1. Speed: H2O uses distributed computing and parallel processing techniques to perform complex model training and prediction tasks efficiently. On the other hand, XGBoost focuses on boosting tree algorithms and is known for its high-speed gradient boosting framework.

2. Algorithm Support: H2O provides a wide range of machine learning algorithms, including deep learning, generalized linear models, random forests, and gradient boosting machines. XGBoost, as its name suggests, primarily focuses on gradient boosting algorithms, making it suitable for tasks that require boosting-based ensemble models.

3. Scalability: H2O is designed to scale horizontally and is capable of handling large datasets with ease. It can also be deployed on cloud platforms and can leverage distributed computing resources for improved performance. XGBoost, on the other hand, is mainly optimized for single machine use and may face challenges when dealing with massive datasets.

4. Ease of Use: H2O provides a user-friendly interface with visualizations and an intuitive flow for building machine learning models. It also offers auto-tuning capabilities to automatically find optimal model configurations. XGBoost, although powerful, requires more manual tuning and hyperparameter optimization to achieve optimal results.

5. Interpretability: H2O offers built-in options for model interpretability, such as feature importance ranking and partial dependence plots. This allows users to gain insights into the model's decision-making process. XGBoost, while providing variable importances, does not offer the same level of interpretability as H2O.

6. Community Support: Both H2O and XGBoost have active communities, providing resources, forums, and documentation. However, XGBoost has gained substantial popularity and has a larger community base, resulting in a vast range of support materials and a more extensive community-driven development ecosystem.

In Summary, H2O and XGBoost differ in terms of speed, algorithm support, scalability, ease of use, interpretability, and community support. These differences make H2O preferable for certain use cases, such as those requiring distributed computing or a wider range of algorithms, while XGBoost excels in terms of speed and is popular for boosting tree ensemble models.