IT AI Index

Index › Vendors › OpenAI · September 2026 Edition

2 categories · Named, not ranked

OpenAI

7Judge labels

0First choices

2Negative labels

4 of 12Models named it

2Categories

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. OpenAI was named 2 times in LLM gateways and 1 other category, where LiteLLM led with 38%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market33 of 63

under the label threshold

Enterprisenot named

No model named it for this buyer.

In llm gateways · 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 OpenAI 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
LLM gateways and observability	Data platform	0%	33 of 63	0%	1	under 10 labels · led by LiteLLM at 38%
ML platforms	Data platform	0%	87 of 99	100%	1	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 OpenAI 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 OpenAI across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

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

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained1 labelNone

Scale-constrained1 labelNone

Negative5 labelsNone

First choiceAlternativeMentionNegative7 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.

“Direct API access from reputable providers (OpenAI, Anthropic, Google, Azure)” Kimi K2 · LLM gateways · negative 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.

“Platforms with aggressive model deprecation policies (like some Azure Databricks and OpenAI services) can disrupt your operations” Kimi K2 · ML platforms · negative prompt · soft negative

Named alongside

The products named in the same answers as OpenAI, over the 7 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and OpenAI was named but was not.

ProductSame answerTook the first choice insteadHead to head

MLflow4 of 71Not in the top three

Anthropic4 of 70Not in the top three

Amazon Bedrock3 of 70Not in the top three

Amazon SageMaker3 of 70Not in the top three

Databricks3 of 70Not in the top three

Google Vertex AI3 of 70Not in the top three

Kubeflow2 of 71Not in the top three

Azure Machine Learning2 of 70Not in the top three

Hugging Face2 of 70Not in the top three

KNIME Analytics Platform1 of 71Not 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 OpenAI. 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 5 of the 7 answers that named OpenAI and are not a share of its labels.

Domains cited

dataiku.com2

itea.org2

learn.microsoft.com2

openai.com2

7wdata.be1

aimultiple.com1

aisotools.com1

aitoolsatlas.ai1

arxiv.org1

augmentcode.com1

Fourteen of the fourteen domain citations in answers naming OpenAI came from somebody else's page.

Pages cited

dataiku.com/blog/enterprise-machine-learning-platforms-how-to-choose2

itea.org/journals/volume-47-2/avoid-vendor-lock-in-ai-procurement2

openai.com/business-data2

7wdata.be/tool/datarobot-ai-platform1

aimultiple.com/responsible-ai-platform1

aisotools.com/blog/datarobot-review-20261

aitoolsatlas.ai/tools/datarobot/pros-cons1

arxiv.org/html/2505.01976v11

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 OpenAI'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 OpenAI, 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 openaiclaude.com is approved on the spot, any other address is reviewed by hand.

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