Weights & Biases

Weights & Biases (W&B) is an AI developer platform built for ML engineers and researchers who need to track, compare, and reproduce machine learning experiments at scale. Founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis, the company grew into the category-defining experiment tracking tool and expanded into LLM observability with the 2024 launch of W&B Weave. In March 2025, CoreWeave, the AI-focused hyperscaler, acquired W&B for a reported $1.7B. The W&B brand and product line remain intact; Lukas Biewald serves as GM of W&B under CoreWeave, and the company continues to operate with a commitment to multi-cloud, infrastructure-agnostic interoperability.

The platform splits into two main products. W&B Models handles the traditional ML lifecycle: log metrics with a few lines of Python, visualize runs in an interactive dashboard, version model artifacts, run automated hyperparameter sweeps (Sweeps), share Reports with stakeholders, and register production-ready checkpoints in the Model Registry. W&B Weave covers the GenAI side: trace every LLM call, evaluate outputs with customizable scorers, monitor production agents in real time with Online Evaluations, and debug RAG pipelines by inspecting retrieval + generation at the function call level. Both products share one account, one API key, and one dashboard.

What W&B actually does in May 2026

W&B Models remains the core product for teams training neural networks. You call wandb.init() at the start of a training run and wandb.log() to push metrics. From there, the platform captures hardware utilization, loss curves, sample outputs, and arbitrary metadata automatically. Sweeps uses Bayesian optimization or grid search to run hyperparameter experiments across hundreds of configurations, with a built-in coordinator that assigns work to idle agents. The Model Registry lets teams promote specific artifact versions to "production" or "staging" state with lineage tracking back to the original run.

W&B Weave, launched April 18, 2024, extends this paradigm to LLM applications. The @weave.op decorator wraps any Python function, and Weave captures inputs, outputs, token counts, latency, and cost for every call. It auto-patches common LLM SDKs including OpenAI, Anthropic, Cohere, Groq, LangChain, and LlamaIndex so a single weave.init() call begins capturing traces without modifying call sites. Online Evaluations, added post-acquisition in mid-2025, score live incoming traces in production to detect quality regressions without waiting for manual review cycles. The platform also added W&B Inference in June 2025, providing a unified endpoint for open-source models (DeepSeek R1-0528, Llama 4 Scout, Phi 4 Mini) powered by CoreWeave's GPU infrastructure.

"W&B allows us to scale insights from one researcher to the entire team." -- Wojciech Zaremba, Cofounder, OpenAI (W&B customer page)

Where W&B sits versus Comet ML and MLflow

The three most common comparisons are W&B vs. Comet ML for hosted experiment tracking, and W&B vs. MLflow for open-source alternatives.

Comet ML is the closest structural analog. Both require code instrumentation, both log runs to a cloud dashboard, and both offer team collaboration. The mechanical differences matter for adoption decisions. Comet's Code Panels allow arbitrary Matplotlib or Plotly scripts to run inline in the experiment UI, enabling custom visualizations without leaving the dashboard. W&B's workspace uses a panel system that is polished but less scriptable at the experiment level. On performance, independent benchmarks from MLtraq show Comet and Neptune outperforming W&B for high-frequency metric logging at scale. W&B's upload pipeline can become a bottleneck in large parallel sweeps. Comet integrates with fewer deep learning frameworks natively; W&B has out-of-the-box integrations with fastai and Catalyst that Comet lacks. For LLM observability, W&B Weave has a significant depth advantage over Comet's current offering. On pricing, Comet uses per-seat pricing (approximately $39/user/month for Pro) while W&B's Pro tier charges $60/month for up to 10 model seats plus storage overages.

MLflow (Apache 2.0, governed by Databricks) is the go-to alternative for teams that need zero vendor dependency. MLflow's tracking server is architecturally lightweight: a REST or gRPC endpoint you deploy wherever you want, with no cloud account required. Its UI stores plots as static artifacts rather than interactive widgets, which is less visually capable but more portable. MLflow has no team collaboration features comparable to W&B Reports or the workspace sharing model. MLflow's 2024 "Tracing" feature for LLMs exists but is substantially less capable than W&B Weave for production use. The tradeoff is total control versus product polish: MLflow requires more setup and operational ownership, while W&B delivers a ready-to-use platform with active product development. For teams already on Databricks, MLflow integrates directly into that ecosystem, which is a lock-in advantage or disadvantage depending on your situation.

