Weights & Biases
FreemiumThe AI developer platform. .
Published 29 May 2026 · Last updated 27 September 2026
Scores
Popularity3/5
Well-known within the ML research and engineering community — 11K+ GitHub stars on the SDK, widely cited in papers and tutorials. Recognised within the experiment tracking niche but not broadly known outside it.
Learning Curve2/5
Extremely low barrier to entry — wandb.init() and wandb.log() are all that is needed to start tracking, and the dashboard is immediately useful without any configuration. Sweeps and the Registry introduce additional concepts once the basics are comfortable.
Flexibility3/5
Configurable within W&B's conventions — custom charts, custom panels, and the full Python SDK allow fine-grained control. However, the platform is cloud-first and somewhat opinionated; teams with strict data residency requirements or unusual logging patterns hit friction.
Performance4/5
Real-time metric sync with sub-second latency in normal conditions. Dashboard rendering is fast for typical experiment volumes. Some users report slowdowns at very high run counts or large artifact sizes.
Portability3/5
Cloud-hosted by default which creates vendor dependency. W&B Server provides a self-hosted option, but it requires Kubernetes and adds operational overhead. Data export is possible but not as simple as MLflow's file-based tracking store.
About Weights & Biases
Weights & Biases (W&B, or wandb) is an AI developer platform for tracking machine learning experiments, comparing runs, and managing models and datasets from training to production. It has been part of CoreWeave since 2025.
Two calls, wandb.init() and wandb.log(), capture a training run: metrics, hyperparameters, system usage, code version, and media such as images, audio, and confusion matrices. Everything syncs live to a shared dashboard where a team can compare runs with parallel-coordinates plots, custom charts, and reports. Integrations cover PyTorch, Keras, TensorFlow, scikit-learn, XGBoost, and Hugging Face.
Around tracking sit the rest of the model lifecycle. Sweeps run hyperparameter searches (Bayesian, grid, or random), Artifacts version datasets and model files with lineage, and the Registry promotes models from experiment to production with governance. W&B Weave adds tracing and evaluation for LLM applications and agents.
W&B is a hosted service by default, with a free tier for individuals and small teams (up to five seats, limited storage and Weave ingestion) and a Pro plan for early-stage teams. Enterprise runs as a dedicated cloud or self-hosted on your own infrastructure, and a free self-hosted Personal licence covers non-corporate use. It focuses on tracking and evaluation rather than model serving, and MLflow is the usual open-source alternative.
Key Features
- Experiment tracking with two lines of code (wandb.init, wandb.log)
- Live, shareable dashboards and reports for comparing runs
- Sweeps: hyperparameter optimisation with Bayesian, grid, or random search
- Artifacts: versioned datasets and models with lineage
- Registry for promoting and governing models
- W&B Weave: tracing and evaluation for LLM apps and agents
- Integrations with PyTorch, Keras, TensorFlow, scikit-learn, XGBoost, and Hugging Face
Pros
- Minimal setup: two calls capture a full training run
- Strong visualisation: parallel coordinates, custom charts, media panels
- Hosted by default, with no infrastructure to run
- Live sync makes it easy for a team to compare runs
- Free tier covers solo researchers and small teams
Cons
- Data leaves your environment unless you pay for a self-hosted Enterprise deployment
- Storage and Weave ingestion are metered beyond plan limits
- The self-hosted Personal licence excludes corporate use
- No model serving: W&B covers tracking and evaluation, not deployment
Weights & Biases Pricing
Freemium- · One user seat on your own server
- · Corporate use not allowed
- · Up to 5 model seats
- · 5 GB storage per month
- · 1 GB Weave data ingestion per month
- · Experiment tracking and Registry
- · From $60 per month
- · Up to 10 model seats
- · 100 GB storage per month, extra at $0.03/GB
- · 1.5 GB Weave data ingestion per month
- · Custom seats, storage, and Weave ingestion
- · Single-tenant option with choice of region
- · SSO, audit logs, and compliance features
- · Contact sales for pricing
- · Deploy on your own infrastructure
- · HIPAA compliance, encryption, SSO, audit logs
- · Trial licence available; contact sales for pricing
Tech Stacks with Weights & Biases
MLOps Pipeline
ProjectProduction-grade ML infrastructure. PyTorch for model training, Apache Airflow (or Dagster or Prefect) for orchestration, dbt for feature transformations, and Snowflake as the data warehouse, with Docker as an optional containerization addition.
Backend
Programming
Databases
Hosting
PyTorch ML Training
ProjectTrain deep learning models with PyTorch, with scikit-learn baselines to compare against, Pandas for data preparation, and Jupyter for experimentation. MLflow or Weights & Biases can be added to track experiments and model versions once runs need comparing.
TensorFlow ML Training
ProjectBuild and train ML models using TensorFlow and Keras, from prototyping in Jupyter notebooks to exported models ready for serving. MLflow or Weights & Biases can be added to track experiments and model versions once runs need comparing.
Tools Related to Weights & Biases
Works well with Weights & Biases(1)
W&B works with scikit-learn for tracking classical ML experiments — metrics from cross-validation and grid search runs can be logged to W&B to compare model families side by side.
Integrates with Weights & Biases(3)
W&B provides a first-party PyTorch integration — wandb.watch() hooks into a PyTorch model to automatically log gradients and parameters, and wandb.log() tracks training metrics per step with no boilerplate.
W&B integrates with Keras through WandbCallback — adding it to model.fit() callbacks automatically logs training/validation metrics, hyperparameters, and model topology to the W&B dashboard.
W&B provides a WandbCallback for TensorFlow/Keras training — it captures metrics, model architecture, and gradients automatically during tf.keras model.fit() calls.
Alternatives to Weights & Biases(1)
Weights & Biases and MLflow are the two dominant ML experiment tracking platforms — both log metrics, parameters, and artifacts, but W&B is cloud-hosted with richer visualisations while MLflow is self-hosted and has a deeper model-serving story.
Vendor
Weights & Biases
Website →Tags
Details
- Maintained
- Yes