Overview Leaderboard Methodology
Industry Report

Analytical Query Engines

High-performance distributed query engines and SQL-on-Hadoop systems that enable fast analytics across large, heterogeneous datasets.

Brands tracked: 26
Brands analyzed: 26
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 221
Total responses: 1,085
Top Brand Overall?
Trino
80/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Databricks
92

Highest score on the LBA metric in this industry.

Authority Leader?
Trino
69

Highest score on the Authority metric in this industry.

TOM Leader?
Trino
91

Highest score on the TOM metric in this industry.

Trino leads the Analytical Query Engines category in AI responses

Trino is the most consistently-surfaced brand when users ask AI assistants about Analytical Query Engines, but not to the point of monopolising the conversation. ClickHouse and a handful of others appear regularly, giving users a reasonable set of alternatives to compare. The top of this leaderboard is where most of the AI-driven traffic will go.

Brand Leaderboard All 26 Analytical Query Engines 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
Trino
trino.io
 82
69
91
80
2
ClickHouse
clickhouse.com
 86
64
81
76
3
Snowflake
snowflake.com
 85
58
83
74
4
Apache
apache.org
 83
43
66
62
5
Databricks
databricks.com
 92
28
63
55
6
DuckDB
duckdb.org
 87
34
41
50
7
Starburst
starburst.io
 78
17
28
35
8
Dremio
dremio.com
 81
13
28
32
9
StarRocks
starrocks.io
 78
8
24
26
10
Presto
prestodb.io
 77
8
8
19
11
SingleStore
singlestore.com
 80
8
8
19
12
CrateDB
crate.io
 74
7
7
18
13
Firebolt
firebolt.io
 75
7
7
18
14
MotherDuck
motherduck.com
 75
8
8
18
15
Rockset
rockset.com
 75
7
7
18
16
Exasol
exasol.com
 66
7
7
16
17
H
Heavy.AI
heavy.ai
 63
6
6
16
18
Kinetica
kinetica.com
 67
7
7
16
19
RisingWave
risingwave.com
 67
7
7
16
20
Yellowbrick
yellowbrick.com
 60
6
6
15
21
Databend
databend.com
 53
5
5
13
22
Kyligence
kyligence.io
 50
5
5
13
23
Ingres
actian.com
 36
4
4
10
24
SelectDB
selectdb.com
 39
4
4
10
25
Ocient
ocient.com
 36
4
4
9
26
B
Brytlyt
brytlyt.com
 24
2
2
7

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 221 shared Analytical Query Engines 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 221 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 →