Acromegaly is a chronic disorder usually diagnosed late in the disease evolution, leading to substantial morbidity and mortality related to this long period of undiagnosed state as well as the difficulty in achieving normalization of GH hypersecretion and controlling tumor mass. First generation somatostatin analogues (SSA) are accepted as the first-line medical therapy or as second-line therapy in patients undergoing unsuccessful surgery. However, because a high percentage of patients experience SSA treatment failure, the inclusion of biomarkers associated with a successful or non-successful response to these drug (as well as to all classes of medical therapy) is necessary to better guide the choice of treatment, potentially allowing for a quicker achievement of disease control. The current treatment algorithms for acromegaly are based upon a "trial and error" approach with additional treatment options provided when disease is not controlled. In many other diseases, their therapeutic algorithms have been evolving towards personalizing treatment with medication that best matches individual disease characteristics, using biomarkers that identify therapeutic response, thus allowing the personalization of the therapy. It is time to introduce this approach to acromegaly treatment algorithms. This paper reviews the potential tools for doing so.