Analytics and monitoring decision
Mentionable
A focused, single‑customer replacement (prompt generation, scheduled scans, storage, audits and an MCP-like API) is realistic for a competent developer, but reproducing Mentionable's multi‑LLM coverage, historical dataset and production-grade MCP offering at scale is not practical as a small DIY project.
Visit website↗$79/mo
$948/yr
Read off the official pricing page.
$100one-off58 h to build
$50/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 1 seat.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Mentionable alternatives, with the arithmetic →
What a replacement has to do
- Generate market prompts from a target URL, run those prompts against LLMs or LLM-surrogates on a schedule, detect and extract domain citations and fan-out queries, store and analyse results, surface alerts/audits and an API for agent access.
What it still won’t have
- Scale and coverage across seven LLMs with maintained connectors
- The vendor's historical dataset (e.g. +130K prompts executed/month) and aggregated market signal
- White‑label reports, agency features and priority support
- Production hardened MCP server with deprecation guarantees and documented tool shapes
What remains hard
- Proprietary data
+130K prompts exécutés chaque mois
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 1 seat.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal 'IA visibility' analytics service in Node.js (Express) + Postgres + cron scheduler (or serverless cron) that: (1) accepts a target URL and auto-generates a set of market prompts; (2) schedules scans and issues prompts to selected public LLM APIs (start with 2: OpenAI + one open alternative) and captures responses; (3) extracts domain citations and fan-out queries from responses and stores them in Postgres; (4) provides a Page Audit job that fetches cited pages, runs basic HTML/metadata checks and scores pages; (5) exposes an HTTP JSON API compatible with the MCP tool shapes (list_prompts, list_fan_outs, list_llm_sources, get_page_audit); (6) includes a small web dashboard to view prompts, recent scans and alerts for new citations. Out of scope: full support for 7 LLMs, multi-tenant billing, white-label reports, and agency-scale overage handling. Require error handling for network failures, retries and backoffs, basic unit/integration tests for API and scheduler, and Docker-compose for local dev.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score64
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productMentionable — official product
- official pricingMentionable Pricing
- official docsMentionable MCP docs
- open sourcelangfuse/langfuse
- open sourceopenobserve/openobserve
Integrity checks
What held up, and what did not.
