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| Criterion |  Architecture C4 Model |  Product Discovery Fake Door Test |  Growth A/B Testing |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
Purposedifferent | The C4 Model makes a system legible across several resolution levels, from context down to code. It suits situations where different audiences need to understand the same architecture from different altitudes. | When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | Low | Medium | High | Medium |
Timedifferent | 1-4 h | 1-5 Tage | 1-4 Wochen | 45-75 min |
Participantsdifferent | 1-5 | Nutzertraffic | 1-6 | 2-8 |
Formatdifferent | Workshop + async | Async | Async | Workshop |
Outputdifferent | Context Diagram, Container Diagram, Component Diagram | Click Data, Interest Signal, Learning Decision | Experiment results, Decision log, Learning summary | Test Matrix, Test Plan |
Tagsno overlap | ArchitectureCommunicationVisualization | ValidationExperimentsDemandDiscovery | ExperimentsGrowthAnalyticsValidation | ExperimentsValidationDiscoveryOptions |