Weights & Biases

Weights & Biases is a comprehensive MLOps platform designed to manage the full lifecycle of machine learning projects. It provides experiment tracking with metrics visualization, hyperparameter optimization through Sweeps, and model registry capabilities for version control. The platform has evolved beyond basic tracking to include Weave, a production-focused suite for LLM applications featuring traces, evaluations, and agentic system observability. W&B also offers serverless infrastructure for fine-tuning and training LLMs, eliminating GPU management overhead. The platform integrates deeply with modern ML workflows through its SDK, artifact management, and automation triggers, positioning itself as essential infrastructure for teams moving models from experimentation to production at scale.

AI analysis from Feb 23, 2026

• Experiment Tracking with real-time metrics visualization, comparison tables, and historical run management
• Hyperparameter Optimization (Sweeps) with Bayesian search, random search, and grid search capabilities
• Model Registry for versioning, publishing, and sharing trained models across teams
• Weave Traces for debugging and observing LLM application execution paths and latency
• Evaluations framework for programmatic assessment of model outputs against custom metrics
• Serverless RL and SFT training to fine-tune LLMs without GPU infrastructure management
• Artifacts and versioning for managing datasets, models, and pipeline dependencies
• Automated Workflows and Monitors for continuous production monitoring and scheduled evaluations

• 1Tracking and comparing hundreds of experiment runs across different model architectures and hyperparameters for computer vision projects
• 2Fine-tuning large language models without managing GPU infrastructure using serverless SFT (supervised fine-tuning)
• 3Evaluating and monitoring LLM applications in production with traces, prompt evaluation, and automated guardrails
• 4Managing end-to-end autonomous vehicle development with experiment tracking and model registry for distributed teams
• 5Documenting and sharing reproducible research findings through interactive reports and artifact versioning
• 6Optimizing quantitative trading models through systematic experiment logging and hyperparameter sweeps
• 7Debugging and improving RAG (Retrieval-Augmented Generation) pipelines with evaluation frameworks and monitoring

Pricing details not publicly available on the homepage. The website contains a Pricing link but specific tier pricing, free plan limits, and paid plan costs are not displayed in the provided content.

Machine learning engineers and research teams at mid-market and enterprise organizations. Best suited for teams with 5+ people actively training models, managing multiple experiments, or deploying LLM applications to production. Particularly valuable for organizations using PyTorch/TensorFlow, fine-tuning LLMs, or requiring reproducible experiment tracking across distributed teams. Less ideal for solo practitioners or small startups with simple, one-off training needs.

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