Overview Leaderboard Methodology
Industry Report

NoSQL Databases

Non-relational databases designed for flexible schemas and horizontal scalability, including key-value, document, and wide-column stores.

Brands tracked: 32
Brands analyzed: 32
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 257
Total responses: 1,295
Top Brand Overall?
MongoDB
96/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
PostgreSQL
100

Highest score on the LBA metric in this industry.

Authority Leader?
MongoDB
100

Highest score on the Authority metric in this industry.

TOM Leader?
Amazon
97

Highest score on the TOM metric in this industry.

MongoDB is the default answer in AI responses for NoSQL Databases

MongoDB and Amazon together dominate AI responses for NoSQL Databases. 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 32 NoSQL 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
MongoDB
mongodb.com
 94
100
94
96
2
Amazon
amazon.com
 88
90
97
92
3
Amazon DynamoDB
amazonaws.com
 90
65
75
76
4
Redis
redis.io
 87
43
72
65
5
Apache
apache.org
 80
36
52
53
6
Couchbase
couchbase.com
 78
19
62
46
7
Microsoft
microsoft.com
 90
17
48
43
8
Google
google.com
 84
13
43
37
9
Neo4j
neo4j.com
 88
9
27
30
10
Redis Enterprise
redis.com
 93
9
22
29
11
DataStax
datastax.com
 84
8
24
28
12
PostgreSQL
postgresql.org
 100
18
10
28
13
Elastic
elastic.co
 87
9
19
26
14
Aerospike
aerospike.com
 75
7
13
21
15
OpenSearch
opensearch.org
 64
6
16
21
16
Riak
riak.com
 66
7
14
20
17
ScyllaDB
scylladb.com
 83
8
8
20
18
MarkLogic
marklogic.com
 79
8
8
19
19
RavenDB
ravendb.net
 81
8
8
19
20
ArangoDB
arangodb.com
 72
7
7
18
21
F
FoundationDB
foundationdb.org
 74
7
7
18
22
KeyDB
keydb.dev
 75
8
8
18
23
Oracle
oracle.com
 72
7
7
18
24
Fauna
fauna.com
 71
7
7
17
25
OrientDB
orientdb.org
 71
7
7
17
26
Realm
realm.io
 69
7
7
17
27
Valkey
valkey.io
 69
7
7
17
28
RethinkDB
rethinkdb.com
 64
6
6
16
29
FerretDB
ferretdb.com
 61
6
6
15
30
Tarantool
tarantool.io
 58
6
6
14
31
SequoiaDB
sequoiadb.com
 47
5
5
12
32
Dragonfly
dragonflydb.io
 36
4
4
10

How is this calculated? Methodology

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