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As a decentralized training approach, federated learning enables multiple organizations to jointly train a model without exposing their private data. This work investigates vertical federated learning (VFL) to address scenarios where collaborating organizations have the same set of users but with different features, and only one party holds the labels. While VFL shows good performance, practitioners often face uncertainty when preparing non-transparent, internal/external features and samples for the VFL training phase. Moreover, to balance the prediction accuracy and the resource consumption of model inference, practitioners require to know which subset of prediction instances is genuinely needed to invoke the VFL model for inference. To this end, we co-design the VFL modeling process by proposing an interactive real-time visualization system, VFLens, to help practitioners with feature engineering, sample selection, and inference. A usage scenario, a quantitative experiment, and expert feedback suggest that VFLens helps practitioners boost VFL efficiency at a lower cost with sufficient confidence.