IT AI Index
Index › Vendors › RapidMiner · September 2026 Edition
1 category · Ranked

RapidMiner

15Judge labels
0First 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.
Best standing
0% in ML platforms for small business buyers
Rank 27 of 99 in the mid-market standing
0 of 12 models made it the first choice on the direct prompt; 0% of its 4 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business25 of 124
0%10 labels
accepted challenger
Mid-market27 of 99
0%4 labels
under the label threshold
Enterprise72 of 75
0%1 label
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 RapidMiner 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.
CategoryFunctionShareRankNegative rateLabelsQuadrant
ML platformsData platform0%27 of 990%4under 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 RapidMiner 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 RapidMiner across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500202
GPT-5.4 mini00000
Gemini 3.5 Flash00000
Perplexity Sonar00000
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash01001
Llama 4 Maverick00000
Qwen 3.7 Flash00000
Kimi K200000
GLM 4.7 FlashX00000
MiniMax M2.500000

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
Comparative5 labelsNone
Budget-constrained2 labelsNone
Scale-constrained0 labelsNone
Negative6 labels1not counted in share
First choiceAlternativeMentionNegative15 labels in all, every segment counted; 0 of the 1 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.

“Visual workflow designer with AutoML, ideal for non-programmers.” 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.

No model argued against it.

Named alongside

The products named in the same answers as RapidMiner, over the 15 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and RapidMiner was named but was not.
ProductSame answerTook the first choice insteadHead to head
Amazon SageMaker14 of 151Not in the top three
Azure Machine Learning13 of 151Not in the top three
Google Vertex AI10 of 152Not in the top three
DataRobot9 of 151Not in the top three
Databricks9 of 151Not in the top three
KNIME7 of 150Not in the top three
Kubeflow6 of 150Not in the top three
MLflow6 of 150Not in the top three
H2O.ai5 of 150Not in the top three
Google Colab4 of 151Not 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 RapidMiner. 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 RapidMiner and are not a share of its labels.

Domains cited

g2.com8
dagshub.com7
devopsschool.com7
tinyctl.dev6
reddit.com5
startupstash.com5
articsledge.com4
checkthat.ai4
deepusecase.com4
godofprompt.ai4

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

Pages cited

dagshub.com/blog/top-deep-learning-platforms7
tinyctl.dev/roundups/machine-learning-platforms6
devopsschool.com/blog/top-10-machine-learning-platforms-tools-in-2025-features-pros-cons-c5
startupstash.com/best-low-code-machine-learning-platforms5
articsledge.com/post/machine-learning-platforms-comparison-azure-ai-vs-aws-sagemaker-vs-go4
godofprompt.ai/blog/machine-learning-platforms-for-small-businesses4
checkthat.ai/answers/what-are-the-best-machine-learning-platforms3
dataiku.com/blog/enterprise-machine-learning-platforms-how-to-choose3

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 RapidMiner'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 RapidMiner, 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 rapidminer.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.

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