Teams running pure GenAI apps with no traditional ML training often compare W&B Weave against specialized LLM observability tools. Langfuse is open source, self-hostable, and free at any trace volume you can host yourself. Helicone focuses narrowly on LLM call logging and cost tracking with a simpler setup. W&B Weave's advantage is the unified platform: if your team already uses W&B for model training, adding Weave for the inference/LLM side costs nothing incremental and keeps all observability in one place.

What the daily workflow reality looks like

Setup is genuinely low-friction. pip install wandb, run wandb login, add three lines to your training script. For Weave, it is one additional line (import weave; weave.init("project-name")) and the patched LLM SDKs handle the rest. The dashboard loads run comparisons, lets you plot any logged metric against any other, and generates shareable Report documents that combine run tables with written analysis for stakeholders who do not have code access.

The friction emerges at scale. When a team runs hundreds of concurrent training jobs, the W&B upload thread competes with the training process for CPU and network. Users in r/MachineLearning and the W&B community forum have documented cases where the sync process blocks GPU training for hours in hyperparameter search scenarios, wasting expensive compute. The recommended fix is offline mode (WANDB_MODE=offline) followed by manual sync, but this breaks real-time dashboard visibility. The sync process itself can enter an infinite loop in edge cases, requiring manual process kill, which has been documented to terminate other running training jobs simultaneously.

"I want a lightweight tool that fits into my workflow, rather than a tool that imposes a new workflow on me. Weave achieves this by making it easy to decorate a few functions." -- Jonathan Whitaker, AI Researcher at Answer.AI (Weave launch announcement, April 2024)

The UI slows noticeably when projects accumulate large experiment counts. Auto-generated charts pile up and W&B lacks bulk deletion tools, leaving users with dashboards that take 10-20 seconds to load even for modest data volumes. This is a known issue with multiple open community threads and GitHub issues dating back to 2022 that remain only partially resolved.

Who W&B is built for

W&B is purpose-built for ML practitioners who run training experiments regularly. The sweet spot is a team of 3-20 ML engineers at a company building custom models, whether that means fine-tuning foundation models, training computer vision systems, or developing RL agents. The platform scales up to enterprise research labs (OpenAI, Meta, NVIDIA, GlaxoSmithKline, Toyota all use it) because the Model Registry and artifact lineage tracking handle the governance needs of large organizations.

For LLM application teams, W&B Weave is most compelling when the team already uses W&B Models. The unified platform reduces tool sprawl and gives visibility from training to inference in one dashboard. Teams building exclusively on pre-trained APIs with no custom model training may prefer lighter-weight, LLM-specific tools: Langfuse for open-source self-hosting, or Helicone for a simpler cost-tracking focus.

The platform works well alongside GPU cloud providers. Teams running training jobs on RunPod, Modal, or Anyscale use W&B to log metrics from distributed training without needing to change their compute setup. The post-acquisition Mission Control integration makes this even tighter for CoreWeave customers, correlating GPU node events directly with training run dashboards.

Academic researchers get a meaningful benefit: W&B's academic program provides a Pro-equivalent license free of charge, with unlimited tracked hours and 200GB storage. This is one of the more generous research programs among commercial ML tools.

What W&B is not

W&B is not a complete MLOps pipeline orchestrator. It tracks experiments and versions artifacts, but it does not schedule training jobs, manage compute provisioning, or orchestrate multi-step pipelines. For pipeline orchestration you need a separate tool (Prefect, ZenML, Metaflow, or Kubeflow) alongside W&B.

It is not a suitable primary tool for teams that require zero vendor dependency. The self-hosted option exists but "corporate use is not allowed" under the free Personal tier, and the Advanced Enterprise self-hosted plan is custom-priced. If open-source with self-hosting is a hard requirement, MLflow is the right choice.

It is not optimized for GenAI teams running purely API-based applications with no custom model training. The 1GB Weave data ingestion limit on the Free tier and 1.5GB on Pro is a real constraint for production LLM apps with meaningful traffic. The overage cost ($0.10/MB above the tier limit) adds up quickly at scale. A self-hosted Langfuse instance has no trace volume limit.

It is not a real-time inference serving platform, despite the new W&B Inference product. That product is in preview as of mid-2025 and is positioned as a convenience for model evaluation, not a production serving infrastructure replacement for dedicated options like Anyscale or RunPod.