Weights & Biases

Weights & Biases (W&B) is an MLOps platform that has expanded from experiment tracking for traditional ML into LLM evaluation, prompt engineering, and agent observability. Its core strength remains experiment tracking — W&B's ability to log, compare, and visualize thousands of experiments is unmatched — and the LLM-specific features build on this foundation.

W&B Weave is the LLM-focused product layer. It provides tracing for LLM applications with automatic capture of inputs, outputs, token counts, and latency. Unlike LLM-native tools, Weave inherits W&B's experiment tracking DNA: you can version prompts, log evaluation metrics, and compare different model configurations using the same dashboarding system that ML engineers already know for training runs.

The evaluation framework in Weave is particularly strong. You define evaluation datasets, create scorer functions (including LLM-as-judge), and run structured evaluations that automatically log results as W&B experiments. This means you get parallel coordinate plots, metric distributions, and comparison tables across evaluation runs — capabilities that LLM-specific tools are still catching up to.

W&B Tables enable collaborative data exploration. Teams can log structured data (including LLM outputs, evaluation scores, metadata) and explore it interactively with filtering, sorting, and custom visualizations. This is powerful for reviewing evaluation results or analyzing production traces as a team.

The integration story is broad but sometimes shallow. W&B has integrations for LangChain, LlamaIndex, OpenAI, Hugging Face, and dozens more, but the depth varies. The Hugging Face and PyTorch integrations are excellent (reflecting W&B's ML heritage). The LLM framework integrations are newer and sometimes lag behind purpose-built tools.

The honest tradeoff: W&B is the best choice if your team already uses it for ML experiment tracking and wants a unified platform for both traditional ML and LLM work. The LLM features benefit enormously from the existing experiment management infrastructure. However, if you're purely building LLM applications without traditional ML workflows, dedicated LLM observability tools like Langfuse or Braintrust offer more focused, streamlined experiences. W&B's breadth means the LLM-specific features can feel like they're bolted onto an ML platform rather than being the primary focus.