Weights & BiasesWeights & Biases
Experiment tracking across ML and LLM work
The long-standing standard for machine-learning experiment tracking, extended into LLM tracing and evaluation. Its advantage is that it covers both classical ML and generative work in one system of record.
[ 01 ] The verdict
The correct choice for organisations doing both classical ML and LLM work who want one lineage story. Pure LLM teams will find lighter, cheaper tools that fit better.
Best for
ML organisations that train models as well as call them, needing unified experiment lineage.
Watch out
The product surface is large and older sections carry historical complexity. Expect onboarding effort.
Strengths
- Covers training and inference in one lineage graph
- Mature artefact versioning and model registry
- Self-hosted deployment for regulated environments
- Deep integration across the ML tooling ecosystem
Trade-offs
- Heavier than LLM-only observability tools
- Pricing has several dimensions to model
- UI complexity reflects a long feature history
[ 02 ] What it actually does
What Weights & Biases actually ships.
Experiment tracking
Log metrics, hyperparameters and system stats across runs with rich comparison views.
Weave
LLM tracing and evaluation layer sharing lineage with training experiments.
Model registry
Versioned model artefacts with staging, approval and lineage back to training data.
Sweeps
Distributed hyperparameter search with early stopping.
[ 04 ] Weights & Biases alternatives
