IT AI Index

Index › Vendors › Kaggle · September 2026 Edition

1 category · Named, not ranked

Kaggle

15Judge labels

4First choices

1Negative labels

7 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

7 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Kaggle was named 7 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 business6 of 124

under the label threshold

Mid-market9 of 99

under the label threshold

Enterprise70 of 75

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 Kaggle 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	4%	9 of 99	0%	7	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 Kaggle 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 Kaggle across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Labels
Claude Haiku 4.5	0	0	0	0
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	0	0
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	1	0	0	1
Mistral Small	1	0	0	1
DeepSeek V4 Flash	0	0	1	1
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	1	1	2
Kimi K2	0	0	1	1
GLM 4.7 FlashX	0	1	0	1
MiniMax M2.5	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

Direct1 labelNone

Paraphrase0 labelsNone

Comparative2 labelsNone

Budget-constrained10 labels4

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative15 labels in all, every segment counted; 4 of the 4 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.

“Google Colab (free tier) or Kaggle Notebooks are typically the best starting points for companies with limited budgets.” Grok 4.1 Fast · ML platforms · budget prompt · first choice

“start with Kaggle Notebooks (free) or KNIME (open-source)” Mistral Small · ML platforms · budget prompt · first choice

“Development: Kaggle (for access to free powerful GPUs).” Qwen 3.7 Flash · ML platforms · budget prompt · alternative

“free cloud notebooks (Kaggle, Colab, Databricks Community)” GLM 4.7 FlashX · ML platforms · budget 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.

No model argued against it.

Named alongside

The products named in the same answers as Kaggle, over the 15 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Kaggle was named but was not.

ProductSame answerTook the first choice insteadHead to head

Azure Machine Learning12 of 150Not in the top three

Google Colab11 of 152Not in the top three

Google Vertex AI9 of 152Not in the top three

Amazon SageMaker9 of 150Not in the top three

KNIME8 of 151Not in the top three

MLflow7 of 151Not in the top three

Hugging Face6 of 152Not in the top three

KNIME Analytics Platform4 of 152Not in the top three

Databricks4 of 150Not in the top three

Weights & Biases4 of 150Not 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 Kaggle. 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 13 of the 15 answers that named Kaggle and are not a share of its labels.

Domains cited

g2.com12

godofprompt.ai8

softwr.com8

startupstash.com7

devopsschool.com6

aimultiple.com4

reddit.com4

trustradius.com4

alphasoftware.com3

articsledge.com3

Fifty-nine of the fifty-nine domain citations in answers naming Kaggle came from somebody else's page.

Pages cited

g2.com/discussions/what-is-the-most-cost-efficient-machine-learning-platform11

godofprompt.ai/blog/machine-learning-platforms-for-small-businesses8

softwr.com/best/machine-learning-for-small-businesses7

startupstash.com/best-low-code-machine-learning-platforms7

devopsschool.com/blog/top-10-machine-learning-platforms-tools-in-2025-features-pros-cons-c5

aimultiple.com/open-source-ai-platforms4

trustradius.com/machine-learning4

alphasoftware.com/blog/best-low-code-tools-for-machine-learning-prices-features-compared3

Pages are listed as the models cited them.

Names read as Kaggle

What the judge wrote, as written, with how often. The vendor table decides that these count as Kaggle; a claim can dispute any of them.

Kaggle Notebooks 6Kaggle / Colab 1

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 Kaggle'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 Kaggle, 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 kaggle.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.