DevTune · AI Search Intelligence

The State of AI Search for Dev Tools: 2026

We asked five AI search platforms thousands of tool-evaluation questions across 43 developer tool verticals and recorded every source they cited. This report shows which pages get cited, which brands appear in answers, and what to build if you want to improve your visibility.

Read the findings Open the Strategy LabPublished 2026-07-01 · frozen 90-day benchmark through 30 Jun 2026

Google AI ModeChatGPT SearchPerplexityGemini SearchxAI Search

developer-tool categories

478

tracked brands

1,075

test scenarios

54,747

answer observations

126,814

citation records

55,446

cited URLs

12,148

cited domains

Chapter 01 · Signal

What the data shows about AI search for developer tools

Six patterns in the data

34.3%
of citation records
Blog posts are the most-cited content type
Blog posts earn more citations than docs, product pages, and landing pages combined. The homepage establishes the positioning; detailed explainers give answer engines the context and evidence they can cite. If you want to shape how AI describes your market, write the explainer.
17.3%
of citation records
Docs are how AI verifies technical claims
Documentation is the second most-cited content type, and it matters most in hands-on categories such as API Development & Management (39.6% docs share), Documentation & Developer Portals (37.5% docs share), API Mocking & Service Virtualization (30.5% docs share). Before AI recommends a tool for a technical job, it looks for docs that prove the tool can do it.
+40.8 pts
above the category median
Some category leaders are far ahead of the median
Northflank shows up in AI Code Sandboxes & Agent Runtimes answers 40.8 percentage points more often than the typical brand in that category, and 22.7 points more than second-placed Modal. Gaps like this are built from citable pages: docs, comparisons, and third-party coverage.
64.4%
of citations in source positions #1-#3
Google AI Mode leans hard on its first three sources
On Google AI Mode, 64.4% of citations were the answer's #1, #2, or #3 source, the highest share of any platform we measured. To show up there, your best page has to be good enough to be a primary source, not one of thirty.
10.5%
of citation records
Reddit and other communities feed AI answers
Reddit is the single most-cited third-party domain in the dataset. AI answers lean on the places developers already go to sanity-check tools: Reddit, forums, GitHub discussions. What communities say about you is part of your AI search visibility.
14.5%
of all citation records
Comparison pages and "best tools" lists are recurring sources
Together, comparison pages and listicles account for this share of all citation records in the report. Being cited is not the same as being recommended: useful pages state clear criteria, honest tradeoffs, and who each tool fits. Unsupported "we're #1" claims are not evidence.

AI search is forcing an infrastructure update, one built for depth, not volume.

Gandharva KumarCo-Founder & CEO at Measure

Methodology

What data powers the report

The report uses DevTune tracking data for public developer-tool categories. We ask each AI search platform a fixed set of tool-evaluation questions, then record every source it cites: the brand, domain, URL, content type, and where it sat in the source list.

The analysis uses a fixed 90-day window from 2 Apr 2026 to 30 Jun 2026. It covers 43 public developer-tool categories across five platforms, with every question re-run weekly during that period. Current data for the underlying categories is available on the DevTune verticals index.

43 developer-tool categories: Dataset coverage
5 measured platforms: AI search platforms
Structured evaluation set: Question design

Metric definitions

Every term and metric used in the report is defined in the glossary at the end.

Platform coverage

This edition covers five AI search platforms: Google AI Mode, ChatGPT Search, Perplexity, Gemini Search, and xAI Search. Future editions will add more, including Microsoft Bing Copilot Search.

