ABSTRACT Here we present juliet, a versatile tool for the analysis of transits, radial velocities, or both. juliet is built over many available tools for the modelling of transits, radial velocities, and stochastic processes (here modelled as Gaussian Processes; GPs) in order to deliver a tool/wrapper which can be used for the analysis of transit photometry and radial-velocity measurements from multiple instruments at the same time, using nested sampling algorithms which allows it to not only perform a thorough sampling of the parameter space, but also to perform model comparison via Bayesian evidences. In addition, juliet allows us to fit transiting and non-transiting multiplanetary systems, and to fit GPs which might share hyperparameters between the photometry and radial velocities simultaneously (e.g. stellar rotation periods), which might be useful for disentangling stellar activity in radial-velocity measurements. Nested Sampling, Importance Nested Sampling, and Dynamic Nested Sampling is performed with publicly available codes which in turn give juliet multithreading options, allowing it to scale the computing time of complicated multidimensional problems. We make juliet publicly available via GitHub.