IT AI Index

Index › Vendors › Weights & Biases' Weave toolkit · September 2026 Edition

1 category · Named, not ranked

Weights & Biases' Weave toolkit

2Judge labels

0First choices

2Negative labels

2 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Weights & Biases' Weave toolkit was named 2 times in ML platforms, where Azure Machine Learning led with 17%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market96 of 99

under the label threshold

Enterprisenot named

No model named it for this buyer.

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' Weave toolkit 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
ML platforms	Data platform	0%	96 of 99	100%	2	under 10 labels · led by Azure Machine Learning at 17%

Movement

This is the first edition on this tier, so no move can be computed for Weights & Biases' Weave toolkit yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Weights & Biases' Weave toolkit across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Negative	Labels
Claude Haiku 4.5	1	1
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	0	0
Mistral Small	0	0
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	1	1
GLM 4.7 FlashX	0	0
MiniMax M2.5	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

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative2 labels in all, every segment counted; 0 of the 0 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.

No positive label carried a quote.

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.

“Weights & Biases' Weave toolkit – Directory traversal vulnerability allowing privilege escalation” Kimi K2 · ML platforms · negative prompt · hard negative

“had a directory traversal vulnerability (CVE-2024-7340) that allows low-privileged users to escalate their permissions” Claude Haiku 4.5 · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as Weights & Biases' Weave toolkit, over the 2 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Weights & Biases' Weave toolkit was named but was not.

ProductSame answerTook the first choice insteadHead to head

ZenML2 of 20Not in the top three

Databricks1 of 20Not in the top three

Deep Lake1 of 20Not in the top three

Lunary1 of 20Not in the top three

MLflow1 of 20Not in the top three

NVIDIA's NeMo1 of 20Not in the top three

OpenAI1 of 20Not in the top three

A head-to-head page exists where both products are in a category's top three. 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' Weave toolkit. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 2 of the 2 answers that named Weights & Biases' Weave toolkit and are not a share of its labels.

Domains cited

sciencedirect.com2

siliconangle.com2

aimultiple.com1

arxiv.org1

augmentcode.com1

community.databricks.com1

compunnel.com1

dataiku.com1

developers.openai.com1

digitalapplied.com1

Twelve of the twelve domain citations in answers naming Weights & Biases' Weave toolkit came from somebody else's page.

Pages cited

sciencedirect.com/science/article/pii/S26663899240018802

siliconangle.com/2024/11/04/jfrog-report-highlights-critical-security-flaws-machine-learni2

aimultiple.com/responsible-ai-platform1

arxiv.org/pdf/1901.100021

arxiv.org/pdf/1906.107421

arxiv.org/pdf/2201.047361

arxiv.org/pdf/2504.179771

augmentcode.com/guides/model-agnostic-ai-why-provider-lock-in-is-so-expensive1

Pages are listed as the models cited them.

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' Weave toolkit'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' Weave toolkit, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at weights.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.