Platform	Measured projects	Runs	Citation records	Citations / test
Google AI Mode	43	327	13,018	1.60
ChatGPT Search	43	523	44,586	3.42
Perplexity	43	519	20,076	1.56
Gemini Search	43	522	33,774	2.64
xAI Search	43	297	15,360	2.33

How to read the data

A controlled benchmark, not a query log: Every platform and category is tested with the same structured set of evaluation questions, re-run weekly. Fixed questions are what make the results comparable: five platforms measured side by side on identical terms, rather than a sample of whatever users happened to type.
Five platforms, kept separate: This edition covers five AI search platforms. We never average them together, because each one picks sources differently.
A citation is one cited source: A citation record is one source link in one AI answer. It tells you which pages AI relies on. Pair it with your traffic and pipeline data before drawing commercial conclusions.
Cited is not recommended: A page can supply evidence while its publisher is absent from the answer and other brands are mentioned. Citations, brand mentions, and recommendations are three different outcomes. Track all three.
Your domains vs. the open web: We split citations into tracked-brand domains (sites the brands control) and everything else. The first shows what you control; the second shows what the market says about you.
AI search changes fast: Treat this report as a snapshot of the market. Before making decisions for your own category, check the current picture with a monitoring tool such as DevTune.

Chapter 02 · So what

Winning AI search is three different jobs

The data splits the work into three jobs. On selective platforms like Gemini Search, get picked as one of the first few sources. On platforms like ChatGPT Search, make your own site answer buyers' questions. On broad platforms like Google AI Mode and xAI Search, get the rest of the web talking about you.

Be one of the first sources cited

Gemini Search, Perplexity

Gemini Search currently shows 46.8% of citations in sources #1-#3 and 2.64 citations per test. Perplexity shows 29.6% of citations in sources #1-#3 and 1.56 citations per test.

So what

These platforms cite few sources, and the first few carry most of the weight. A thin page doesn't get a second chance.

Pick a small number of pages — category explainers, comparisons, key docs — and make each one good enough to be the answer on its own.

Measure: Citation share in sources #1-#3

Make your own site quotable

ChatGPT Search, Google AI Mode

ChatGPT Search cites the most balanced mix of docs, product pages, discussions, and comparisons. Google AI Mode casts wider but still leans on company and editorial pages.

So what

Your site needs to read like proof, not a brochure. Product pages, docs, and blog posts should make the same claims in words an AI answer can quote directly.

For each thing buyers care about, build the full set: what it is, how it works, what it integrates with, what it costs, and what the limits are. A well-structured FAQ section is a straightforward, clean way to answer these questions on the page.

Measure: Own-site citation share by page type

Get the rest of the web telling your story

Google AI Mode, Perplexity, xAI Search

These platforms cite community posts, video, editorial pages, and reference sites. Reddit, YouTube, Medium, GitHub, Dev.to, LinkedIn, and Stack Overflow all show up in the data.

So what

AI search doesn't just read your site. If Reddit or an old tutorial explains your category badly, that version becomes part of your answers.

Treat community answers, GitHub, tutorials, listicles, and partner pages as channels. Show up there and keep the story consistent.

Measure: Third-party citation quality

It's too easy as a founder to believe your category is defined on your homepage, and get really precious about the messaging there. The reality is it's defined in a Reddit thread you've maybe never even read, and now that thread is training the answer engine your buyers talk to. You can either be in that conversation or be described by the people who are.

Barrie SegalFounder at Kind Stranger

Category position

Match the content plan to the evidence pattern

Categories differ in citation activity, brand presence, docs reliance, and third-party influence. Locate the pattern that best matches your category, then use the corresponding action as a benchmark to test against current data for your own brand.

High activity, low brand presence: Answers cite many sources, but no tracked brand appears consistently.e.g. Cloud development environments, agent authentication, AI code sandboxes, workflow orchestration.Name the category before the incumbents do: publish the market overview, the decision criteria, the alternatives pages, and the how-to guides.
High activity, high brand presence: Answers cite many sources, and established brands already appear consistently.e.g. Incident management, secrets management, API development, documentation portals.Beat them on freshness and detail: update core pages, prove your integrations, publish data, and keep comparison claims current.
Docs-led evidence: Docs win an above-average share of citations, because AI needs technical proof before it recommends anything here.e.g. API development, data curation, documentation and developer portals, secrets management.Put the things marketers usually hide in your docs: what is supported, what the limits are, how security works, what it costs, and complete examples.
Third-party-led evidence: Third-party sites (Reddit, YouTube, GitHub, review lists) shape the answers more than most founders expect.e.g. MLOps and incident management lean on Reddit; autonomous coding agents on YouTube; agent orchestration and API mocking on GitHub; data engineering and AI code review on listicles.Find the pages AI already cites in your category, fix the ones that get you wrong, and earn coverage on the rest.

