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Weights and Biases
Experiment tracking and model-development platform used by machine learning teams to log runs, datasets, and artifacts.
ML teams that train models and need a shared record of runs, metrics, and artifacts.
App teams that only call a hosted model API and have no training loop.
Verdict
Weights & Biases is a great fit for machine learning teams that train or fine-tune models and need the run history in one place. Pricing is mid-range for MLOps and scales with seats and tracked runs, fair once training is real work. Research and ML engineering groups are loyal to the shared record of metrics and artifacts; it remains a category default for experiment tracking.
Score Breakdown
How Weights and Biases scores in the categories that matter to its buyers.
Pricing
Weights & Biases sells cloud-hosted Free, Pro, and Enterprise plans, with separate self-hosted options. Prices below are USD from W&B’s public pricing page as of September 2026.
| Plan | Price | Model seats | What stands out |
|---|---|---|---|
| Free (cloud) | $0 / mo | Up to 5 | 5 GB storage; 1 GB / mo Weave ingestion |
| Pro (cloud) | Starts at $60 / mo | Up to 10 | 100 GB storage; for teams under 50 employees |
| Enterprise (cloud) | Sales quote | Custom | SSO, audit logs, HIPAA option, private connectivity |
| Personal (self-hosted) | $0 / mo | 1 | Local Docker server; corporate use not allowed |
| Advanced Enterprise (self-hosted) | Sales quote | Custom | Self-managed with enterprise controls |
| Extra cloud storage | $0.03 / GB | Usage | Billed monthly above plan storage |
| Extra Weave ingestion | $0.10 / MB | Usage | Pro overage meter |
The Field at a Glance
Where Weights & Biases ranks among MLOps & experiment tracking vendors we reviewed, by Overall Score and relative typical engagement cost.
Weights & Biases scores 8.2, ahead of ClearML (7.5), Comet (6.9), and Domino Data Lab (6.8). Its relative typical engagement cost is higher than Comet's and ClearML's and just below Domino Data Lab's.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Training run history | Strong | The main reason teams adopt it. |
| Fine-tune comparisons | Strong | Fits the same logging habit. |
| Hosted-API app with no training | Poor | Light tracing covers an app that only calls a hosted model. |
| Replacing a data warehouse | Poor | It is not a business system of record. |
Review Excerpts
Excerpts from public reviews and forum discussions of Weights and Biases. Paid reviews and pay-for-play sites were excluded.
“We use Weights and Biases for tracking experiments, metrics, log configs, model artifcats”
“Use wandb. It is much better in every way. We used TensorBoard in our company. I made the switch over to wandb. It is much nicer experience.”
“Powerful platform, but designed primarily for traditional ML experiment tracking. Setup is complex and requires significant ML expertise. Our product team struggles to use it effectively.”
Company Facts
- Founded
- 2017 (CB Insights)
- Headquarters
- San Francisco, California, United States (CB Insights)
- Total funding raised
- $250M (CB Insights)
- Status
- Acquired by CoreWeave in May 2025 (CB Insights, CoreWeave)
- Key investors
- CoreWeave, Coatue, Bloomberg Beta, Insight Partners (CB Insights)
- Website
- wandb.ai
Key Leadership
Lukas Biewald
Co-founder
Methodology
This page is an independent evaluation of Weights and Biases for buyers comparing options in MLOps and experiment tracking. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Weights and Biases did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for MLOps and experiment tracking)
- Experiment tracking
- Artifact and dataset record
- Team collaboration
- LLM-era fit
- Company & commercial
- Innovation &
- product leadership
- Project management &
- communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
Weights and Biases is graded here as MLOps and experiment tracking. Criteria scores can move as more review volume and product checks are added.