Pathology images provide a unique challenge for computer-vision-based analysis: a single whole slide image is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology foundation model (FM) that is pre-trained on a diverse dataset of 195 million image tiles collected from multiple sites. We design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks ranging from subcellular- to slide-scale, including instance segmentation, tile classification, and slide-level prediction. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific FMs, some of which use orders-of-magnitude larger datasets and model sizes. Our findings present a path towards a universal embedding to power pathology image analysis, and motivate further exploration around pathology FMs in terms of data diversity, architectural improvements, sample efficiency, and practical deployability in real-world applications.