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.

Authority Leader?
InfluxDB
92

Highest score on the Authority metric.

TOM Leader?
InfluxDB
96

Highest score on the TOM metric.

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

When users ask ChatGPT, Claude, or Gemini about Time-Series Databases, InfluxDB is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. ClickHouse is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 34 Time-Series Databases 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
InfluxDB
influxdata.com
 89
92
96
92
2
ClickHouse
clickhouse.com
 82
41
60
59
3
Prometheus
prometheus.io
 79
36
60
56
4
VictoriaMetrics
victoriametrics.com
 76
32
58
52
5
QuestDB
questdb.io
 70
18
49
39
6
Grafana
grafana.com
 86
12
37
33
7
Apache
apache.org
 73
14
30
31
8
Amazon
amazon.com
 85
3
21
17
9
Thanos
thanos.io
 37
3
12
11
10
Timescale
timescale.com
 82
11
1
10
11
PostgreSQL
postgresql.org
 100
1
9
9
12
O
OpenTSDB
opentsdb.net
 59
1
5
7
13
Graphite
graphite.dev
 78
0
5
2
14
Google
google.com
 66
0
5
1
15
TDengine
tdengine.com
 75
0
2
1
16
Akumuli
akumuli.org
 13
0
0
0
17
B
BTrDB
btrdb.io
 32
0
0
0
18
CrateDB
crate.io
 71
0
0
0
19
DolphinDB
dolphindb.com
 63
0
0
0
20
eXtremeDB
xtremedb.com
 47
0
0
0
21
GridDB
griddb.net
 60
0
0
0
22
I
IginX
iginx.org
 9
0
0
0
23
KairosDB
kairosdb.org
 52
0
0
0
24
kdb+
kx.com
 80
0
0
0
25
M3
m3as.com
 64
0
0
0
26
Machbase
machbase.com
 40
0
0
0
27
Microsoft
microsoft.com
 93
0
0
0
28
ModelarDB
modelardb.com
 4
0
0
0
29
QuasarDB
quasardb.net
 33
0
0
0
30
RRDtool
rrdtool.org
 66
0
0
0
31
SiriDB
siridb.com
 33
0
0
0
32
Timeplus
timeplus.com
 47
0
0
0
33
Tinybird
tinybird.co
 65
0
0
0
34
Warp 10
warp10.io
 32
0
0
0

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
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 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 →