Phylogenetic placement is the problem of adding sequences to an existing phylogenetic tree. While many techniques have been developed for this problem, methods based on optimizing maximum likelihood, such as pplacer and EPA-ng, have been shown to provide the highest accuracy. Unfortunately, these methods are limited to at most moderately large placement trees due to their design. SCAMPP and Batch-SCAMPP are two methods that have been developed to improve the scalability of both pplacer and EPA-ng to very large trees, while maintaining high accuracy. Here, we describe these methods and show how to use them in two applications: metagenomics, including taxon identification and abundance profiling, and incrementally growing large trees. SCAMPP and Batch-SCAMPP are available in open-source form on GitHub and PyPI.