# Weights & Biases

Weights & Biases (W&B) is a San Francisco-based company providing an MLOps platform for experiment tracking, dataset versioning, and model evaluation, widely used in machine learning and AI development.

Weights & Biases (W&B) is a software company that develops a platform for machine learning experiment tracking, dataset versioning, and model evaluation. Founded in 2017, the company provides tools that help researchers and engineers log, visualize, and compare machine learning experiments, including metrics, hyperparameters, and outputs. Its platform integrates with popular frameworks such as [TensorFlow](https://www.wikiprompt.org/wiki/machine-learning), [PyTorch](https://www.wikiprompt.org/wiki/deep-learning), and [Keras](https://www.wikiprompt.org/wiki/neural-network), and supports workflows for [large language models](https://www.wikiprompt.org/wiki/large-language-model) and [generative AI](https://www.wikiprompt.org/wiki/generative-ai) applications.

The company was co-founded by Lukas Biewald, Chris Van Pelt, and Shawn Lewis, who previously worked on CrowdFlower, a data labeling platform. W&B emerged from the need to streamline the iterative process of training models, offering a centralized dashboard for tracking runs, comparing results, and sharing findings across teams. As of 2024, the platform is used by over 1,000 organizations, including [OpenAI](https://www.wikiprompt.org/wiki/openai), [Anthropic](https://www.wikiprompt.org/wiki/anthropic), and [Google DeepMind](https://www.wikiprompt.org/wiki/google-deepmind), and has raised more than $250 million in funding, with a valuation exceeding $1 billion.

## Core Features

W&B provides a suite of tools centered on experiment tracking. Users can log metrics such as loss and accuracy in real time, visualize them in interactive charts, and compare multiple runs side by side. The platform also supports hyperparameter sweeps, enabling automated search over parameter spaces, and artifact management for versioning datasets and models. For production workflows, W&B offers model registry and evaluation tools, allowing teams to track model versions, monitor performance, and facilitate collaboration through shared reports and comments.

## Integration and Ecosystem

The platform integrates with major [AI](https://www.wikiprompt.org/wiki/artificial-intelligence) frameworks and cloud services. It works with [Amazon Web Services](https://www.wikiprompt.org/wiki/amazon-web-services), [Microsoft Azure](https://www.wikiprompt.org/wiki/azure), and [Google Cloud](https://www.wikiprompt.org/wiki/google-cloud), and supports hardware accelerators from [AMD](https://www.wikiprompt.org/wiki/amd), [Intel](https://www.wikiprompt.org/wiki/intel), and [NVIDIA](https://www.wikiprompt.org/wiki/nvidia) (via third-party libraries). W&B also provides SDKs for Python and R, and offers integrations with [Hugging Face](https://www.wikiprompt.org/wiki/hugging-face) and [MLflow](https://www.wikiprompt.org/wiki/mlflow) for model management. Its open-source library, wandb, is widely adopted, with over 500,000 downloads per month as of 2024.

## Applications in AI Research

W&B is extensively used in academic and industrial research. It has been cited in thousands of papers, including work on [transformers](https://www.wikiprompt.org/wiki/transformer) and [deep learning](https://www.wikiprompt.org/wiki/neural-network) architectures. Teams at [Stanford AI Lab](https://www.wikiprompt.org/wiki/stanford-ai-lab), [MIT CSAIL](https://www.wikiprompt.org/wiki/mit-csail), and [Berkeley AI Research](https://www.wikiprompt.org/wiki/berkeley-ai-research) use the platform for reproducibility and collaboration. In industry, companies like [Tesla](https://www.wikiprompt.org/wiki/tesla-autopilot) and [Waymo](https://www.wikiprompt.org/wiki/waymo) leverage W&B for training autonomous driving models, while [OpenAI](https://www.wikiprompt.org/wiki/openai) and [Anthropic](https://www.wikiprompt.org/wiki/anthropic) use it for [LLM](https://www.wikiprompt.org/wiki/large-language-model) development and evaluation.

## Business and Growth

Headquartered in San Francisco, W&B has expanded globally with offices in London and Seoul. The company raised a Series C round of $135 million in 2021, led by [Sequoia Capital](https://www.wikiprompt.org/wiki/sequoia-capital) and [Tiger Global](https://www.wikiprompt.org/wiki/tiger-global), bringing total funding to over $250 million. In 2023, W&B acquired the open-source project Launchpad to enhance its deployment capabilities. The company has grown to over 300 employees and continues to add features for [generative AI](https://www.wikiprompt.org/wiki/generative-ai) monitoring, including tools for tracking [LLM](https://www.wikiprompt.org/wiki/large-language-model) prompts and responses.

## Community and Open Source

W&B maintains a strong open-source presence. The wandb library is available under the Apache 2.0 license, and the company sponsors community events and educational initiatives. It offers free tiers for academic researchers and students, and hosts an annual conference, W&B Summit, which features talks from leading AI practitioners. The platform's documentation and tutorials are widely used in online courses, including those from Coursera and fast.ai.

## Future Directions

As [machine learning](https://www.wikiprompt.org/wiki/machine-learning) evolves, W&B is focusing on tools for [LLM](https://www.wikiprompt.org/wiki/large-language-model) evaluation, [reinforcement learning](https://www.wikiprompt.org/wiki/reinforcement-learning) from human feedback, and [MLOps](https://www.wikiprompt.org/wiki/mlops) automation. The company aims to become the standard infrastructure for AI development, similar to how GitHub serves software engineering. With the rise of [generative AI](https://www.wikiprompt.org/wiki/generative-ai), W&B is expanding its capabilities to handle massive-scale training runs and real-time monitoring, positioning itself at the center of the AI infrastructure stack.

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Source: https://www.wikiprompt.org/wiki/weights-biases
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-05T14:06:43.980845+00:00