Small business=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 37 → 14 of 61
Azure Data Factory
64Judge labels
0First choices
10Negative 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
0% in ETL for mid-market buyers
Rank 12 of 75 in the mid-market standingaccepted challenger
0 of 14 models made it the first choice on the direct prompt; 9% of its 23 labels there were negative.
What the models named instead of Azure Data Factory →By buyer segmentRead the same way at every buyer size.
Small business15 of 62
0%2 labels
under the label threshold
Mid-market12 of 75
0%23 labels
accepted challenger
Enterprise13 of 72
0%27 labels
accepted challenger
In etl · 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 Data Factory 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 |
|---|---|---|---|---|---|---|---|
| ETL and ELT | Data platform | 0% | 12 of 75 | 9% | 23 | accepted challenger | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Workflow orchestration | Data platform | 0% | 25 of 79 | 0% | 3 | under 10 labels · led by Prefect at 21% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
Movement
Since September 2026, Azure Data Factory held within the floor in all two categories: no change cleared 11 points.
Best category · ETL · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 13 → 12 of 69
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 10 → 11 of 64
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| ETL | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 13 → 12 of 75 |
| Orchestration | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 28 → 25 of 79 |
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 Data Factory 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 Data Factory 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 | 1 | 1 | 0 | 2 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 2 | 1 | 0 | 3 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 3 | 0 | 3 |
| Mistral Small | 0 | 0 | 2 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 2 | 0 | 2 |
| Llama 4 Maverick | 0 | 0 | 1 | 0 | 1 |
| Qwen 3.7 Flash | 0 | 0 | 2 | 0 | 2 |
| Kimi K2 | 0 | 1 | 2 | 0 | 3 |
| GLM 4.7 FlashX | 0 | 0 | 2 | 1 | 3 |
| MiniMax M2.5 | 0 | 1 | 2 | 0 | 3 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 1 | 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
Direct9 labelsNone
Paraphrase10 labelsNone
Comparative21 labelsNone
Budget-constrained6 labelsNone
Scale-constrained7 labelsNone
Negative11 labelsNone
First choiceAlternativeMentionNegative64 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.
“Cloud-native tool (AWS Glue/Azure Data Factory) if you're heavily invested in one cloud” Kimi K2 · ETL · scale prompt · alternative
“On Azure and prefer visual pipelines | Azure Data Factory (free tier)” MiniMax M2.5 · ETL · budget prompt · alternative
“With serverless cloud ETL, you don't pay flat monthly subscription fees.” Gemini 3.5 Flash · ETL · budget prompt · alternative
“Affordable entry point for Microsoft ecosystem users” Claude Haiku 4.5 · ETL · 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.
“If you're still on Informatica, Talend, or Azure Data Factory, you know the pain: rising license renewals, per-connector fees” Muse Glimmer 30B · ETL · negative prompt · hard negative
“May not handle complex transformations well” GLM 4.7 FlashX · ETL · negative prompt · hard negative
Named alongside
The products named in the same answers as Azure Data Factory, over the 64 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Azure Data Factory was named but was not.ShowHide
Named alongside
The products named in the same answers as Azure Data Factory, over the 64 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Azure Data Factory 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 Data Factory. 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 53 of the 64 answers that named Azure Data Factory and are not a share of its labels.
Domains cited
integrate.io29
fivetran.com24
airbyte.com23
estuary.dev20
hevodata.com16
kestra.io16
domo.com14
dataquest.io13
zoho.com13
blog.coupler.io12
No domain is on file for Azure Data Factory, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Azure Data Factory stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Azure Data Factory stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 22,200 a month (“azure data factory”)
- AI search demand for its name, est.
- 311 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
0%
rank 12 of 75 in ETL
Segment leader
40%
Fivetran
- First choices
- 0 across its categories
- Named in
- 64 answers
- Its own site cited
- No site on file to match
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 Data Factory
What the judge wrote, as written, with how often. The vendor table decides that these count as Azure Data Factory; a claim can dispute any of them.
Azure Data Factory (ADF) 4Azure Data Factory / Fabric Data Factory 1
Follow Azure Data Factory
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 Data Factory.ShowHide
The company
Azure is the company behind Azure Data Factory.
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 Data Factory'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 Data Factory, 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 Data Factory by email, built from the raw record of the edition. It shows:
- where Azure Data Factory 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 Data Factory, 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.