Overview Leaderboard Methodology
Industry Report

Relational Databases

Traditional SQL-based database systems that store data in structured tables with ACID transactional guarantees and support complex queries via SQL.

Brands tracked: 32
Brands analyzed: 32
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 257
Total responses: 1,295
Top Brand Overall?
PostgreSQL
100/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
PostgreSQL
100

Highest score on the LBA metric.

Authority Leader?
PostgreSQL
100

Highest score on the Authority metric.

TOM Leader?
PostgreSQL
100

Highest score on the TOM metric.

PostgreSQL is the default answer in AI responses for Relational Databases

When users ask ChatGPT, Claude, or Gemini about Relational Databases, PostgreSQL is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. MySQL is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 32 Relational Databases 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
PostgreSQL
postgresql.org
 100
100
100
100
2
MySQL
mysql.com
 90
59
82
76
3
Oracle
oracle.com
 90
29
67
55
4
MariaDB
mariadb.com
 83
27
54
50
5
Microsoft
microsoft.com
 100
14
73
47
6
SQLite
sqlite.org
 98
17
48
43
7
Google
google.com
 81
19
49
42
8
Amazon
amazon.com
 82
13
52
38
9
Supabase
supabase.com
 81
5
26
22
10
CockroachDB
cockroachlabs.com
 78
5
12
16
11
Neon
neon.tech
 74
2
16
13
12
PlanetScale
planetscale.com
 87
0
16
5
13
IBM
ibm.com
 85
0
13
2
14
SAP
sap.com
 74
0
6
2
15
YugabyteDB
yugabyte.com
 79
0
6
2
16
Firebird
firebirdsql.org
 62
0
3
1
17
TiDB
pingcap.com
 81
0
3
1
18
Alibaba Cloud
alibabacloud.com
 65
0
0
0
19
Altibase
altibase.com
 60
0
0
0
20
CUBRID
cubrid.org
 51
0
0
0
21
D
Dameng
dameng.com
 52
0
0
0
22
H2
h2database.com
 28
0
0
0
23
HCL Connections
hcl-software.com
 35
0
0
0
24
HSQLDB
hsqldb.org
 82
0
0
0
25
Huawei Cloud
huaweicloud.com
 62
0
0
0
26
Ingres
actian.com
 50
0
0
0
27
InterSystems IRIS
intersystems.com
 74
0
0
0
28
KingbaseES
kingbase.com.cn
 56
0
0
0
29
O
OceanBase
oceanbase.com
 73
0
0
0
30
openGauss
opengauss.org
 61
0
0
0
31
SingleStore
singlestore.com
 77
0
0
0
32
Teradata
teradata.com
 79
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 257 shared Relational Databases 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 257 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 →