The growing deployment of Machine Learning models has increased the demand for interpretability, leading to the development of various explainability methods. Yet, each method has its strengths and limitations, making it challenging to identify a one-size-fits-all solution. This paper introduces FIRE360, a multi-faceted and fast local explanation approach for tabular data. FIRE360 incorporates various explanation types (feature importances, rules, counterfactuals, exemplars, and counter-exemplars), offering a 360-degree model interpretability. FIRE360 also includes a dashboard with indicators for prediction reliability and explanation quality, such as fidelity and robustness. To ensure efficiency, FIRE360 uses GAN-generated synthetic data to approximate the original dataset and selects similar records to the target instance, avoiding costly on-demand neighborhood generation. Multiple local surrogate models are trained to capture different aspects of the data and black-box behavior. FIRE360 achieves higher fidelity than state-of-the-art methods, with improvements up to 95% on multiclass datasets.