Small business▼−6Since September 2026: 10% → 4%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
4%from 10% in September 2026
Inside the floor by 5 pointsRank 3 → 7 of 131
Azure · 2 categories · RankedAzure Machine Learning
159Judge labels
20First choices
34Negative labels
14 / 14Models named it
2Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
21% in ML platforms for mid-market buyers
Rank 1 of 107 in the mid-market standingaccepted challenger
5 of 14 models made it the first choice on the direct prompt; 14% of its 49 labels there were negative.
What the models named instead of Azure Machine Learning →By buyer segmentStrongest at mid-market.
Small business7 of 139
5%48 labels
criticized challenger
Mid-market1 of 107
21%49 labels
accepted challenger
Enterprise3 of 65
11%55 labels
accepted challenger
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 Azure Machine Learning 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 | Since September 2026 |
|---|---|---|---|---|---|---|---|
| ML platforms | Data platform | 21% | 1 of 107 | 14% | 49 | accepted challenger | ▲+1Since September 2026: 17% → 18%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| AI governance platforms | Data platform | 0% | 35 of 135 | 0% | 3 | under 10 labels · led by Credo AI at 18% |
Movement
Since September 2026, Azure Machine Learning held within the floor in all two categories: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-market▲+1Since September 2026: 17% → 18%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.
18%from 17% in September 2026
Inside the floor by 10 pointsRank 2 → 1 of 97
Enterprise▲+1Since September 2026: 12% → 13%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.
13%from 12% in September 2026
Inside the floor by 10 pointsRank 3 of 55, unchanged
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 17% | 18% | ▲+1Since September 2026: 17% → 18%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 2 → 1 of 107 |
| AI governance | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 35 of 135, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Azure Machine Learning across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Azure Machine Learning across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 2 | 1 | 0 | 3 |
| GPT-5.4 mini | 1 | 1 | 0 | 0 | 2 |
| Gemini 3.5 Flash | 0 | 1 | 1 | 1 | 3 |
| Perplexity Sonar | 1 | 1 | 1 | 0 | 3 |
| Grok 4.20 | 1 | 2 | 1 | 2 | 6 |
| Mistral Small | 0 | 2 | 1 | 1 | 4 |
| DeepSeek V4 Flash | 2 | 1 | 2 | 0 | 5 |
| Llama 4 Maverick | 0 | 0 | 1 | 0 | 1 |
| Qwen 3.7 Flash | 0 | 3 | 3 | 1 | 7 |
| Kimi K2 | 2 | 1 | 2 | 0 | 5 |
| GLM 4.7 FlashX | 1 | 0 | 1 | 0 | 2 |
| MiniMax M2.5 | 1 | 1 | 0 | 1 | 3 |
| GPT-6 Luna | 1 | 2 | 0 | 0 | 3 |
| Muse Glimmer 30B | 2 | 0 | 2 | 1 | 5 |
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
Direct27 labels7
Paraphrase31 labels9
Comparative39 labels4not counted in share
Budget-constrained22 labels3
Scale-constrained12 labels1
Negative28 labelsNone
First choiceAlternativeMentionNegative159 labels in all, every segment counted; 20 of the 24 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.
“Small team, tight budget, general ML: Azure Machine Learning or SageMaker AI are solid picks.” GPT-5.4 mini · ML platforms · budget prompt · first choice
“If you want a default pick and you're already Microsoft-centric, I'd choose Azure Machine Learning.” GPT-6 Luna · ML platforms · paraphrase prompt · first choice
“Azure Machine Learning and Google Vertex AI are the most broadly suitable platform choices” Perplexity Sonar · ML platforms · direct prompt · first choice
“AWS SageMaker and Azure Machine Learning are the most practical managed choices” Muse Glimmer 30B · ML platforms · paraphrase prompt · first choice
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.
“models suggesting caution or avoidance unless you have the necessary expertise and budget” Mistral Small · ML platforms · negative prompt · hard negative
“challenges such as data privacy, vendor lock-in, and cost unpredictability remain persistent concerns” Muse Glimmer 30B · ML platforms · negative prompt · soft negative
“Enterprise Cloud Platforms for Small Teams/Startups (e.g., SageMaker, Vertex AI, Azure ML)” Grok 4.20 · ML platforms · negative prompt · soft negative
“very cautious" with enterprise-focused, full-featured cloud ML services” MiniMax M2.5 · ML platforms · negative prompt · soft negative
Named alongside
The products named in the same answers as Azure Machine Learning, over the 159 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Azure Machine Learning was named but was not.ShowHide
Named alongside
The products named in the same answers as Azure Machine Learning, over the 159 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Azure Machine Learning was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. 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 Azure Machine Learning. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 143 of the 159 answers that named Azure Machine Learning and are not a share of its labels.
Domains cited
g2.com66
checkthat.ai47
it-ai-index.com38
learn.g2.com37
deepusecase.com33
eweek.com33
devopsschool.com29
databricks.com26
linkedin.com26
axis-intelligence.com24
359 of the 359 domain citations in answers naming Azure Machine Learning came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Azure Machine Learning stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Azure Machine Learning stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 1,600 a month (“azure machine learning”)
- AI search demand for its name, est.
- 45 a month
- Its own site
- It sits on Azure's site (azure.cn), so there are no site figures for it alone
In answers
This editionShare of first choices
21%
rank 1 of 107 in ML platforms
First choices
20
across its categories
- Named in
- 159 answers
- Its own site cited
- in 143 of the answers that named it
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as Azure Machine Learning
What the judge wrote, as written, with how often. The vendor table decides that these count as Azure Machine Learning; a claim can dispute any of them.
Microsoft Azure Machine Learning 11Azure ML 7Microsoft Azure ML 4Azure ML Studio 1Azure Machine Learning (Azure ML) 1Azure Machine Learning (Microsoft Azure) 1Microsoft Azure Machine Learning (with Microsoft Fabric) 1Microsoft Azure Machine Learning / Azure AI 1Microsoft Purview / Azure Machine Learning 1
Follow Azure Machine Learning
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
The company
Azure is the company behind Azure Machine Learning.ShowHide
The company
Azure is the company behind Azure Machine Learning.
ShowHideOn record
Company records- Also from Azure
- Azure ACR, Azure AD Conditional Access, Azure AD Graph API, Azure AI Document Intelligence, Azure AI Foundry, Azure AI Gateway, Azure AI Search, Azure API Management and 79 more
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 Azure Machine Learning'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 Azure Machine Learning, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Azure Machine Learning by email, built from the raw record of the edition. It shows:
- where Azure Machine Learning is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Azure Machine Learning, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.
Badges
Wins only: a badge exists where Azure Machine Learning is ranked in a category's top three this edition. Three sizes, one file each, light and dark; the code is under each.ShowHide
Badges
Wins only: a badge exists where Azure Machine Learning is ranked in a category's top three this edition. Three sizes, one file each, light and dark; the code is under each.
ShowHideAI models recommend Azure Machine Learning first in ML platforms this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Wide · 320 × 104 Website, beside other badges
Square · 240 × 240 LinkedIn, slides
Compact · 200 × 56 Email signature, footer
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.