The management implication

Founder: AI search decides how your category gets described. If you don't define the buying criteria, AI will borrow them from competitors, listicles, and Reddit.; Fund the category story, the pages that prove it, and the third-party coverage as one budget, not three.
Marketing: More generic blog posts isn't the answer. The highest-leverage content answers the questions buyers ask when comparing tools, and gets backed up elsewhere.; Plan content by buyer question and platform, not by channel calendar.
Docs and developer relations: Docs, code examples, GitHub, and community answers all get cited. They shape how AI describes what your product is for.; Treat technical accuracy, working examples, and community answers as marketing work, not just support work.

Optimizing for AI search is the new marketing frontier, which is especially true in devtools. To win, generic content won't cut it. One needs a rigorous, focused strategy that establishes absolute domain authority.

Chris BrightVP of Revenue at Depot

Chapter 03 · Platforms

Each platform picks sources differently

This edition covers Google AI Mode, ChatGPT Search, Perplexity, Gemini Search, and xAI Search. Compare them one at a time: how many sources they cite, how concentrated those sources are, and how often they cite brand-owned sites all vary a lot.

Fig. 03.1 · Platform map

Breadth versus source-list concentration

Bubble size shows how many distinct domains a platform cites. The vertical axis shows how much weight its first three sources carry. Platforms high on this chart reward one great page; platforms to the right reward breadth.

Platform	Volume	Citations / test	Domains	Sources #1-#3	Tracked-brand share
Google AI Mode	13,018	1.60	2,802	64.4%	23.0%
ChatGPT Search	44,586	3.42	5,274	38.0%	24.6%
Perplexity	20,076	1.56	4,785	29.6%	12.9%
Gemini Search	33,774	2.64	4,000	46.8%	26.9%
xAI Search	15,360	2.33	3,340	12.7%	31.5%

Google AI Mode64.4% in sources #1-#3

Casts a wider net than Gemini Search. Strong pages on your own site help, but video, editorial coverage, and community discussion count too.

ChatGPT Search38% in sources #1-#3

Balanced mix of sources. Make your docs, product pages, and comparisons each answer buying questions on their own, without needing the homepage for context.

Perplexity29.6% in sources #1-#3

Puts heavy weight on its first few sources and likes third-party pages: listicles, short explainers, community threads, GitHub, and video.

Gemini Search46.8% in sources #1-#3

The most selective platform. It cites few sources, so every cited page matters. One definitive, current page beats ten average ones.

xAI Search12.7% in sources #1-#3

Pulls from the widest slice of the web. Community posts, video, social, and long-tail pages all feed its answers.

Source mix by platform

The same page won't win everywhere

Gemini Search cites a few strong sources. ChatGPT Search leans on docs, product pages, discussions, and comparisons. xAI Search pulls from the long tail of the web. Decide which platforms matter for you before you assign content budget.

Fig. 03.2 · Content composition by platform

DocsBlogComparisonProductDiscussionArticleListicleVideoLanding

xAI Search

Google AI Mode

ChatGPT Search

Perplexity

Gemini Search

xAI Search

Company / commercial: 74.5%
Community: 3.8%
Editorial: 8.1%

Google AI Mode

Company / commercial: 66.3%
Community: 6.8%
Editorial: 7.2%

ChatGPT Search

Company / commercial: 59.4%
Community: 21.0%
Editorial: 4.5%

Perplexity

Company / commercial: 68.8%
Community: 7.0%
Editorial: 8.9%

Gemini Search

Company / commercial: 77.7%
Community: 3.1%
Editorial: 10.5%

Platform	Company / commercial	Community	Editorial
xAI Search	74.5%	3.8%	8.1%
Google AI Mode	66.3%	6.8%	7.2%
ChatGPT Search	59.4%	21.0%	4.5%
Perplexity	68.8%	7.0%	8.9%
Gemini Search	77.7%	3.1%	10.5%

Open web domains outside tracked brands: 75.98%; 96,351 citations · 11,754 domains · 37.3% in sources #1-#3
Tracked brand domains: 24.02%; 30,463 citations · 781 domains · 43.0% in sources #1-#3

For the past few years savvy buyers have been appending 'reddit' to their Google searches because they trust a stranger with first-hand experience over a vendor landing page. AI is now outsourcing trust to the same place buyers already did, but at scale. The winning tools aren't gaming it; they just actually showed up in those conversations.

