Overview Leaderboard Methodology
Industry Report

Error Monitoring Platforms

Services focused on capturing, grouping, and notifying on application exceptions and client-side errors with context for debugging and root-cause analysis.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-22
Model: OpenAI GPT-5
Prompts: 270
Total responses: 1,390
Top Brand Overall?
Sentry
97/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Sentry
91

Highest score on the LBA metric.

Authority Leader?
Sentry
100

Highest score on the Authority metric.

TOM Leader?
Sentry
100

Highest score on the TOM metric.

Sentry is the default answer in AI responses for Error Monitoring Platforms

When users ask ChatGPT, Claude, or Gemini about Error Monitoring Platforms, Sentry is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Datadog 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 35 Error Monitoring Platforms 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
Sentry
sentry.io
 91
100
100
97
2
Datadog
datadoghq.com
 86
72
95
84
3
Bugsnag
bugsnag.com
 79
71
88
79
4
Rollbar
rollbar.com
 76
57
84
71
5
New Relic
newrelic.com
 80
31
63
54
6
Honeybadger
honeybadger.io
 68
20
58
43
7
Airbrake
airbrake.io
 64
4
14
15
8
GlitchTip
glitchtip.com
 60
2
4
8
9
Raygun
raygun.com
 66
0
22
7
10
AppSignal
appsignal.com
 69
0
12
2
11
LogRocket
logrocket.com
 75
0
10
2
12
TrackJS
trackjs.com
 71
0
6
2
13
Honeycomb
honeycomb.io
 78
0
1
1
14
Instabug
instabug.com
 79
0
1
1
15
Atatus
atatus.com
 40
0
0
0
16
Backtrace
backtrace.io
 52
0
0
0
17
Bird Eats Bug
birdeatsbug.com
 53
0
0
0
18
Bugfender
bugfender.com
 49
0
0
0
19
Bugsink
bugsink.com
 9
0
0
0
20
C
CrashKiOS
crashkios.com
 0
0
0
0
21
E
ECS Logging
ecslogging.com
 11
0
0
0
22
Embrace
embrace.io
 40
0
0
0
23
E
Errbit
errbit.com
 46
0
0
0
24
Exceptionless
exceptionless.com
 49
0
0
0
25
Highlight
highlight.io
 44
0
0
0
26
Jam
jam.dev
 24
0
0
0
27
OpenReplay
openreplay.com
 50
0
0
0
28
OverOps
overops.com
 59
0
0
0
29
PagerDuty
pagerduty.com
 90
0
0
0
30
Qovery
qovery.com
 32
0
0
0
31
Scout APM
scoutapm.com
 70
0
0
0
32
Shake
shakebugs.com
 13
0
0
0
33
Slack
slack.com
 74
0
0
0
34
Stackify
stackify.com
 60
0
0
0
35
Zipy
zipy.ai
 31
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 270 shared Error Monitoring Platforms 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 270 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 →