Overview Leaderboard Methodology
Industry Report

Enterprise Source Code Management Platforms

Self-hosted or managed platforms offering Git repository hosting, fine-grained access controls, compliance features, and large-team workflow support for enterprises.

Brands tracked: 33
Brands analyzed: 33
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 263
Total responses: 1,330
Top Brand Overall?
GitHub
99/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
GitHub
97

Highest score on the LBA metric.

Authority Leader?
GitHub
100

Highest score on the Authority metric.

TOM Leader?
GitHub
100

Highest score on the TOM metric.

GitHub is the default answer in AI responses for Enterprise Source Code Management Platforms

GitHub and GitLab together dominate AI responses for Enterprise Source Code Management Platforms. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 33 Enterprise Source Code Management Platforms brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
GitHub
github.com
 97
100
100
99
2
GitLab
gitlab.com
 88
100
99
95
3
Bitbucket
bitbucket.org
 76
69
88
77
4
Atlassian
atlassian.com
 79
12
57
38
5
Gitea
gitea.com
 71
9
16
22
6
Forgejo
forgejo.org
 57
5
9
14
7
Microsoft
microsoft.com
 92
1
4
6
8
Gerrit
gerritcodereview.com
 67
0
13
2
9
Apache
apache.org
 32
0
1
1
10
Helix TeamHub
perforce.com
 28
0
2
1
11
RhodeCode
rhodecode.com
 59
0
2
1
12
SourceHut
sr.ht
 52
0
2
1
13
Amazon
amazon.com
 74
0
0
0
14
Assembla
assembla.com
 55
0
0
0
15
Beanstalk
beanstalkapp.com
 60
0
0
0
16
B
Bonobo Git Server
bonobogitserver.com
 33
0
0
0
17
Codebase
codebasehq.com
 21
0
0
0
18
Codeberg
codeberg.org
 50
0
0
0
19
Coding.net
coding.net
 37
0
0
0
20
Gitblit
gitblit.com
 39
0
0
0
21
GitCode
gitcode.com
 16
0
0
0
22
Gitee
gitee.com
 58
0
0
0
23
GitFlic
gitflic.ru
 25
0
0
0
24
GitVerse
gitverse.ru
 14
0
0
0
25
Gogs
gogs.io
 58
0
0
0
26
Kallithea
kallithea-scm.org
 48
0
0
0
27
Launchpad
launchpad.net
 10
0
0
0
28
OneDev
onedev.io
 40
0
0
0
29
Pagure
pagure.io
 46
0
0
0
30
P
Phabricator
phacility.com
 59
0
0
0
31
Radicle
radicle.xyz
 14
0
0
0
32
SCM-Manager
scm-manager.org
 50
0
0
0
33
SourceForge
sourceforge.net
 54
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 263 shared Enterprise Source Code Management Platforms prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 263 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →