LLM Pulse ↗
Recurring answer monitoring without operating a self-hosted stack is preferred.
← All toolsAnother direction / 01 Another direction / 02 Another direction / 03
The selection guideSource-backed research ↗
AI Search Monitoring / Open-source, self-hosted AI visibility tracker
Cheap Seats
Open-source, self-hosted AI visibility tracking for buyer questions
AI visibility measurementSelf-hosted GEO operationsOpen-source analytics
Why it stands out
Free MIT-licensed software with inspectable implementation.
Source review: · Public-source research, not a hands-on test
↗ Best for
Developer-led GEO and technical SEO teams that can self-host.
! Know before you buy
No checked source proves grader accuracy, citation precision, repeatability, causal visibility lift, traffic, or revenue outcomes.
01 / Editorial assessment
Is Cheap Seats for you?
The AICiteKit take
Cheap Seats is a transparent open-source option for teams that want to own the AI-visibility measurement loop. Official documentation describes buyer-question prompts, search and recall tracks, recommendation grades, cited evidence, fix briefs, a dashboard, CLI, JSON API, and MCP server. The public repository adds inspectable Go code, tests, Docker deployment, and SQLite storage. Evidence is stronger for advertised scope and implementation than for efficacy: no checked source proves grader accuracy, citation precision, repeatability, causal visibility lift, traffic, or revenue outcomes. It merits a controlled pilot for engineering-led teams that can pin prompts, models, configuration, and raw answers.
A good fit if you are…
- Developer-led GEO and technical SEO teams that can self-host.
- Organizations wanting raw answers, citations, and prompt configuration under their control.
- Teams comparing build-versus-buy options for AI visibility measurement.
Consider another option if…
- Buyers wanting turnkey hosted SaaS onboarding.
- Teams requiring broad multi-engine coverage without operating integrations.
- Organizations requiring independently audited measurement accuracy or managed compliance evidence.
02 / Scope, not promises
Features & coverage
- Documented answerers
Claude and OpenAI answerers with separate search and recall tracks; verify current provider integrations.
Archived research · Official documentation checked 2026-10-26Research excerpt · Quick facts- Measurement workflow
Buyer questions, model responses, recommendation grades, cited evidence, themes, competitors, and page-level fix briefs.
Archived research · Official documentation and repository checked 2026-10-26Research excerpt · Measurement and evidence handling- Operating costs
Software is free; model API, hosting, subscriptions, and optional provider passes can cost money.
Archived research · Official quick-start and cost documentation checked 2026-10-26Research excerpt · Pricing, model costs, and limits
Buyer-question visibility runs
Configure brand questions, competitors, answerers, samples, and budgets, then compare search and recall tracks.
Evidence and recommendation grading
Inspect answers, cited URLs, evidence quotes, recommendation grades, themes, objections, and competitor context rather than only a summary score.
Self-hosted operations and integrations
Run the dashboard with Docker or Go, and use the CLI, JSON API, Streamable HTTP MCP, SQLite, and deployment material documented in the repository.
Evidence boundary
No checked source establishes grader calibration, citation accuracy, customer satisfaction, causal lift, traffic, or revenue outcomes.
A focused evaluation workflow
- Configure brand, buyer questions, competitors, answerers, samples, and budgets.
- Run search and recall tracks through Docker/API or local subscription CLIs.
- Inspect answers, cited evidence, grades, themes, and page-level fix briefs.
- Pin prompts and model/configuration versions before comparing later runs.
03 / Commercial boundaries
Pricing & limits
Terms below reflect the 2026-10-26 source review, not a new live quote. Confirm currency, billing term, tax and required scope with the provider.
- Operating costs
Software is free; model API, hosting, subscriptions, and optional provider passes can cost money.
Archived research · Official quick-start and cost documentation checked 2026-10-26Research excerpt · Pricing, model costs, and limits
- Delivery model
Free, MIT-licensed, self-hosted software with dashboard, CLI, JSON API, and MCP server.
Archived research · Official site and repository checked 2026-10-26Research excerpt · Overview
Additional costs & usage boundaries
- Operating costs
Software is free; model API, hosting, subscriptions, and optional provider passes can cost money.
Archived research · Official quick-start and cost documentation checked 2026-10-26Research excerpt · Pricing, model costs, and limits
Your buying checklist
Get these answers first.
- Which answerers and model IDs are current and available in the chosen setup?
- How are grader decisions calibrated against human adjudication?
- What are the retention, backup, authentication, and provider data-use boundaries?
- How do hosted/demo results differ from a self-hosted deployment?
Pricing context & qualifications
Cheap Seats is free, MIT-licensed software rather than a paid hosted plan. Official materials checked 2026-10-26 state that API calls, hosting, subscriptions, and optional provider passes can add cost; budget estimates are not billing quotes.
04 / The tradeoffs
Strengths & weaknesses
Reasons to shortlist
- Free MIT-licensed software with inspectable implementation.
- Raw answers, cited URLs, grades, themes, and fix briefs remain available to the operator.
- Dashboard, CLI, JSON API, MCP, Docker, and local subscription paths are documented.
- Explicit statistical and evidence cautions reduce simplistic score interpretation.
Reasons to pause
- Model access, hosting, security, retention, and provider costs are operator responsibilities.
- Checked sources describe Claude and OpenAI answerers; broader engine coverage is not established.
- No independent rating sample, controlled benchmark, or external efficacy study was found.
- Grader calibration, citation accuracy, causal lift, traffic, and revenue remain unverified.
No checked source proves grader accuracy, citation precision, repeatability, causal visibility lift, traffic, or revenue outcomes.
05 / Choose by the job
Alternatives to consider
Editorial fit comparisons—not performance rankings or claims that one option always wins.
GEO Metrics ↗
A hosted dashboard and reporting workflow are more important than self-hosting.
Otterly.AI ↗
Managed SaaS onboarding and broader commercial coverage are priorities.
06 / What we can substantiate
Reviews & evidence
Public evidence is stronger for advertised scope and implementation than for customer satisfaction or efficacy.
AICiteKit assessment: public-source research, not a hands-on product test. Official materials establish stated scope; reviews are dated and may concern the broader platform. Neither establishes guaranteed citations, traffic or revenue.
Sources & original verification dates
| Source | Evidence type | Checked |
|---|---|---|
| Cheap Seats official homepage ↗ | Official product page | |
| Cheap Seats documentation ↗ | Official documentation | |
| Cheap Seats compare page ↗ | Official comparison/documentation | |
| Cheap Seats GitHub repository ↗ | Public vendor-controlled open-source implementation | |
| Cheap Seats public demo run ↗ | Vendor-controlled demonstration; not independent validation | |
| Awesome AI Search Visibility list ↗ | Independent category context; not product validation |
Original review evidence & date context
Preserved from the published research. Dates, review counts and product-scope qualifications below have not been refreshed by this layout migration.
Medium confidence for product identity, advertised scope, open-source implementation, and operating boundaries; low confidence for grader calibration, citation precision, repeatability, traffic, and revenue outcomes. The independent category list provides context only.
Research notes & unresolved discrepancies
- The Awesome AI Search Visibility list was used as independent category context, not product validation.
- The public demo and GitHub repository are vendor-controlled evidence despite the MIT license.
- No product-specific rating sample, hands-on external test, or controlled outcome study was found.
Layout migration: October 4, 2026. The original MDX research is retained in the repository; this reorganization does not refresh its factual verification dates.