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Cloud computing relies on a deep stack of system layers: virtual machine, operating system, distributed middleware and language runtime. However, those numerous, distributed, virtual layers prevent any low-level understanding of the properties of FaaS applications, considered as programs running on real hardware. As a result, most research analyses only consider coarse-grained properties such as global performance of an application, and existing datasets include only sparse data. FaaSLoad is a tool to gather fine-grained data about performance and resource usage of the programs that run on Function-as-a-Service cloud platforms. It considers individual instances of functions to collect hardware and operating-system performance information, by monitoring them while injecting a workload. FaaSLoad helps building a dataset of function executions to train machine learning models, studying at fine grain the behavior of function runtimes, and replaying real workload traces for in situ observations. This research software project aims at being useful to cloud system researchers with features such as guaranteeing reproducibility and correctness, and keeping up with realistic FaaS workloads. Our evaluations show that FaaSLoad helps us understanding the properties of FaaS applications, and studying the latter under real conditions.