Weights & Biases vs Comet ML: which experiment tracking platform wins in 2026?
Weights & Biases wins on community + integrations + LLM-specific features (Weave, Prompts). Comet ML wins on enterprise self-host + pricing + workflow automation (Opik for LLM evals). Pick W&B for breadth, Comet for self-host or LLM evals first.
At a glance
| Dimension | Weights & Biases | Comet ML |
|---|---|---|
| Experiment tracking | Industry standardWIN | Mature equivalent |
| LLM observability | Weave + Prompts | Opik (full LLM eval platform) |
| Hyperparameter sweeps | Best-in-classWIN | Solid |
| Model registry | Yes | Yes |
| Datasets / artifacts | First-class | First-class |
| Self-host | Enterprise tier | Free self-host (Opik OSS)WIN |
| Pricing | Free academic + usage-based paid | Free academic + usage-based paid |
| Integrations | PyTorch, TF, JAX, HF, all majorsWIN | PyTorch, TF, JAX, HF |
| Best for | Broad ML workflows, LLM teams via Weave | Self-host first, LLM evals via Opik OSS |
Verdict
Weights & Biases is the right pick for teams wanting industry-standard tracking with the broadest community + integration ecosystem, plus Weave for LLM workflows. Comet ML is the right pick for self-host-required teams (Opik OSS gives full LLM eval out of the box) and teams that want a unified eval-first LLM observability path. Both are mature; the choice is community vs self-host + eval-first.
When to pick which
Pick Weights & Biases
Industry-standard tracking, broadest integrations, Weave for LLM apps.
Pick Comet ML
Self-host (Opik OSS), LLM eval first, predictable pricing.
FAQ
Self-hostable?
Both — W&B has an enterprise tier; Comet ships Opik as open-source self-host for LLM observability.
Better for LLM apps?
Comet via Opik (open-source eval platform) is more LLM-eval-focused; W&B Weave is broader.
Cheaper?
Both have free academic tiers and similar paid pricing; depends on workload size.
Last updated: 2026-06-01.