In recent years, geometric machine learning models, such as graph neural networks, have achieved significant success in chemical and materials science research, particularly in high-throughput virtual screening and atomic simulations. These models excel because they can effectively learn the underlying representations of atomic structures directly from training data. However, this strength also highlights a weakness: their heavy reliance on data, which limits their applicability in scenarios where data is sparse. Data scarcity is a common challenge in these fields, and addressing it has spurred the development of pre-trained machine learning models. These models have learned fundamental geometric relationships present in atomic data and can be fine-tuned for smaller, application-specific datasets. Pre-trained models that have been trained on large and diverse atomic datasets have shown impressive generalization and flexibility in downstream tasks, earning them the designation of "atomistic foundation models." To unlock the full potential of these atomistic foundation models, we introduce MatterTune, an integrated and user-friendly platform designed to facilitate sophisticated fine-tuning. MatterTune offers a modular and scalable framework that seamlessly incorporates atomistic foundation models into materials informatics and simulation workflows. This integration reduces the barriers to entry and promotes a wide range of applications in the field of materials science. Currently, MatterTune supports several state-of-the-art foundation models, including ORB, MatterSim, JMP, and EquformerV2. The platform is equipped with a comprehensive array of features, such as modular and flexible design, distributed and customizable fine-tuning capabilities, and extensive support for downstream informatics tasks. These features collectively enhance the usability and effectiveness of atomistic foundation models, making them more accessible to researchers and practitioners in materials science. MatterTune's modular design allows users to customize the fine-tuning process according to their specific needs, whether for materials discovery or detailed simulation studies. The platform's distributed fine-tuning capability means it can efficiently handle large datasets and complex computational tasks, ensuring scalability and performance. Furthermore, MatterTune provides a user-friendly interface that simplifies the integration of fine-tuned models into existing workflows, making advanced materials science research more approachable and inclusive. By lowering the technical requirements and enhancing the adaptability of atomistic foundation models, MatterTune aims to democratize access to powerful computational tools. This advancement is expected to accelerate materials discovery and improve the accuracy of simulations, ultimately contributing to breakthroughs in areas such as renewable energy, drug development, and advanced materials engineering. MatterTune represents a significant step forward in the field, bringing the benefits of pre-trained models to a broader audience and fostering innovation in materials science.