In vitro fertilization (IVF) has become a standard treatment for subfertility after it was demonstrated to be of value to humans in 1978. However, the introduction of IVF into mainstream clinical practice has been accompanied by concerns regarding the number of multiple gestations that it can produce, as multiple births present significant medical consequences to mothers and offspring. When considering IVF as a treatment modality, a balance must be set between the chance of having a live birth and the risk of having a multiple birth. As IVF is often a costly decision for patients—financially, medically, and emotionally—there is benefit from estimating a patient’s specific chance that IVF could result in a birth as fertility treatment options are contemplated. Historically, a patient’s “chance of success” with IVF has been approximated from institution-based statistics, rather than on the basis of any particular clinical parameter (except age). Furthermore, the likelihood of IVF resulting in a twin or triplet outcome must be acknowledged for each patient, given the known increased complications of multiple gestation and consequent increased risk of poor birth outcomes. In this research, we describe a multivariate risk assessment model that incorporates metrics adapted from a national 7.5-year sampling of the Human Fertilisation & Embryology Authority (HFEA) dataset (1991–1998) to predict reproductive outcome (including estimation of multiple birth) after IVF. To our knowledge, http://www.formyodds.com is the first Software-as-a-Service (SaaS) application to predict IVF outcome. The approach also includes a confirmation functionality, where clinicians can agree or disagree with the computer-generated outcome predictions. It is anticipated that the emergence of predictive tools will augment the reproductive endocrinology consultation, improve the medical informed consent process by tailoring the outcome assessment to each patient, and reduce the potential for adverse outcomes with IVF.