Small business▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
2%from 0% in September 2026
Inside the floor by 9 pointsRank 26 → 12 of 131
Weights & Biases
27Judge labels
2First choices
6Negative labels
12 / 14Models named it
2Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
2% in ML platforms for small business buyers
Rank 18 of 107 in the mid-market standing
0 of 14 models made it the first choice on the direct prompt; 33% of its 9 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business13 of 139
2%13 labels
accepted challenger
Mid-market18 of 107
2%9 labels
under the label threshold
Enterprise63 of 65
0%3 labels
under the label threshold
In ml platforms · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named Weights & Biases for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| ML platforms | Data platform | 2% | 18 of 107 | 33% | 9 | under 10 labels · led by Azure Machine Learning at 21% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| AI governance platforms | Data platform | 0% | 113 of 135 | 0% | 1 | under 10 labels · led by Credo AI at 18% |
Movement
Since September 2026, Weights & Biases held within the floor in all two categories: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-market▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
2%from 0% in September 2026
Inside the floor by 9 pointsRank 21 → 18 of 97
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 16 → 53 of 55
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 0% | 2% | ▲+2Since September 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 21 → 18 of 107 |
| AI governance | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 113 of 135, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Weights & Biases across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Weights & Biases across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 1 | 0 | 0 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 1 | 1 | 0 | 2 |
| Perplexity Sonar | 0 | 0 | 0 | 1 | 1 |
| Grok 4.20 | 0 | 0 | 1 | 0 | 1 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 1 | 0 | 1 |
| Kimi K2 | 0 | 1 | 1 | 1 | 3 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 1 | 1 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct0 labelsNone
Paraphrase8 labels2
Comparative3 labelsNone
Budget-constrained3 labelsNone
Scale-constrained8 labelsNone
Negative5 labelsNone
First choiceAlternativeMentionNegative27 labels in all, every segment counted; 2 of the 2 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“For most mid-sized B2B companies, I'd suggest starting with Weights & Biases combined with cloud-native infrastructure” Claude Haiku 4.5 · ML platforms · paraphrase prompt · first choice
“Use this exclusively for experiment tracking, artifact versioning, and model registry during the training phase” Gemini 3.5 Flash · ML platforms · paraphrase prompt · alternative
“specialized players like Weights & Biases + open-source tools for experiment tracking” Kimi K2 · ML platforms · scale prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“What to Avoid | Weights & Biases | Costs scale quickly” Kimi K2 · ML platforms · paraphrase prompt · hard negative
“be cautious if you need full pipeline orchestration or have strict budget constraints” Perplexity Sonar · ML platforms · negative prompt · soft negative
“Directory traversal vulnerability (CVE-2024-7340)” MiniMax M2.5 · ML platforms · negative prompt · soft negative
Named alongside
The products named in the same answers as Weights & Biases, over the 27 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Weights & Biases was named but was not.ShowHide
Named alongside
The products named in the same answers as Weights & Biases, over the 27 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Weights & Biases was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named Weights & Biases. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 22 of the 27 answers that named Weights & Biases and are not a share of its labels.
Domains cited
axis-intelligence.com11
g2.com11
learn.g2.com10
checkthat.ai9
ciopages.com7
devopsschool.com7
it-ai-index.com7
us.fitgap.com7
dataiku.com6
guideflow.com6
No domain is on file for Weights & Biases, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Weights & Biases stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Weights & Biases stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- not counted (“weights & biases”)
- AI search demand for its name, est.
- 98 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
2%
rank 18 of 107 in ML platforms
Segment leader
21%
Azure Machine Learning
- First choices
- 2 across its categories
- Named in
- 27 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as Weights & Biases
What the judge wrote, as written, with how often. The vendor table decides that these count as Weights & Biases; a claim can dispute any of them.
Weights & Biases (W&B) 2
Follow Weights & Biases
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Weights & Biases's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Weights & Biases, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Weights & Biases by email, built from the raw record of the edition. It shows:
- where Weights & Biases is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Weights & Biases, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.