Every category where a model named Hugging Face 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	2%	16 of 99	33%	6	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 Hugging Face 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 Hugging Face across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	0	0	0	0
GPT-5.4 mini	0	0	0	0	0
Gemini 3.5 Flash	0	1	1	0	2
Perplexity Sonar	0	0	0	0	0
Grok 4.1 Fast	0	1	0	1	2
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	1	0	0	0	1
Kimi K2	0	0	0	0	0
GLM 4.7 FlashX	0	0	0	1	1
MiniMax M2.5	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

Direct2 labelsNone

Paraphrase0 labelsNone

Comparative3 labels1not counted in share

Budget-constrained6 labels1

Scale-constrained1 labelNone

Negative7 labels1not counted in share

First choiceAlternativeMentionNegative19 labels in all, every segment counted; 1 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.

“Best Overall Ecosystem (Prototyping & Sharing) Hugging Face This has become the industry standard for open-source ML.” Qwen 3.7 Flash · ML platforms · budget prompt · first choice

“Free CPU/GPU inference; host models/Spaces; great for NLP/LLMs. | Shared resources; quotas on free tier.” Grok 4.1 Fast · ML platforms · budget prompt · alternative

“Choose Hugging Face if you are working with LLMs, NLP, or generative AI.” Gemini 3.5 Flash · 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.

“Vulnerabilities in conversion bots, model loading ... Popular for models but risky for untrusted repos.” Grok 4.1 Fast · ML platforms · negative prompt · soft negative

“it is a prime target for supply chain attacks... Do not blindly download models.” GLM 4.7 FlashX · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as Hugging Face, over the 19 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Hugging Face was named but was not.

ProductSame answerTook the first choice insteadHead to head

Azure Machine Learning17 of 191Not in the top three

Amazon SageMaker16 of 190Not in the top three

Google Vertex AI15 of 191Not in the top three

MLflow10 of 190Not in the top three

Google Colab7 of 193Not in the top three

DataRobot7 of 191Not in the top three

Databricks7 of 190Not in the top three

Weights & Biases7 of 190Not in the top three

KNIME6 of 191Not in the top three

Kaggle6 of 191Not 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 Hugging Face. 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 9 of the 19 answers that named Hugging Face and are not a share of its labels.

Domains cited

dagshub.com6

checkthat.ai5

g2.com5

startupstash.com5

articsledge.com4

devopsschool.com4

learn.g2.com4

reddit.com4

thirstysprout.com4

tinyctl.dev4

Forty-five of the forty-five domain citations in answers naming Hugging Face came from somebody else's page.

Pages cited

dagshub.com/blog/top-deep-learning-platforms6

checkthat.ai/answers/what-are-the-best-machine-learning-platforms5

startupstash.com/best-low-code-machine-learning-platforms5

thirstysprout.com/post/best-machine-learning-platforms4

tinyctl.dev/roundups/machine-learning-platforms4

articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-sagemaker-vs-go3

devopsschool.com/blog/top-10-machine-learning-platforms-tools-in-2025-features-pros-cons-c3

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

Pages are listed as the models cited them.

Names read as Hugging Face

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

Hugging Face (Spaces/ZeroGPU) 1Hugging Face Hub 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 Hugging Face'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 Hugging Face, 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 huggingface.co 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.