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 in this industry.

Authority Leader?
PostgreSQL
100

Highest score on the Authority metric in this industry.

TOM Leader?
PostgreSQL
100

Highest score on the TOM metric in this industry.

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. Oracle 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 (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
PostgreSQL
postgresql.org
 100
100
100
100
2
Oracle
oracle.com
 90
76
88
84
3
MySQL
mysql.com
 90
59
82
76
4
Microsoft
microsoft.com
 100
30
82
63
5
Amazon
amazon.com
 82
31
69
57
6
MariaDB
mariadb.com
 83
27
54
50
7
SQLite
sqlite.org
 98
17
48
45
8
Google
google.com
 81
19
49
43
9
Supabase
supabase.com
 81
8
26
28
10
PlanetScale
planetscale.com
 87
9
16
25
11
IBM
ibm.com
 85
8
13
23
12
Neon
neon.tech
 74
7
16
22
13
CockroachDB
cockroachlabs.com
 78
8
12
21
14
HSQLDB
hsqldb.org
 82
8
8
20
15
TiDB
pingcap.com
 81
8
8
20
16
SingleStore
singlestore.com
 77
8
8
19
17
Teradata
teradata.com
 79
8
8
19
18
YugabyteDB
yugabyte.com
 79
8
8
19
19
InterSystems IRIS
intersystems.com
 74
7
7
18
20
O
OceanBase
oceanbase.com
 73
7
7
18
21
SAP
sap.com
 74
7
7
18
22
Alibaba Cloud
alibabacloud.com
 65
6
6
16
23
Altibase
altibase.com
 60
6
6
15
24
Firebird
firebirdsql.org
 62
6
6
15
25
Huawei Cloud
huaweicloud.com
 62
6
6
15
26
openGauss
opengauss.org
 61
6
6
15
27
KingbaseES
kingbase.com.cn
 56
6
6
14
28
CUBRID
cubrid.org
 51
5
5
13
29
D
Dameng
dameng.com
 52
5
5
13
30
Ingres
actian.com
 50
5
5
13
31
HCL Connections
hcl-software.com
 35
4
4
9
32
H2
h2database.com
 28
3
3
8

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
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
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 →