zfit is a scalable, pythonic model fitting library that aims at implementing likelihood fits in HEP. So far, the main functionality was focused on unbinned fits. With zfit 0.10, the concept of binning is introduced and allows for binned datasets, PDFs and losses such as Chi2 or Binned likelihoods. All of these elements are interchangeable and convertable to unbinned counterparts and allow for an arbitrary mixture of both. In this talk, we will introduce the binned part of zfit and its integration into the existing Scikit-HEP ecosystem.