Barrie SegalFounder at Kind Stranger

Content types

What content types win citations

Blog posts dominate, but docs, discussions, articles, listicles, product pages, and comparisons all earn real share. The useful unit is a connected set of pages that explains the category, proves the technical claims, and makes comparison easy.

The report-wide mix is a benchmark, not a substitute for monitoring your own brand. A company's cited sources can look very different from the aggregate pattern.

It’s much more distracting than it is helpful to think about AEO in terms of purely keywords. When looking at actually cited sources at Railway, it’s mostly sources that are user-generated, not company-generated.

Sarah Krasnik BedellFounding Growth Marketer at Railway

Where blog content appears most often

Testing & QA46.1%

Infrastructure as Code46.0%

Mobile Development Platforms & Cross-Platform45.5%

Data Engineering & ETL/ELT Pipelines44.0%

Internal Developer Platforms41.4%

Containers & Orchestration41.1%

Blog post34.3%

43,477

Documentation17.3%

21,923

Discussion8.8%

11,132

Product page7.5%

9,514

Article7.4%

9,395

Comparison7.3%

9,268

Listicle7.2%

9,126

Other6.7%

8,435

The data is compelling: blog posts are the largest citation surface, docs second, and landing pages barely register. Useful explainers win. Specific docs win. Concrete comparisons win. Vague landing pages lose.
Blogs often come from having actually solved a real problem, which is probably why they win. It is also why Stack Overflow worked so well, and why Reddit threads are cited so often today: real questions, specific context, answers shaped by people who have actually encountered the problem.

Gandharva KumarCo-Founder & CEO at Measure

Chapter 03A · Documentation

Docs are becoming AI infrastructure

Docs used to be for developers who had already chosen you. Now AI search reads them to decide whether to recommend you at all: what you integrate with, how security works, and whether your claims hold up.

External signal · GitBook

GitBook recorded 23.8 million AI-agent requests and 22.2 million human requests across qualifying GitBook-hosted documentation sites from 27 April to 3 May 2026. After excluding crawler and other detected-bot traffic, agents accounted for 51.8% of human-plus-agent requests.

This is GitBook traffic data, not part of DevTune's 90-day citation benchmark, but it points to the same operating reality: documentation now serves machines as well as people.

GitBook included sites with more than 100 human page views in April 2026. Figures are aggregate requests or page views, not unique readers or sessions; about 22% of agent traffic had no identifiable bot name. Read the GitBook research.

01
Docs are how AI verifies your claims
Documentation is the second most-cited content type overall, and its share is much higher in hands-on categories such as API Development & Management (39.6% docs share), Documentation & Developer Portals (37.5% docs share), API Mocking & Service Virtualization (30.5% docs share).
Make each docs page complete on its own: what it is for, prerequisites, limits, working examples, error cases, and next steps.
02
In docs-heavy categories, be specific
Categories where docs win the most citations are the ones where buyers need to see the workflow before they trust a recommendation.
Write task-level guides for the questions buyers actually ask: setup, integration, migration, security, pricing limits, and what happens when things fail.
03
Blog-heavy categories still need docs
Blog posts often define a market, but AI needs docs and product detail before it can recommend a tool for a concrete job.
For every claim a blog post introduces, make sure a docs page proves it.
04
There is more than one way to win
The brand scorecards show different brands winning with different pages: FAQs, category blog posts, product pages, docs, and comparisons all carry visibility somewhere.
Find your strongest cited page type, then build the companion pages around it so AI doesn't have to guess the missing context.

