[unCited]
ProductCitation IndexAI InfluenceBlogBook
[unCited]/Datadog
ProductCitation IndexAI InfluenceBlogBook
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AEO Score

74

Strong Presence

Avg Prompt Score

38

across 769 prompts

AI Share of Voice

38%

across 769 prompts

Category Visibility

#1

in Observability & APM · of 44

Critical Issues

2

critical + high

Per-stage performance

🔍Discovery
451 category
Cited65%291/451
Share of voice38%avg
Engine consensus58%of engines
Competitors34.9avg/cited
Sentimentpositive5+ / 1~ / 0−
⚖️Evaluation
11 brand-level
Cited100%11/11
Share of voice92%avg
Engine consensus92%of engines
Competitors2.7avg/cited
Sentimentpositive6+ / 6~ / 0−
🛡️Trust
7 brand-level
Cited100%7/7
Share of voice91%avg
Engine consensus90%of engines
Competitors2.3avg/cited
Sentimentpositive3+ / 3~ / 0−
💰Conversion
5 brand-level
Cited100%5/5
Share of voice87%avg
Engine consensus87%of engines
Competitors1.2avg/cited
Sentimentneutral1+ / 5~ / 0−

Cited rate · share of voice · engine consensus · sentiment, broken out by buyer-journey stage. Sentiment is the net positive−negative skew across engines that cited the brand at this stage.

Categories Datadog is visible in

4
  • Observability & APM1 of 44→
  • Developer Tools2 of 220→
  • Error Tracking2 of 29→
  • AI Observability3 of 46→

Executive summary

Datadog is highly likely to be cited by AI engines for evaluation-stage observability/monitoring queries because it has strong third-party review authority (e.g., G2 and TrustRadius) and clear, crawlable pricing content with public tier-like numbers. The single highest-ROI fix is to add/expand first-party, brand-led comparison pages (e.g., “Datadog vs Splunk/Dynatrace/New Relic”) and ensure they are SSR-crawlable, because that query pattern is where AI engines most often need a direct evaluation source.

Based on audit of datadog.com · Jun 2, 2026

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