semrush as tools for measuring AI answer engine optimization (AEO), where visibility means being cited or named in AI responses rather than ranking in search results. Scrunch is designed specifically for AEO, monitoring how brands appear in AI answers across multiple engines, while Semrush integrates AEO tracking into its broader SEO suite. The comparison assesses tools based on coverage, citation tracking, reporting, and workflow integration, with each section concluding with a recommendation tailored to specific use cases.
Scrunch’s Core plan covers four engines—ChatGPT, Perplexity, Google AI Overviews, and Copilot—at $250 per month, with additional engines requiring an Enterprise plan. Semrush’s AI Visibility Toolkit includes five engines—ChatGPT, Gemini, Perplexity, Google AI Overviews, and AI Mode—across its reports, with coverage varying by plan. Scrunch allows prompts to be assigned to multiple engines and supports global tracking, while Semrush’s Prompt Tracking updates daily and uses a compiled database of AI search queries for discovery reports.
Both tools track citations, but Scrunch records them at the URL and domain level, tagging ownership and flagging mixed-ownership domains, while Semrush groups cited external domains to highlight competitor advantages. Scrunch auto-detects competitors and benchmarks share of voice and sentiment, whereas Semrush provides side-by-side competitor reports and flags high-consensus prompts where rivals appear without your brand. Scrunch defaults to a 12-week trend view for visibility and citation shifts, while Semrush combines AI-visibility trends with its long-standing SEO database in a single interface.
The guide advises testing tools during free trials by verifying engine coverage, confirming localized answers for non-US markets, and reviewing how each tool collects and samples responses. It also recommends inspecting raw answers and understanding the limitations of AI answer engines, which may return different citations for the same prompt due to their probabilistic nature. Users are encouraged to confirm specific features and pricing before committing to a plan.