IT AI Index

Index › Vendors › PyTorch · September 2026 Edition

1 category · Named, not ranked

PyTorch

10Judge labels

0First choices

0Negative labels

5 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

8 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. PyTorch was named 8 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 business68 of 124

under the label threshold

Mid-market21 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 PyTorch 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%	21 of 99	0%	8	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 PyTorch 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 PyTorch across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

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

Comparative3 labelsNone

Budget-constrained4 labelsNone

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative10 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.

“Combine open-source libraries (PyTorch, TensorFlow, scikit-learn) with a notebook environment” GLM 4.7 FlashX · ML platforms · budget prompt · alternative

“Use open-source tools (PyTorch + MLflow) on Google Vertex AI with spot GPUs.” DeepSeek V4 Flash · ML platforms · budget prompt · alternative

“Choose PyTorch for research, experimentation, and cutting-edge deep learning” Kimi K2 · ML platforms · comparative prompt · alternative

“PyTorch - Meta's open-source ML framework, free and widely used” Claude Haiku 4.5 · 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 PyTorch, over the 10 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and PyTorch was named but was not.

ProductSame answerTook the first choice insteadHead to head

Azure Machine Learning9 of 101Not in the top three

TensorFlow9 of 101Not in the top three

Amazon SageMaker8 of 101Not in the top three

Google Vertex AI8 of 101Not in the top three

scikit-learn8 of 101Not in the top three

Databricks6 of 100Not in the top three

MLflow5 of 101Not in the top three

DataRobot4 of 100Not in the top three

H2O.ai3 of 100Not in the top three

Kaggle3 of 100Not 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 PyTorch. 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 8 of the 10 answers that named PyTorch and are not a share of its labels.

Domains cited

deepusecase.com5

g2.com4

aimultiple.com3

github.com3

godofprompt.ai3

modern-datatools.com3

softwr.com3

unite.ai3

articsledge.com2

checkthat.ai2

Thirty-one of the thirty-one domain citations in answers naming PyTorch came from somebody else's page.

Pages cited

aimultiple.com/open-source-ai-platforms3

deepusecase.com/blog/machine-learning-software-comparison-20243

g2.com/discussions/what-is-the-most-cost-efficient-machine-learning-platform3

godofprompt.ai/blog/machine-learning-platforms-for-small-businesses3

softwr.com/best/machine-learning-for-small-businesses3

unite.ai/10-best-machine-learning-software3

articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-sagemaker-vs-go2

checkthat.ai/answers/what-are-the-best-machine-learning-platforms2

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 PyTorch'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 PyTorch, 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 pytorch.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.