Small businessnewNew since September 2026: not ranked then, 0% now.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 62 of 131, unchanged
Google AutoML
4Judge labels
0First choices
0Negative labels
4 / 14Models named it
1Category
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, 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. Google AutoML was named 2 times in ML platforms, where Azure Machine Learning led with 21%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
Small business47 of 139
0%2 labels
under the label threshold
Mid-market30 of 107
0%2 labels
under the label threshold
Enterprisenot named
0%
No model named it for this buyer.
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 Google AutoML 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 | 0% | 30 of 107 | 0% | 2 | under 10 labels · led by Azure Machine Learning at 21% | newNew since September 2026: not ranked then, 0% now. |
Movement
Since September 2026, Google AutoML held within the floor in its one category: no change cleared 11 points.
Best category · ML platforms · by segment
Mid-marketnewNew since September 2026: not ranked then, 0% now.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 30 of 97, unchanged
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ML platforms | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 30 of 107, 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 Google AutoML 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 Google AutoML 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 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.1 Fast | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 1 | 0 | 0 | 1 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 1 | 0 | 0 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 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
Direct0 labelsNone
Paraphrase0 labelsNone
Comparative0 labelsNone
Budget-constrained3 labelsNone
Scale-constrained1 labelNone
Negative0 labelsNone
First choiceAlternativeMentionNegative4 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.
“though they are more expensive than the open-source options” Mistral Small · ML platforms · budget prompt · alternative
“Google AutoML (minimal coding required)” Kimi K2 · 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.
No model argued against it.
Named alongside
The products named in the same answers as Google AutoML, over the 4 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google AutoML was named but was not.ShowHide
Named alongside
The products named in the same answers as Google AutoML, over the 4 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Google AutoML 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 Google AutoML. 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 4 of the 4 answers that named Google AutoML and are not a share of its labels.
Domains cited
appreviewlab.com2
byteplus.com2
deepusecase.com2
eweek.com2
spotsaas.com2
us.fitgap.com2
alphasoftware.com1
anaconda.com1
articsledge.com1
checkthat.ai1
Sixteen of the sixteen domain citations in answers naming Google AutoML came from somebody else's page.
Pages cited
Pages are listed as the models cited them.
Follow Google AutoML
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
Google Cloud is the company behind Google AutoML.ShowHide
The company
Google Cloud is the company behind Google AutoML.
ShowHideOn record
Company records- Also from Google Cloud
- Google 2-Step Verification, Google Alerts, Google AlloyDB for PostgreSQL, Google Analytics, Google Antigravity, Google Artifact Registry, Google BigQuery ML, Google BigQuery Omni and 84 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 Google AutoML'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 Google AutoML, 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 Google AutoML by email, built from the raw record of the edition. It shows:
- where Google AutoML 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 Google AutoML, 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.