The Free Energy Principle, which underlies Active Inference (AI), is a way to explain human perception and behaviour. Previous literature has hinted at a relation between AI and Linear-Quadratic Gaussian (LQG) control, the latter being a textbook controller. AI and LQG are, however, defined with different settings in mind: LQG has access to inputs, whereas AI estimates these; LQG is optimal for White Gaussian Noise, whereas noise needs to be coloured for AI, in order to make derivatives. Therefore, a comparison is provided on two bases: the setting of LQG control; and the setting of AI. The optimal LQG controller is obtained for both settings, and AI is applied to both settings as well. When AI is reduced to the setting of LQG, an equivalent expression can be obtained by a proper choice of tuning parameters. This entails choosing a matrix such that the closed-loop is stable, which contrasts LQG control, which is always a stabilizing controller. When LQG is extended closer to the normal AI setting, we find that tuning of AI becomes harder for more complex systems, but that AI is mostly able to show optimal behaviour.