Index › Products › Faker · October 2026 Edition

1 category · Named, not ranked

Faker

11Judge labels

0First choices

3Negative labels

5 / 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

3 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Faker was named 3 times in Data masking, where DATPROF led with 17%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business30 of 152

under the label threshold

Mid-market96 of 140

under the label threshold

Enterprisenot named

No model named it for this buyer.

In data masking · 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 Faker 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%	96 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 Faker 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 Faker across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.

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Model	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	0	0	0
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	1	0	0	1
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	0	0	0	0
Mistral Small	0	0	0	0
DeepSeek V4 Flash	0	0	1	1
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	0	0	0	0
GPT-6 Luna	0	0	0	0
Muse Glimmer 30B	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

Direct2 labelsNone

Paraphrase1 labelNone

Comparative2 labelsNone

Budget-constrained3 labelsNone

Scale-constrained0 labelsNone

Negative3 labelsNone

First choiceAlternativeMentionNegative11 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.

“If you have in-house Python developers and want maximum flexibility for $0, write a lightweight pipeline using Pandas + Faker.” Gemini 3.5 Flash · Data masking · 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.

“Examples: RedactingLib, Datafaker, Faker (for masking), and various GitHub utilities.” DeepSeek V4 Flash · Data masking · negative prompt · soft negative

Named alongside

The products named in the same answers as Faker, over the 11 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Faker was named but was not.

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ProductSame answerTook the first choice insteadHead to head

Delphix8 of 110Not among the top eight

Informatica Intelligent Data Management Cloud5 of 110Not among the top eight

Greenmask4 of 113Not among the top eight

Neosync4 of 113Not among the top eight

Tonic.ai4 of 111Not among the top eight

Oracle Data Masking and Subsetting4 of 110Not among the top eight

PostgreSQL Anonymizer4 of 110Not among the top eight

K2View Test Data Management3 of 110Not among the top eight

IRI FieldShield2 of 112Not among the top eight

IBM InfoSphere Optim2 of 110Not 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 Faker. 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 8 of the 11 answers that named Faker and are not a share of its labels.

Domains cited

ovaledge.com8

getautonoma.com6

k2view.com6

gigantics.io5

iri.com4

atlan.com3

datprof.com3

github.com3

kanerika.com3

securityboulevard.com3

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

Pages cited

ovaledge.com/blog/data-masking-tools8

getautonoma.com/blog/data-masking-tools6

gigantics.io/en/blog/data-masking-tools-comparison5

iri.com/support/data-education-center/the-top-15-data-masking-tools-in-20264

k2view.com/blog/open-source-data-masking-tools4

atlan.com/data-masking-tools3

datprof.com/solutions/best-data-masking-tools3

getautonoma.com/blog/data-anonymization3

Pages are listed as the models cited them.

Search and answers

Where Faker 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 (“faker”)
AI search demand for its name, est.: 9,169 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 96 of 140 in data masking

Segment leader

17%

DATPROF

First choices: 0 across its categories
Named in: 11 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.

Names read as Faker

What the judge wrote, as written, with how often. The vendor table decides that these count as Faker; a claim can dispute any of them.

Faker (Python / JS) 1

Follow Faker

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

where Faker 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 Faker, 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.