Every category where a model named TensorFlow 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%	8 of 99	0%	10	accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for TensorFlow 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 TensorFlow 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	1	0	1	2
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	0	0	1	1
Mistral Small	0	0	1	1
DeepSeek V4 Flash	0	1	1	2
Llama 4 Maverick	1	0	0	1
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	0	1	0	1
MiniMax M2.5	0	1	0	1

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

Comparative4 labelsNone

Budget-constrained6 labels3

Scale-constrained0 labelsNone

Negative2 labelsNone

First choiceAlternativeMentionNegative13 labels in all, every segment counted; 3 of the 3 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.

“such as KNIME or TensorFlow... TensorFlow is suitable for developers building custom models” Llama 4 Maverick · ML platforms · budget prompt · first choice

“open-source tools like TensorFlow or Scikit-learn” Claude Haiku 4.5 · ML platforms · budget prompt · first choice

“Choose TensorFlow for production deep learning systems that need to scale” Kimi K2 · ML platforms · comparative prompt · alternative

“TensorFlow \u2013 Production-ready, TPU support, extensive ecosystem.” 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 TensorFlow, over the 13 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and TensorFlow was named but was not.

ProductSame answerTook the first choice insteadHead to head

Azure Machine Learning11 of 131Not in the top three

Amazon SageMaker10 of 131Not in the top three

Google Vertex AI10 of 131Not in the top three

PyTorch9 of 130Not in the top three

Databricks7 of 130Not in the top three

scikit-learn7 of 130Not in the top three

DataRobot4 of 130Not in the top three

MLflow4 of 130Not in the top three

RapidMiner4 of 130Not in the top three

KNIME Analytics Platform3 of 132Not 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 TensorFlow. 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 11 of the 13 answers that named TensorFlow and are not a share of its labels.

Domains cited

g2.com6

deepusecase.com4

softwr.com4

checkthat.ai3

dagshub.com3

medium.com3

trustradius.com3

unite.ai3

aimultiple.com2

articsledge.com2

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

Pages cited

g2.com/discussions/what-is-the-most-cost-efficient-machine-learning-platform4

dagshub.com/blog/top-deep-learning-platforms3

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

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

trustradius.com/machine-learning3

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

aimultiple.com/open-source-ai-platforms2

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

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 TensorFlow'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 TensorFlow, 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 tensorflow.org 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.