GTM AI Index
Index Vendors › Matillion · September 2026 Edition
2 categories · Named, not ranked

Matillion

20Judge labels
0First choices
3Negative labels
10 of 12Models named it
2Categories
September 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-09-16.3, every buyer segment counted.
Standing
9 labels, too few to rank
A product needs 10 labels in a category before a share or quadrant is stated. Matillion was named 9 times in DMP and 1 other category, where Segment led with 18%. The labels and the evidence are below, counted exactly.
By buyer segmentRead the same way at every buyer size.
In dmp · 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 Matillion 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
Data management platformsGTM data and infrastructure0%37 of 1830%8under 10 labels · led by Segment at 18%
Data warehouse and reverse ETL for marketingGTM data and infrastructure0%61 of 1000%1under 10 labels · led by Hightouch at 42%

Movement

This is the first edition on this tier, so no move can be computed for Matillion yet. The next is due October 1, 2026. 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 Matillion across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.
ModelFirst choiceAlternativeMentionNegativeLabels
Claude Haiku 4.500000
GPT-5.4 mini00101
Gemini 3.5 Flash00000
Perplexity Sonar01001
Grok 4.1 Fast00000
Mistral Small00101
DeepSeek V4 Flash01001
Llama 4 Maverick00101
Qwen 3.7 Flash00101
Kimi K200101
GLM 4.7 FlashX00101
MiniMax M2.500101

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
Direct1 labelNone
Paraphrase1 labelNone
Comparative12 labelsNone
Budget-constrained0 labelsNone
Scale-constrained0 labelsNone
Negative6 labelsNone
First choiceAlternativeMentionNegative20 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, fast deployment (enhanced with Maia AI agents); not storage/analytics” DeepSeek V4 Flash · DMP · comparative prompt · alternative
“Managed ingestion: Fivetran or Matillion.” Perplexity Sonar · DMP · 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 Matillion, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Matillion was named but was not.
ProductSame answerTook the first choice insteadHead to head
Fivetran14 of 200Not in the top three
Snowflake13 of 206Not in the top three
Databricks13 of 202Not in the top three
BigQuery12 of 202Not in the top three
Collibra9 of 201Not in the top three
dbt9 of 200Not in the top three
Alation7 of 200Not in the top three
Informatica IDMC7 of 200Not in the top three
Microsoft Fabric7 of 200Not in the top three
Microsoft Purview7 of 200Not 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 Matillion. 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 2 of the 20 answers that named Matillion and are not a share of its labels.

Domains cited

blog.hubspot.com2
domo.com2
fortegrp.com2
gitnux.org2
improvado.io2
matillion.comYour site2
solutionsreview.com2
techdogs.com2
cio.com1
clickup.com1

Sixteen of the eighteen domain citations in answers naming Matillion came from somebody else's page.

Pages cited

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 Matillion'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 Matillion, 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 matillion.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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