AI agents now account for the majority of intentional documentation reads on GitBook — they crossed 50% this spring, up from under 10% at the start of 2025. Docs used to convert developers who had already chosen you. Now they're the evidence AI weighs before it recommends you at all. The teams that win won't be the ones with the most content — they'll be the ones whose docs are structured, specific, and current enough for a machine to trust.

Addison SchultzDeveloper Relations Lead at GitBook

Docs-led categories

In these categories, docs already win a big share of citations. If you compete here, your docs need to answer buying questions, not just setup steps.

API Development & Management39.6% docs

Documentation & Developer Portals37.5% docs

API Mocking & Service Virtualization30.5% docs

Design Systems & Component Libraries30.1% docs

Version Control & Code Collaboration27.1% docs

Secrets Management & Vault24.6% docs

High-activity categories where docs are underbuilt

These categories get lots of citations, but few of them go to docs. That is an open gap: publish the technical detail before comparison sites and Reddit threads define the category for you.

DevSecOps & Application Security: 2.64 citations/test · 6.2% docs
Data Engineering & ETL/ELT Pipelines: 2.70 citations/test · 8.9% docs
Salesforce DevOps: 2.72 citations/test · 9.8% docs
Web Data Infrastructure for AI: 2.93 citations/test · 11.1% docs
AI Code Sandboxes & Agent Runtimes: 2.75 citations/test · 12.3% docs

Our argument at dbt is that outputs are only as trustworthy as the context behind them. That holds for analytics, and it turns out it holds for AI search too.

Dan PoppySenior Manager, Content at dbt Labs

Content architecture for AI consumption

Answer the question first. A page should fully answer the evaluation or how-to question it exists for before you worry about headings, schema, or promotion.
Open every important page with the short version: what the product does, who it is for, when to use it, and what it replaces.
Give each page one job: concept pages, task guides, API references, integration pages, comparisons, pricing explainers, FAQs, troubleshooting.
Make comparison and ranked pages honest: clear criteria, real tradeoffs, who each option fits, and proof. A thin page that ranks yourself first won't get reused.
Put the technical facts on the page: current code examples, SDK and API versions, setup steps, prerequisites, limits, security notes, and pricing constraints.
Write small pages for specific questions, then link them into category and comparison hubs so readers and crawlers can see the bigger picture.
Use machine-readable structure where it helps: clean headings, canonical URLs, FAQ schema, raw markdown or llms.txt, and docs APIs if you have them.
Line up the outside story: GitHub, community answers, review lists, partner docs, tutorials, and videos should repeat the same criteria your own pages use.

Interestingly enough, we see that Docs tend to be the most commonly cited surface for Clerk

Alex RappHead of Growth Marketing at Clerk

Chapter 03B · Measurement

Measure influence and business outcomes

A page can shape an AI answer without getting a click, so page views alone miss part of its influence. Measure citations and recommendations alongside traffic, then follow AI-referred visitors through signup, activation, monetisation, and retention.

Utilizing AI search tracking tools are imperative for attribution strategy and progress tracking, as AI search relevancy is no longer 1:1 leading to direct clicks. By relying upon basic attribution through a platform like GA4, you're missing out on detailed information about how AI search overviews and AI answers are leading (or not) back to your brand.

Samantha TroiloMarketing Lead at Beefree

What to report to leadership

The AI-search scorecard

Citation visibility shows whether answer engines use your content; it does not establish commercial impact on its own. The dashboard should answer two connected questions: when buyers and AI search look at our category, do they find current, provable pages that make our case; and when those answers send us traffic, does that traffic produce valuable, retained customers?

Do this next: add an AI-search view to the content review you already run. Join citation visibility to the referral funnel: visits, signups, activation, paid conversion, revenue, and retention by platform and landing page.

