> ## Documentation Index > Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt > Use this file to discover all available pages before exploring further. # How is W&B different from TensorBoard? If you're evaluating experiment tracking tools or migrating from TensorBoard, this page summarizes the key differences to help you decide whether W\&B fits your workflow. W\&B integrates with TensorBoard and extends what experiment tracking tools can do. The founders created W\&B to address common frustrations that TensorBoard users face. Key improvements include: * **Model reproducibility**: W\&B supports experimentation, exploration, and model reproduction. It captures metrics, hyperparameters, and code versions, and it saves model checkpoints to ensure reproducibility. * **Automatic organization**: W\&B simplifies project handoffs and vacations by providing an overview of all attempted models, which saves time by preventing you from re-running old experiments. * **Quick integration**: Integrate W\&B into your project in five minutes. Install the free open source Python package and add a few lines of code. Logged metrics and records appear with each model run. * **Centralized dashboard**: Access a consistent dashboard regardless of where training occurs (locally, on lab clusters, or cloud spot instances). You don't need to manage TensorBoard files across different machines. * **Filtering table**: Search, filter, sort, and group results from your models. Identify the best-performing models for different tasks, an area where TensorBoard often struggles with larger projects. * **Collaboration tools**: W\&B supports collaboration on complex machine learning projects. Share project links and use private teams to share results. Create reports with interactive visualizations and Markdown descriptions for work logs or presentations. *** [TensorBoard](/support/models/tags/tensorboard)