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| Criterion |  Architecture C4 Model |  Product Discovery Smoke Test |  Growth Growth Experiment |  Growth A/B Testing |
|---|---|---|---|---|
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 only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | When growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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. |
Complexitydifferent | Low | Low | Medium | High |
Timedifferent | 1-4 h | 1-5 Tage | 1-2 Wochen | 1-4 Wochen |
Participantsdifferent | 1-5 | Nutzertraffic | 1-6 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Context Diagram, Container Diagram, Component Diagram | Interest Metrics, Conversion Signal, Learning Note | Experiment card, Result summary, Next bet | Experiment results, Decision log, Learning summary |
Tagsno overlap | ArchitectureCommunicationVisualization | ValidationExperimentsDemandGrowth | MarketingGrowthExperimentsLearning | ExperimentsGrowthAnalyticsValidation |