Overview Leaderboard Methodology
Industry Report

Time-Series Databases

Databases optimized for ingesting, storing, and querying time-stamped data for monitoring, metrics, and IoT use cases.

Brands tracked: 34
Brands analyzed: 34
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 264
Total responses: 1,355
Top Brand Overall?
InfluxDB
92/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
PostgreSQL
100

Highest score on the LBA metric in this industry.

Authority Leader?
InfluxDB
92

Highest score on the Authority metric in this industry.

TOM Leader?
InfluxDB
96

Highest score on the TOM metric in this industry.

InfluxDB is the default answer in AI responses for Time-Series Databases

InfluxDB and Timescale together dominate AI responses for Time-Series 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 34 Time-Series 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
InfluxDB
influxdata.com
 89
92
96
92
2
Timescale
timescale.com
 82
81
92
85
3
ClickHouse
clickhouse.com
 82
41
60
59
4
Prometheus
prometheus.io
 79
36
60
56
5
VictoriaMetrics
victoriametrics.com
 76
32
58
53
6
QuestDB
questdb.io
 70
18
49
40
7
Grafana
grafana.com
 86
14
38
37
8
Apache
apache.org
 73
14
30
32
9
Amazon
amazon.com
 85
9
28
29
10
PostgreSQL
postgresql.org
 100
10
10
24
11
Microsoft
microsoft.com
 93
9
11
23
12
Graphite
graphite.dev
 78
8
8
19
13
kdb+
kx.com
 80
8
8
19
14
TDengine
tdengine.com
 75
7
7
18
15
CrateDB
crate.io
 71
7
7
17
16
DolphinDB
dolphindb.com
 63
6
6
16
17
Google
google.com
 66
7
7
16
18
M3
m3as.com
 64
6
6
16
19
RRDtool
rrdtool.org
 66
7
7
16
20
Tinybird
tinybird.co
 65
6
6
16
21
GridDB
griddb.net
 60
6
6
15
22
O
OpenTSDB
opentsdb.net
 59
6
6
15
23
KairosDB
kairosdb.org
 52
5
5
13
24
Thanos
thanos.io
 37
4
12
13
25
eXtremeDB
xtremedb.com
 47
5
5
12
26
Timeplus
timeplus.com
 47
5
5
12
27
Machbase
machbase.com
 40
4
4
10
28
B
BTrDB
btrdb.io
 32
3
3
9
29
QuasarDB
quasardb.net
 33
3
3
9
30
SiriDB
siridb.com
 33
3
3
9
31
Warp 10
warp10.io
 32
3
3
9
32
Akumuli
akumuli.org
 13
1
1
4
33
I
IginX
iginx.org
 9
1
1
3
34
ModelarDB
modelardb.com
 4
0
0
1

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 264 shared Time-Series 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 264 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 →