AEO Audit is a free tool that scores B2B SaaS websites for AI search visibility — how likely they are to be cited by ChatGPT, Perplexity, and Google AI Overviews when B2B buyers search.

What is AI Engine Optimization (AEO)?

AI Engine Optimization (AEO) is the practice of structuring content, entity signals, and technical infrastructure so that AI engines cite your brand in generated answers. It extends traditional SEO to cover ChatGPT, Perplexity, Gemini, and Google AI Overviews.

Yes. AEO Audit is free. Enter any B2B SaaS brand and receive a full AI visibility score with prioritised recommendations.

How does AEO Audit score websites?

AEO Audit scores across six pillars: AI Visibility (22%), Brand Authority (23%), Content Quality (18%), Platform Optimization (15%), Structured Data (12%), and Technical AEO (10%). Each pillar reflects specific signals that AI engines use when deciding which sources to cite.

Category · AI Citation Index

Developer Tools

Developer Tools is a fragmented category split across infrastructure layers — CI/CD, observability, error tracking, feature flags. Datadog is the consensus pick: visible on every engine, wins more head-to-head comparisons than it loses. GitLab captures the most AI attention in discovery prompts, surfacing in 82% of buyer queries across all four engines. Atlassian matches Datadog's head-to-head win rate and appears in 61% of discovery prompts, making it the third pole. The absent giants — Microsoft, Vercel, New Relic, Cloudflare — win head-to-heads but almost never surface in AI discovery prompts. This is a contested category: no single brand owns AI attention, and the engines scatter citations across two dozen tools.

273 discovery queries · 112 head-to-heads · refreshed May 1, 2026

Discovery stage

The shortlist

Across 273 buyer-style "Developer Tools" queries

Most cited · Developer ToolsLive

GitLab

gitlab.com

Cited in 223 of 273 buyer questions for Developer Tools — surfaced by 3 of 3 AI engines.

ChatGPTGeminiClaude

Citation rate

82%

Visibility score72/100

See the full GitLab profile→

GitLab shows up in 82% of discovery prompts about developer tools and surfaces across all four engines. Datadog lands in 71% of those same queries, visible on every engine. Atlassian appears in 61% of prompts, also across all four engines. Sentry surfaces in 55% of buyer queries on every engine, while Hashicorp captures 41% and Prometheus 37%, both with four-engine visibility. The top six all maintain ubiquitous presence; no brand here depends on a single engine.

Hover or click a logo to see brand details

X = coverage across discovery prompts · Y = engine diversity · Bubble size = total mentions

Tracked acrossChatGPT,Gemini,Claude

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Score shifts, new entrants, citation gaps — every Monday.

Signal by intent

By topic

Top 5 most-cited brands per intent cluster. Brands with zero citations in a topic are not shown.

Datadog

26/37

GitLab

23/37

Mulesoft

17/37

Comprehensive

17/37

Sentry

17/37

Readme

11/12

Base

11/12

Gitbook

10/12

Make

10/12

Internal

9/12

Default

9/9

Linear

8/9

GitLab

8/9

Base

7/9

Atlassian

7/9

Comprehensive

9/9

Make

7/9

GitLab

6/9

Default

6/9

POINT

6/9

Cursor

7/7

Supabase

7/7

Vercel

7/7

GitLab

7/7

Linear

7/7

Jira

7/7

monday dev

7/7

Linear

7/7

Jira Software

7/7

GitLab

7/7

Atlassian

6/6

Jira

6/6

Default

5/6

Bitbucket

5/6

Less

5/6

GitLab

6/6

Bitbucket

6/6

SonarQube

6/6

Snyk

6/6

Semgrep

6/6

≥50% cited

25–49%

<25%

Topics are discovery-stage prompt clusters · developer-tools

Evaluation stage

Head-to-head

How often AI cites each brand across uniform category evaluation prompts · median 53/100

When buyers ask AI to compare developer tools head-to-head, Vercel wins the most matchups across four comparison queries, though its discovery footprint is thin. Microsoft wins more head-to-heads than it loses across ten matchups but almost never surfaces in AI discovery prompts. Datadog scores 69 across six head-to-heads — above the category median of 53 — and is the only brand that both wins more than it loses and captures meaningful discovery share. Dynatrace appears in a dozen head-to-heads but loses more than it wins, scoring 58.

Hover or click a logo to see brand details

X = evaluation citation rate · Y = evaluation prompts cited in · Bubble size = citation exposure

Median citation rate 53/100

Each brand's score is the share of category evaluation prompts where AI cited them across all four engines — the same prompt pool for every brand. Brands above the median citation rate have stronger presence in evaluation-stage queries.

Brands to know

In this category

Datadog

Consensus pick

Cloud-scale observability and APM platform

Read brand profile →

GitLab

DevSecOps platform for CI/CD and source control

Read brand profile →

Atlassian

Jira and Confluence for software teams

Read brand profile →

Microsoft

Absent giant

Azure DevOps and GitHub enterprise tooling

Read brand profile →

Newrelic

Absent giant

Full-stack observability for production systems

Read brand profile →

Citation sources

Where AI pulls citations from

836 citations captured across Developer Tools prompt runs.

Vendor pages

190

Product, help, and marketing pages from tracked vendors

Independent sources

200

Reviews, encyclopedias, forums, press — not vendor-owned

Buyer questions

What AI cites for top Developer Tools questions

Most queries here are top-of-funnel exploration — buyers ask AI for the best developer tools by team size, vertical, or use case ('best version control solutions for enterprise companies', 'developer tools for cybersecurity firms'). A smaller slice digs into trust signals and compliance ('SOC 2 compliant developer software', 'highest-rated developer tools according to user reviews'). Pricing and free-tier questions make up the tail ('developer tools for startups on a budget', 'developer tools with free tiers').