Every category where a model named Fogger 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
Data masking and test data management	Data platform	0%	114 of 140	33%	3	under 10 labels · led by DATPROF at 17%

Movement

This is the first edition on this tier, so no move can be computed for Fogger yet. From the next edition this section shows, per buyer segment and per category, whether its share moved by more than the measured noise floor.

By model

How each model treated Fogger across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	Mention	Negative	Labels
Claude Haiku 4.5	1	0	1
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	1	0	1
Llama 4 Maverick	0	0	0
Qwen 3.7 Flash	0	1	1
Kimi K2	0	0	0
GLM 4.7 FlashX	0	0	0
MiniMax M2.5	0	0	0
GPT-6 Luna	0	0	0
Muse Glimmer 30B	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

Comparative1 labelNone

Budget-constrained5 labels1

Scale-constrained0 labelsNone

Negative10 labels1not counted in share

First choiceAlternativeMentionNegative16 labels in all, every segment counted; 1 of the 2 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.

No positive label carried a quote.

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.

“While tools like Fogger, Masked-AI, or basic repository scripts are excellent for personal projects or internal testing, they are often risky for Highly Regulated Enterprises” Qwen 3.7 Flash · Data masking · negative prompt · soft negative

Named alongside

The products named in the same answers as Fogger, over the 16 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Fogger was named but was not.

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ProductSame answerTook the first choice insteadHead to head

Masked-AI13 of 160Not among the top eight

Delphix7 of 160Not among the top eight

Oracle Data Masking and Subsetting6 of 160Not among the top eight

DATPROF5 of 162Not among the top eight

Percona Server5 of 160Not among the top eight

Greenmask4 of 163Not among the top eight

IRI FieldShield4 of 162Not among the top eight

Tonic.ai4 of 161Not among the top eight

IBM InfoSphere Optim4 of 160Not among the top eight

K2View Test Data Management3 of 161Not among the top eight

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 Fogger. 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 16 of the 16 answers that named Fogger and are not a share of its labels.

Domains cited

k2view.com12

getautonoma.com9

hevodata.com9

softwaretestinghelp.com9

g2.com8

sourceforge.net8

checkthat.ai7

devopsschool.com7

us.fitgap.com7

fivetran.com6

No domain is on file for Fogger, so its own site is not marked.

Pages cited

k2view.com/blog/open-source-data-masking-tools12

getautonoma.com/blog/data-masking-tools9

hevodata.com/learn/data-masking-tools9

softwaretestinghelp.com/data-masking-tools9

checkthat.ai/answers/what-are-the-best-data-masking-tools7

devopsschool.com/blog/top-10-data-masking-tools-in-2025-features-pros-cons-comparison7

fivetran.com/learn/data-masking-software6

ovaledge.com/blog/data-masking-tools6

Pages are listed as the models cited them.

Search and answers

Where Fogger stands in Google search beside where it stands in the models' answers.

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In search

Google, US estimates

Searches for its name, Google: 49,500 a month (“fogger”)
AI search demand for its name, est.: 8,649 a month
Its own site: No site of its own on file, so no site figures

In answers

This edition

Share of first choices

rank 114 of 140 in data masking

Segment leader

17%

DATPROF

First choices: 1 across its categories
Named in: 16 answers
Its own site cited: No site on file to match

Search figures are US estimates from DataForSEO, read October 5, 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.

Follow Fogger

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.

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 Fogger'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 Fogger, 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 Fogger by email, built from the raw record of the edition. It shows:

where Fogger 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 Fogger, 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.