Overview Leaderboard Methodology
Industry Report

Application Performance Monitoring

Platforms that collect, analyze, and visualize application metrics, traces, and errors to help engineers detect and resolve performance issues.

Brands tracked: 34
Brands analyzed: 34
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 269
Total responses: 1,365
Top Brand Overall?
Datadog
96/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Dynatrace
92

Highest score on the LBA metric in this industry.

Authority Leader?
Datadog
100

Highest score on the Authority metric in this industry.

TOM Leader?
Datadog
100

Highest score on the TOM metric in this industry.

Datadog is the default answer in AI responses for Application Performance Monitoring

When users ask ChatGPT, Claude, or Gemini about Application Performance Monitoring, Datadog is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. New Relic 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 Application Performance Monitoring 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
Datadog
datadoghq.com
 89
100
100
96
2
New Relic
newrelic.com
 79
78
92
83
3
Dynatrace
dynatrace.com
 92
59
95
80
4
Grafana
grafana.com
 80
44
74
64
5
Elastic
elastic.co
 86
33
77
60
6
Cisco
cisco.com
 79
15
66
44
7
Honeycomb
honeycomb.io
 81
18
54
44
8
AppDynamics
appdynamics.com
 76
15
66
43
9
Sentry
sentry.io
 84
13
40
36
10
Splunk
splunk.com
 70
9
53
35
11
OpenTelemetry
opentelemetry.io
 82
28
9
29
12
Prometheus
prometheus.io
 76
8
25
26
13
Jaeger
jaegertracing.io
 86
9
9
21
14
Instana
instana.com
 77
8
8
19
15
Coralogix
coralogix.com
 73
7
7
18
16
Site24x7
site24x7.com
 76
8
8
18
17
Lumigo
lumigo.io
 68
7
7
17
18
Scout APM
scoutapm.com
 72
7
7
17
19
AppSignal
appsignal.com
 66
7
7
16
20
Raygun
raygun.com
 66
7
7
16
21
SigNoz
signoz.io
 66
7
7
16
22
Chronosphere
chronosphere.io
 62
6
6
15
23
eG Innovations
eginnovations.com
 61
6
6
15
24
LogicMonitor
logicmonitor.com
 58
6
6
15
25
Sematext
sematext.com
 59
6
6
15
26
Stackify
stackify.com
 60
6
6
15
27
groundcover
groundcover.com
 52
5
5
13
28
ManageEngine ServiceDesk Plus
manageengine.com
 50
5
5
13
29
HyperDX
hyperdx.io
 45
5
5
12
30
Atatus
atatus.com
 40
4
4
10
31
Observe
observeinc.com
 36
4
4
9
32
SolarWinds Web Help Desk
solarwinds.com
 36
4
4
9
33
Middleware
middleware.io
 31
3
3
8
34
Blue Matador
bluematador.com
 25
3
3
7

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 269 shared Application Performance Monitoring 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 269 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 →