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 in this industry.

Authority Leader?
Snowflake
100

Highest score on the Authority metric in this industry.

TOM Leader?
Snowflake
100

Highest score on the TOM metric in this industry.

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

Snowflake and Google Cloud 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 (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
Snowflake
snowflake.com
 92
100
100
97
2
Google Cloud
cloud.google.com
 91
100
97
96
3
Google
google.com
 97
74
95
88
4
Amazon Web Services
aws.amazon.com
 98
76
88
87
5
Amazon
amazon.com
 88
77
88
84
6
Microsoft
microsoft.com
 93
32
72
60
7
Databricks
databricks.com
 86
31
69
58
8
Teradata
teradata.com
 83
8
18
25
9
Oracle
oracle.com
 78
8
19
24
10
Tableau
tableau.com
 85
9
11
22
11
Azure DevOps
azure.com
 86
9
9
21
12
dbt
dbt.com
 88
9
9
21
13
Looker
looker.com
 84
8
10
21
14
IBM
ibm.com
 83
8
8
20
15
ClickHouse
clickhouse.com
 77
8
8
19
16
Dremio
dremio.com
 75
8
8
18
17
SAP
sap.com
 75
8
8
18
18
SingleStore
singlestore.com
 74
7
7
18
19
Vertica
vertica.com
 72
7
7
18
20
MariaDB
mariadb.com
 69
7
7
17
21
Rockset
rockset.com
 68
7
7
17
22
StarRocks
starrocks.io
 69
7
7
17
23
Alibaba Cloud
alibabacloud.com
 64
6
6
16
24
CelerData
celerdata.com
 65
6
6
16
25
Firebolt
firebolt.io
 67
7
7
16
26
Exasol
exasol.com
 59
6
6
15
27
MotherDuck
motherduck.com
 61
6
6
15
28
Yellowbrick
yellowbrick.com
 60
6
6
15
29
Cloudera Data Warehouse
cloudera.com
 56
6
6
14
30
CrateDB
crate.io
 58
6
6
14
31
Kinetica
kinetica.com
 54
5
5
14
32
ByteHouse
bytehouse.cloud
 47
5
5
12
33
Aqua Data Studio
aquafold.com
 43
4
4
11
34
SelectDB
selectdb.com
 34
3
3
9
35
Ingres
actian.com
 12
1
1
4

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
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 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 →