Tiny Machine Learning (TinyML) allows the execution of small machine learning models on low-power devices like microcontrollers. TinyML-as-a-Service (TinyMLaaS) is an architecture to make the usage of TinyML models easier by having a platform that optimizes and compiles machine learning models according to the constraints of target devices, and then deploys the model code on microcontrollers. Within the Cloud-to-IoT continuum, both TinyML and multi-tenant microcontrollers focus on empowering microcontrollers and enabling on-device computing. Multi-tenant microcontrollers are designed to securely execute codes from mu- tually distrusting actors through the usage of lightweight software containerization solutions, like WebAssembly. In this paper, we propose to integrate TinyMLaaS with multi-tenant microcontrollers by using WebAssembly-based containerization, and we implement a proof-of-concept of the TinyMLaaS architecture based on WebAssembly Micro Runtime (WAMR) and RIOT-ML. In the second part of the paper, to improve the usage of containerized TinyML on microcontrollers, we propose CS4WAMR, a framework to enhance WAMR usage by enabling running simultaneously multiple instances of WAMR to allow better permission and memory consumption control.