{"id":"wandb-api","name":"Weights & Biases","homepage":"https://wandb.ai","repo_url":"https://github.com/wandb/wandb","category":"ai-ml","subcategories":["ml-experiment-tracking","model-management","ai-observability"],"tags":["wandb","weights-and-biases","ml-tracking","experiment-tracking","model-registry","llm-monitoring"],"what_it_does":"ML experiment tracking and model management platform that logs training metrics, visualizes model performance, manages model artifacts, and monitors LLM applications in production via a REST API and Python SDK.","use_cases":["Logging training runs, hyperparameters, and metrics for ML model development","Comparing experiment results across runs to identify optimal model configurations","Managing model artifacts and versioning via the model registry","Monitoring LLM application quality and costs in production via Weave (W&B's LLM product)","Automating hyperparameter sweeps and surfacing results via API"],"not_for":["Production infrastructure monitoring (use Datadog or Prometheus for ops metrics)","Teams not doing ML model training or LLM application development","Simple data science notebooks without experiment comparison needs","Non-ML software observability (too ML-specific)"],"best_when":"You're training ML models or building LLM applications and need experiment tracking, artifact management, and collaborative result sharing across a data science team.","avoid_when":"You're building non-ML applications, or your ML workflow is simple enough that local logging and manual comparison is sufficient.","alternatives":["comet-api","langsmith-api","mlflow"],"af_score":78.6,"security_score":null,"reliability_score":null,"package_type":"mcp_server","discovery_source":["github"],"priority":"low","status":"evaluated","version_evaluated":"current","last_evaluated":"2026-03-01T09:50:06.383148+00:00","performance":{"latency_p50_ms":100,"latency_p99_ms":400,"uptime_sla_percent":99.9,"rate_limits":"No prominently documented rate limits; SDK handles batching and retries automatically","data_source":"llm_estimated","measured_on":null}}