All major cloud providers offer a dazzling array of machine-learning (ML) solutions. These powerful tools can provide a lot of value, but come at a cost. There is the pure run cost for these services charged by the cloud provider. In addition, there is a kind of operations tax. These complex tools need to be understood and operated, and with each new tool added to the architecture this tax burden increases. In our experience, teams often choose complex tools because they underestimate the power of simpler tools such as linear regression. Many ML problems don't require a GPU or neural networks. For that reason we advocate for the simplest possible ML, using simple tools and models and a few hundred lines of Python on the compute platform you have at hand. Only reach for the complex tools when you can demonstrate the need for them.