Relationships between systems costs and model performance would ideally inform machine learning pipelines during design; yet, most existing network traffic representation decisions are made a priori, without concern for future use by models. To enable this exploration, we have created , a system designed to offer flexibly extensible network data representations, the ability to assess the systems-related costs of these representations, and the effects of different representations on model performance.