Weights & Biases provides a developer-first MLOps platform that enables machine learning teams to track, visualize, and optimize their experiments and models through tools like hyperparameter sweeps and automated workflows. The platform addresses the challenges of managing ML pipelines and data, facilitating collaboration and improving model performance across AI applications.
Funding
$265M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Machine learning and generative AI projects generate large numbers of experiments, model checkpoints, and data artifacts that are difficult to track, reproduce, and share across teams. Without a unified system, developers spend excessive time managing code, hyperparameters, and infrastructure, leading to slower iteration and higher risk of errors.
Solution
Weights & Biases provides an AI developer platform that centralizes experiment tracking, hyperparameter optimization, model versioning, and monitoring for both training and inference workflows. The platform offers SDKs for popular frameworks (PyTorch, TensorFlow, JAX, Scikit‑Learn, XGBoost, etc.) that log metrics, system usage, and artifacts with minimal code changes. Integrated tools such as Sweeps automate hyperparameter searches, while the Model Registry stores and serves versioned checkpoints. Weave extends the platform to trace and evaluate LLM and agentic applications, enabling end‑to‑end observability of prompts, tool calls, and responses. All data are stored securely in a cloud service that supports compliance standards (ISO 27001, SOC 2, HIPAA, GDPR) and can be deployed in multi‑tenant or dedicated environments.
Target Audience
Primary users are data scientists, ML engineers, and AI product teams building and deploying models at scale, including enterprises that require audit‑ready tracking and compliance.
Features
- SDKs for experiment tracking that log metrics, configs, and system resources with a few lines of code
- Hyperparameter sweep engine that orchestrates distributed searches and visualizes results
- Model Registry for versioned storage, promotion, and deployment of checkpoints
- Weave framework for tracing LLM calls, prompt pipelines, and agent steps in generative AI apps
- Real‑time monitoring of training and inference resources (CPU/GPU usage, memory, latency)
- Collaborative reports and dashboards for sharing insights and reproducible documentation
- Compliance‑ready hosting with ISO 27001, SOC 2, HIPAA, GDPR, and options for dedicated clouds
- Mobile app for on‑the‑go experiment monitoring and alerting