Metric	Business question	What to do
Answer presence	Do we show up when people ask about our category?	Track this per platform and per question topic. One blended number hides where you are losing.
Recommendation language	Does the answer recommend us, or just cite us as a source?	Track when answers recommend you, when they recommend competitors, and which pages back each recommendation.
Citation count	Which of our pages are AI answers actually using?	Double down on pages that are getting cited. Check they support your story rather than a competitor narrative.
Citations per test	How many sources does an answer in our category cite?	Categories with many citations per answer have room for lots of content. Categories with few need one definitive page.
Source #1-#3 citation share	How often are we one of the first three sources cited?	On platforms where the first few sources dominate, one strong page beats ten average ones. Consolidate.
Own-site citation share	Is AI learning about us from our site, or from someone else?	If third-party pages carry your story, publish the definitive version on your own site, then keep the external coverage accurate.
Third-party dependency	Which outside sites shape answers about us?	Treat Reddit, GitHub, YouTube, and review sites as channels. Show up there deliberately.
AI-referral conversion	Do visits from AI answers become signups, trials, or leads?	Preserve AI referrers and landing pages through the signup journey. Compare conversion rates by platform, category page, and cited URL.
Activation and retention	Do AI-referred users reach value and keep using the product?	Cohort AI-referred signups by activation milestone, time to value, retained usage, and churn. Use those outcomes to test whether AI referrals bring better-fit users, not only more visits.
Monetisation	Does AI-referred demand create pipeline and revenue?	Connect acquisition data to CRM and billing outcomes: qualified pipeline, paid conversion, revenue, expansion, and payback by AI source.
Platform coverage	Which platforms need their own plan?	Report each platform separately. Averaging five platforms into one number hides what each one needs.
Freshness / movement	Is our visibility improving or decaying?	Review movement weekly. Refresh pages before a stale claim becomes the cited answer.

The goal: get an LLM to mention your product, and then enable the user to implement it in their project effectively. Even better, to cite something you wrote, but frankly citation of first-party content is a byproduct, not a goal.

Sarah Krasnik BedellFounding Growth Marketer at Railway

Category benchmarks

Where AI answers cite the most sources

In these categories, each AI answer cites more sources, which means more chances for your pages to be one of them. Publish the page type the category is missing, then watch whether it starts getting cited.

Developer Tunnels & Localhost Ingress3.95 / test

Web Data Infrastructure for AI2.93 / test

AI Browser Infrastructure2.78 / test

AI Code Sandboxes & Agent Runtimes2.75 / test

Salesforce DevOps2.72 / test

Strategy Lab · interactive

Explore the data for your own category

Find your category by name or by a tracked brand. The category profile then shows its leaders, citation sources, platform patterns, and benchmark-backed priorities in one place.

Category heatmap

Find your category by its name or any tracked brand, then select a row. Every panel in the category profile below will update.

MetricFind category or brand

A 0-100 score combining citations per test (35%), cited-domain breadth (20%), lower average brand presence (25%), and lower tracked-brand citation share (20%). Higher scores indicate less-settled category visibility.

Category	Openness	Cites / test	Domains	Docs	Blog	Brand share	Top-3 share
IDEs & Code Editors	54.2	2.44	791	8.9%	31.1%	5.5%	39.3%
Open Source Commercial / OSS Infrastructure	52.6	2.12	633	14.0%	38.3%	6.2%	40.4%
Mobile Development Platforms & Cross-Platform	51.5	2.15	657	15.0%	45.5%	9.5%	40.8%
AI/ML Infrastructure & LLM Tools	51.4	2.45	643	12.6%	37.3%	12.7%	38.1%
API Development & Management	50.8	2.30	495	39.6%	30.6%	7.9%	41.1%
Search & Vector Databases	50.4	2.48	685	13.5%	32.6%	18.1%	35.1%
Containers & Orchestration	50.3	1.92	544	13.0%	41.1%	9.2%	38.2%
Communications APIs	50.1	2.43	671	15.6%	30.9%	17.9%	36.8%
Databases & Data Infrastructure	49.5	1.98	546	19.0%	38.6%	14.4%	41.8%
Agent Authentication & Identity for AI	49.1	2.63	603	20.3%	36.1%	20.0%	35.6%
Testing & QA	48.8	2.12	500	11.2%	46.1%	14.2%	37.4%
Cloud Development Environments (CDEs)	48.6	2.47	500	16.0%	27.9%	16.0%	41.7%
Developer Tunnels & Localhost Ingress	48.5	3.95	550	16.3%	35.6%	24.3%	36.1%
CI/CD & Build Systems	48.4	1.92	545	14.2%	35.8%	17.0%	43.2%
DevSecOps & Application Security	48.3	2.64	518	6.2%	36.2%	19.1%	33.1%
Messaging & Event Streaming	48.2	2.53	534	22.0%	34.4%	19.0%	38.1%
Cloud Infrastructure & PaaS	47.9	2.50	673	15.8%	35.1%	28.0%	36.5%
Deployment & Hosting Platforms	47.6	2.23	615	16.9%	30.7%	25.3%	40.7%
Documentation & Developer Portals	47.5	2.40	620	37.5%	28.9%	27.2%	37.8%
AI Code Review & Code Quality	47.1	2.32	447	11.7%	32.3%	18.6%	39.7%
Web Data Infrastructure for AI	47.1	2.93	566	11.1%	32.3%	27.1%	33.0%
Design Systems & Component Libraries	47.0	2.29	513	30.1%	29.6%	21.8%	39.7%
Payments & Fintech APIs	46.7	2.20	559	23.3%	31.2%	27.3%	39.3%
Transactional Email & Developer Email APIs	46.7	2.52	432	14.5%	29.9%	17.5%	39.5%
Workflow Orchestration & Durable Execution	46.5	2.65	517	23.8%	28.7%	26.2%	37.5%
Version Control & Code Collaboration	46.4	2.07	559	27.1%	31.9%	22.5%	40.5%
Error Tracking & Crash Reporting	45.8	2.38	388	23.4%	27.0%	22.7%	37.1%
Data Engineering & ETL/ELT Pipelines	45.3	2.70	464	8.9%	44.0%	27.4%	36.3%
Issue Tracking & Project Management	45.3	2.13	521	16.4%	30.5%	27.5%	42.3%
Observability & Monitoring	45.3	2.11	381	10.3%	34.9%	23.6%	39.7%
Developer Analytics & Product Analytics	44.6	1.79	432	12.3%	33.8%	25.6%	44.1%
AI Code Sandboxes & Agent Runtimes	44.4	2.75	467	12.3%	28.9%	32.3%	38.5%
Backend-as-a-Service & Realtime	43.9	2.22	476	23.8%	35.5%	32.4%	40.1%
AI Browser Infrastructure	43.7	2.78	395	15.1%	40.7%	30.2%	33.7%
API Mocking & Service Virtualization	43.5	2.57	318	30.5%	27.3%	25.5%	43.8%
Headless CMS & Content Platforms	43.4	2.63	426	15.8%	38.4%	35.7%	36.1%
Infrastructure as Code	43.3	1.90	401	13.9%	46.0%	31.0%	40.7%
Internal Developer Platforms	43.0	2.34	381	15.2%	41.4%	31.8%	38.9%
Secrets Management & Vault	42.7	2.34	358	24.6%	27.5%	30.7%	40.4%
Authentication & Identity	42.5	2.12	468	19.7%	36.3%	33.7%	40.1%
Salesforce DevOps	38.5	2.72	190	9.8%	29.6%	43.5%	36.5%
Incident Management & On-Call	37.8	2.38	325	11.5%	28.9%	47.7%	41.2%
Feature Flags & Experimentation	36.3	2.31	264	16.1%	34.1%	52.4%	41.1%

Category profile

Cloud Development Environments (CDEs)

The plan, brand ranking, platform metrics, and source matrix below are all filtered to this category. Choose another heatmap row to update the complete profile.

View live category data

What the benchmark suggests for Cloud Development Environments (CDEs)

These recommendations use only the citation patterns measured for this category. Treat them as a market benchmark, then validate the priorities against current monitoring for your own brand.

Category openness

48.6

Tracked-brand share

16.0%

Category citations/test

2.47

Lead with comparison content

Cloud Development Environments (CDEs) has 27.9% blog share, 16.0% docs share, and 17.8% comparison/listicle share. Start with the content type AI already cites here, then fill in the missing page types around it.

