Internet of Things (IoT) is playing increasingly more fundamental role in wide range of sectors, including industry, agriculture, health care, and other services. In many cases, cloud computing serves as an elastic and efficient paradigm for implementing IoT back-ends. With the emerging lightweight software container technologies, the feasible approaches and design options for such IoT back-ends have been significantly enriched. In our paper we present the evolution of an IoT back-end, which is responsible for collecting (among others) meteorological, image and soil data from cultivated fields in order to enable precision farming. The different versions, namely the cloud VM-based and the Docker containerized variants, provide highly scalable and vendor independent (cloud provider agnostic) solutions, therefore they can form a robust and adaptive framework for further pilot applications areas, e.g. Connected Cars and Industry 4.0, as the presented benchmarks illustrate the throughput and other parameters of the current implementation in the paper.