Every category where a model named DataRobot 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%	19 of 99	16%	19	accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for DataRobot 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 DataRobot across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	1	2	0	3
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	2	0	2	4
Perplexity Sonar	1	0	0	1
Grok 4.1 Fast	1	1	0	2
Mistral Small	0	0	0	0
DeepSeek V4 Flash	2	0	1	3
Llama 4 Maverick	0	1	0	1
Qwen 3.7 Flash	0	0	0	0
Kimi K2	1	1	0	2
GLM 4.7 FlashX	2	0	0	2
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

Direct15 labelsNone

Paraphrase7 labelsNone

Comparative26 labelsNone

Budget-constrained7 labels2

Scale-constrained5 labelsNone

Negative10 labels1not counted in share

First choiceAlternativeMentionNegative70 labels in all, every segment counted; 2 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.

“If you need rapid, governed predictive models: DataRobot's AutoML and compliance features can accelerate time-to-value.” GLM 4.7 FlashX · ML platforms · comparative prompt · alternative

“Consider AutoML platforms (DataRobot, SageMaker Autopilot) when you need rapid model development” Kimi K2 · ML platforms · comparative prompt · alternative

“Highly intuitive visual interfaces designed to bridge data scientists and business analysts.” Gemini 3.5 Flash · ML platforms · comparative prompt · alternative

“Enterprise AutoML; automates model building, tuning, and deployment for non-experts” DeepSeek V4 Flash · ML platforms · comparative 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.

“DataRobot is famous for its opaque and highly expensive enterprise licensing” Gemini 3.5 Flash · ML platforms · negative prompt · hard negative

“enterprise low-code platforms like Alteryx or DataRobot charge steep enterprise licensing fees” Gemini 3.5 Flash · ML platforms · budget prompt · soft negative

“premium-priced — restrictive licensing for smaller teams/startups; heavy enterprise focus” DeepSeek V4 Flash · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as DataRobot, over the 70 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and DataRobot was named but was not.

ProductSame answerTook the first choice insteadHead to head

Amazon SageMaker66 of 7010Not in the top three

Google Vertex AI62 of 7012Not in the top three

Azure Machine Learning61 of 709Not in the top three

Databricks44 of 7011Not in the top three

H2O.ai34 of 700Not in the top three

Dataiku32 of 703Not in the top three

MLflow22 of 703Not in the top three

Domino Data Lab20 of 701Not in the top three

KNIME15 of 701Not in the top three

Kubeflow15 of 701Not 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 DataRobot. 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 56 of the 70 answers that named DataRobot and are not a share of its labels.

Domains cited

g2.com28

checkthat.ai25

learn.g2.com21

tinyctl.dev19

artificialintelligencecompanies.com16

deepusecase.com15

articsledge.com14

medium.com14

thirstysprout.com14

linkedin.com13

179 of the 179 domain citations in answers naming DataRobot came from somebody else's page.

Pages cited

tinyctl.dev/roundups/machine-learning-platforms19

artificialintelligencecompanies.com/best/machine-learning-platforms16

checkthat.ai/answers/what-are-the-best-ai-ml-platforms16

learn.g2.com/best-machine-learning-tools14

thirstysprout.com/post/best-machine-learning-platforms14

articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-sagemaker-vs-go13

checkthat.ai/answers/what-are-the-best-machine-learning-platforms13

learn.g2.com/best-data-science-ml-platforms13

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 DataRobot'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 DataRobot, 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 datarobot.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.