Master Weights & Biases with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for free W&B account at wandb.ai and install the Python SDK: pip install wandb Import wandb in your code and login with wandb.login() to authenticate your session For LLM work, initialize a Weave project and start tracing with weave.init() in your application Log experiments using wandb.log() for metrics and wandb.Table() for structured data Create evaluation datasets and use Weave's evaluation framework to score model outputs
💡 Quick Start: Follow these 1 steps in order to get up and running with Weights & Biases quickly.
Weave is a product layer within W&B focused on LLM application development. It uses the same W&B account, workspace, and infrastructure. Think of it as the LLM-specific interface built on top of W&B's core experiment tracking capabilities.
W&B is broader (covering traditional ML + LLM) while Langfuse and Braintrust are deeper on LLM-specific features. W&B excels at experiment comparison and team reporting. If you only do LLM work, dedicated tools are more streamlined. If you do both ML and LLM, W&B unifies everything.
Yes, through Weave's tracing and W&B's monitoring features. However, W&B's roots are in offline experiment tracking, so real-time production alerting is less mature than dedicated monitoring tools. Many teams use W&B for evaluation and a separate tool for production monitoring.
The free tier supports small teams with limited storage and compute. The Team plan starts around $50/user/month. For 10 engineers, expect $500-1,000/month depending on usage. Enterprise pricing is custom and includes SSO, audit logs, and dedicated support.
Now that you know how to use Weights & Biases, it's time to put this knowledge into practice.
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Tutorial updated March 2026