Overview Leaderboard Methodology
Industry Report

Cloud Data Warehouses

Centralized, fully managed data storage and query platforms designed for analytical workloads and scalable SQL-based reporting in the cloud.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 275
Total responses: 1,400
Top Brand Overall?
Snowflake
97/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Amazon Web Services
98

Highest score on the LBA metric.

Authority Leader?
Snowflake
100

Highest score on the Authority metric.

TOM Leader?
Snowflake
100

Highest score on the TOM metric.

Snowflake is the default answer in AI responses for Cloud Data Warehouses

Snowflake and Google together dominate AI responses for Cloud Data Warehouses. 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 35 Cloud Data Warehouses 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
Snowflake
snowflake.com
 92
100
100
97
2
Google
google.com
 97
74
95
88
3
Amazon
amazon.com
 88
63
87
79
4
Databricks
databricks.com
 86
31
69
57
5
Microsoft
microsoft.com
 93
22
63
50
6
ClickHouse
clickhouse.com
 77
4
4
11
7
Looker
looker.com
 84
0
10
4
8
Google Cloud
cloud.google.com
 91
0
10
2
9
Oracle
oracle.com
 78
0
19
2
10
Tableau
tableau.com
 85
0
11
2
11
Teradata
teradata.com
 83
0
18
2
12
dbt
dbt.com
 88
0
3
1
13
Firebolt
firebolt.io
 67
0
5
1
14
IBM
ibm.com
 83
0
2
1
15
SingleStore
singlestore.com
 74
0
1
1
16
Alibaba Cloud
alibabacloud.com
 64
0
0
0
17
Amazon Web Services
aws.amazon.com
 98
0
0
0
18
Aqua Data Studio
aquafold.com
 43
0
0
0
19
Azure DevOps
azure.com
 86
0
0
0
20
ByteHouse
bytehouse.cloud
 47
0
0
0
21
CelerData
celerdata.com
 65
0
0
0
22
Cloudera Data Warehouse
cloudera.com
 56
0
0
0
23
CrateDB
crate.io
 58
0
0
0
24
Dremio
dremio.com
 75
0
0
0
25
Exasol
exasol.com
 59
0
0
0
26
Ingres
actian.com
 12
0
0
0
27
Kinetica
kinetica.com
 54
0
0
0
28
MariaDB
mariadb.com
 69
0
0
0
29
MotherDuck
motherduck.com
 61
0
0
0
30
Rockset
rockset.com
 68
0
0
0
31
SAP
sap.com
 75
0
0
0
32
SelectDB
selectdb.com
 34
0
0
0
33
StarRocks
starrocks.io
 69
0
0
0
34
Vertica
vertica.com
 72
0
0
0
35
Yellowbrick
yellowbrick.com
 60
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 275 shared Cloud Data Warehouses 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 275 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 →