This paper introduces RouteFinder, a framework for developing foundation models for Vehicle Routing Problems (VRPs). Our key idea is that a foundation model for VRPs should be able to model variants by treating each variant as a subset of a larger VRP problem, equipped with different attributes. We introduce a parallelized environment to handle any combination of attributes simultaneously in a batched manner and an efficient sampling procedure to train on a mix of problems at each optimization step, greatly improving convergence robustness. We also introduce novel Global Feature Embeddings that project instance-wise attributes efficiently onto the latent space and help the model understand different VRP variants. Finally, we introduce Efficient Adapter Layers, a simple-yet-effective technique to finetune pretrained RouteFinder models to solve novel variants with previously unseen attributes, outside of the original feature space. We validate our approach through extensive experiments on 24 VRP variants, demonstrating competitive results over recent multi-task learning models. We make our code openly available at https://github.com/ai4co/routefinder.