Weights & Biases is an AI developer platform for building AI agents, applications, and models with confidence. The platform combines model development, post-training, inference, and GenAI application evaluation so teams can manage experiments, traces, datasets, prompts, code, and model assets in one workflow.
Weights & Biases is designed for organizations that need reproducibility, observability, and collaboration across the AI lifecycle. Its portfolio centers on Weave for tracing, evaluation, and monitoring of LLM and agentic applications, and Models for training, fine-tuning, and model management, with deployment options spanning managed SaaS, dedicated cloud, and customer-managed environments.
Offerings, Capabilities, and Integrations
Weights & Biases supports AI workflows across experimentation, fine-tuning, post-training, hosted inference, evaluation, monitoring, artifact management, reporting, and automation. Its platform is built to help teams move from exploratory work to governed, repeatable production processes without losing visibility into model behavior or development history.
Weights & Biases emphasizes interoperability with established ML and GenAI tooling. It integrates with widely used frameworks and libraries such as TensorFlow, Keras, PyTorch Lightning, XGBoost, Hugging Face, and Diffusers; connects with services such as Amazon SageMaker, Databricks, Azure OpenAI fine-tuning, and Vertex AI; and supports orchestration tools including Kubeflow Pipelines, Metaflow, Dagster, and Hydra. For LLM applications, it also supports tracing across major model providers and agent frameworks with minimal manual instrumentation.
Products and Services
- Weave: Observability and evaluation platform for LLM and agentic applications, with tracing, scorers, judges, prompt and model comparison, and production monitoring.
- Models: Suite for managing AI model development, including training and fine-tuning workflows, reporting, sweeps, and registry-backed versioning.
- Experiments: Experiment tracking for metrics, hyperparameters, system performance, and model artifacts, with dashboards and APIs for comparing runs.
- Sweeps: Hyperparameter search and optimization capability that coordinates search strategies and helps teams launch, manage, and analyze tuning jobs.
- Tables: Interactive tabular analysis environment for datasets and predictions, with filtering, grouping, joins, rich media, and side-by-side comparisons.
- Reports: Interactive reports and workspaces for documenting findings, combining narrative text with plots, panels, images, and shared views.
- Registry: Central registry for publishing, organizing, and governing versioned artifacts such as datasets, models, prompts, code, and related metadata.
- Artifacts: Artifact versioning and lineage tracking for datasets, models, and other pipeline assets across training, evaluation, and deployment workflows.
- Automations: Workflow automation capability for triggering downstream actions and operational processes around AI development activities.
- Serverless RL: Managed serverless reinforcement learning for post-training LLMs on agentic tasks, using autoscaling GPU infrastructure and integrations with ART and RULER.
- Serverless SFT: Managed supervised fine-tuning service for LLMs that trains LoRA adapters on curated datasets and can automatically host resulting checkpoints.
- Serverless Inference: Hosted access to open-source foundation models through an OpenAI-compatible API, with usage tracking and Weave integration for tracing and evaluation.
Target Customers
Weights & Biases serves AI practitioners ranging from individual developers and early-stage startups to large enterprise AI teams. Core users include ML engineers, data scientists, LLM engineers, researchers, and platform teams that need shared visibility into experiments, evaluations, lineage, and application performance.
The platform is especially relevant for organizations training or fine-tuning models, building agentic and LLM-powered applications, or operating under stronger security and governance requirements. Its customer footprint spans foundation model builders, healthcare and life sciences organizations, automotive and autonomous systems teams, financial services use cases, and other enterprises running production-scale AI programs.
Cloud Integrations and Marketplace
- AWS Marketplace: Weights & Biases AI Development Platform for AWS is available through AWS Marketplace and supports AWS-centric AI workflows, including integration with Amazon SageMaker.
- Azure Marketplace: Weights & Biases for Microsoft Azure is available through Microsoft Commercial Marketplace and integrates with Azure OpenAI fine-tuning and broader Azure AI workflows.
- Google Cloud Marketplace: Weights & Biases maintains Google Cloud Marketplace presence and pairs it with native Google Cloud integrations, particularly Vertex AI and related cloud infrastructure for model development and GenAI application workflows.
Key People
- Lukas Biewald: General Manager and Cofounder
- Shawn Lewis: Distinguished Engineer and Cofounder
- Chris Van Pelt: Distinguished Engineer and Cofounder
- Mike Saparov: VP of Engineering
- Phil Gurbacki: VP of Product
- Robin Bordoli: VP of Revenue
- Seann Gardiner: VP of Business Development
- Adrian Swanberg: Head of Weave
- Lavanya Shukla: Director of AI
Key Facts
- Headquarters: San Francisco, California, United States
- Employees: Approximately 300
- Annual Revenue: Undisclosed
- Parent Company: CoreWeave, Inc.
- Subsidiaries: None
- Publicly Listed: No; subsidiary of CoreWeave, Inc. (Nasdaq: CRWV)
Analyst Recognitions
- Everest Group: Everest Group AI Top 50 2023: Ranked #46 among AI-first technology providers.
