Weights & Biases (W&B) is the “System of Record” for the artificial intelligence industry—the digital notebook where the world’s most important models are logged, tracked, and debugged. While 2024 was defined by its ubiquity among researchers (used by OpenAI, NVIDIA, and Meta to train their frontier models), 2025 marked a historic shift in its trajectory: in May 2025, W&B was acquired by CoreWeave for approximately $1.7 Billion. This strategic merger effectively combined the world’s premier AI software layer with the world’s fastest-growing compute cloud, creating a vertically integrated giant that rivals the hyperscalers.
Despite the acquisition, W&B continues to operate as an agnostic platform, maintaining its “Switzerland” status by supporting any cloud (AWS, GCP, Azure) and any framework (PyTorch, TensorFlow, JAX). By 2026, its product focus has expanded heavily into the Application Layer with W&B Weave. While the original platform was built for training models (tracking loss curves and gradients), Weave is built for running them—providing a lightweight, code-first toolkit that allows developers to trace the complex, multi-step reasoning of autonomous agents, debug hallucinations, and evaluate performance in production.
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Core Technology: The System of Record & Weave
W&B Experiments: The industry-standard dashboard for visualizing model training
It tracks every hyperparameter, code version, and dataset used to create a model, ensuring that scientific breakthroughs are reproducible.
W&B Weave: A specialized toolkit for the “Agentic” era
It logs the inputs and outputs of LLM calls, visualizing them as a “Trace Tree” so developers can see exactly where an agent failed (e.g., “Did it fail to search the web, or did it misinterpret the search result?”).
Model Registry: A central hub that acts as the “App Store” for an enterprise’s internal models
Managing versioning, lineage, and approval workflows before a model is deployed to production.
Launch: A job orchestration tool
That allows researchers to fire off training runs on massive compute clusters (like CoreWeave’s H100 pods) directly from their laptop.
Business & Market Status
Acquisition: Acquired by CoreWeave in May 2025 for ~$1.7 Billion, marking one of the most significant consolidations in the AI infrastructure space.
Market Share: Remains the dominant MLOps tool, used by over 30 foundation model builders and thousands of enterprises. It is effectively the standard for “Logging” in machine learning.
Integration: Post-acquisition, W&B has begun offering deeper optimizations for CoreWeave’s infrastructure, allowing users to visualize GPU health and cluster utilization alongside their model metrics.
Company Profile
Founders: Lukas Biewald (CEO), Chris Van Pelt (CPO), and Shawn Lewis (CTO).
Headquarters: San Francisco, California.
Funding: Raised over $250 Million prior to acquisition.
Key Investors (Pre-Exit): Coatue, Insight Partners, Felicis, BOND, NVIDIA.
Key Use Cases
- Foundation Model Training: Labs like OpenAI and Mistral use W&B to track the loss curves of massive training runs across thousands of GPUs, identifying instability before it wastes millions of dollars.
- Agent Debugging: Developers use Weave to trace the execution path of autonomous coding agents, pinpointing exactly which step in a 50-step chain caused a bug.
- Regulatory Audit: Healthcare and Finance companies use W&B as an immutable audit trail, proving to regulators exactly which data and code produced a specific AI decision.
Why It Matters
Weights & Biases solved the “Crisis of Reproducibility” in AI. Before W&B, building a model was like doing chemistry without a lab notebook—messy, forgetful, and impossible to scale. By standardizing how the industry records its work, they became the default operating system for machine learning. The CoreWeave acquisition cements this role, ensuring that the software which tracks the intelligence is tightly coupled with the hardware that powers it.