Treat the category as wide-open

16.0% of citations point to tracked brand domains and average brand presence is 7.1%. Define the category language now, before AI answers settle around incumbents.

Turn the scenario theme into a page brief

The top question theme here is what cloud development environments support air-gapped or private network configurations for teams working with regulated data?. Treat it as a page brief: answer the question directly, explain the tradeoffs, add proof, and link to the relevant docs and comparisons.

Scenario drilldown

What cloud development environments support air-gapped or private network configurations for teams working with regulated data?

149 citations21.5% docs18.8% blogcoder.com

Use “What cloud development environments support air-gapped or private network configurations for teams working with regulated data?” as a page brief: answer it directly, explain tradeoffs, add verifiable proof, and connect the page to the relevant docs and comparisons.

What cloud development environment tools allow enterprises to bring their own cloud account so workspace infrastructure runs in their own VPC?

148 citations24.3% docs30.4% blognorthflank.com

Use “What cloud development environment tools allow enterprises to bring their own cloud account so workspace infrastructure runs in their own VPC?” as a page brief: answer it directly, explain tradeoffs, add verifiable proof, and connect the page to the relevant docs and comparisons.

Which cloud IDE platforms let a contributor open and run a project in a browser without installing anything locally?

147 citations2.0% docs27.2% blogthesoftwarescout.com

Create a dedicated page for “Which cloud IDE platforms let a contributor open and run a project in a browser without installing anything locally?” that names the decision criteria, gives a direct answer, and links every claim to product or technical proof.

Platform guidance

Each platform rewards different things. Use these cards to adapt the category plan to how each platform actually picks its sources.

Google AI Mode

Category citations / test

1.04

Add open-web and video proof

Broad Google research surface. It cites more pages and includes video and wider web coverage.

Pair strong blog and docs pages with video, third-party validation, and consistent entity language across the open web.

ChatGPT Search

Category citations / test

3.85

Make owned pages self-contained

Balanced source selection. Docs, product pages, discussion, and comparisons appear more often than on most platforms.

Make core docs and product pages answer buying and implementation questions without needing homepage context.

Perplexity

Category citations / test

1.82

Win credible ranked-list coverage

Puts heavy weight on its first few sources. Listicles and compact third-party pages can carry unusual weight.

Earn credible ranked-list coverage and keep owned comparison pages concise, specific, and current.

Gemini Search

Category citations / test

3.00

Compete for a top citation position

Selective source selection. Fewer sources make top citation positions unusually important.

Prioritize definitive, fresh, tightly structured pages that can earn a top citation slot for the questions buyers ask in your category.

xAI Search

Category citations / test

2.04

Broaden the source footprint

Largest long-tail source set. Community, video, and deep web sources matter more than elsewhere.

Broaden surface area across Reddit, GitHub, YouTube, docs, blogs, and category comparisons so the long tail reinforces your positioning.

Leading tracked brands in Cloud Development Environments (CDEs)

Brand presence is the share of measured answer observations in this category with at least one citation mapped to each tracked company. This is an AI-search visibility ranking, not a judgment of product quality.

GitHub Codespaces

22.3%

Category leader

Coder

18.4%

Second-highest presence

Category median2.2%

The middle tracked brand when category presence is ranked.

What appears to drive GitHub Codespaces's visibility

Documentation authority drives most of the leader's citations. ChatGPT Search cites the leader most.

Docs 55.1%Blog 0.6%Compare 0.6%Product 28.5%

Lead over second: 3.8 pts
Leader citation records: 158
Leading page type: Documentation

Open the live Cloud Development Environments (CDEs) benchmark

All tracked companies in Cloud Development Environments (CDEs)

Brand presence is the share of measured answer observations with at least one citation mapped to the company. Owned citations count every such citation record, so one answer can contribute more than one; top-three share and average position show how prominently those sources appear. Select any column heading to reorder the comparison.


GitHub Codespaces github.com	22.3%	158	52.5%	9.2	Documentation	ChatGPT Search
Coder coder.com	18.4%	109	46.8%	11.6	Comparison page	ChatGPT Search
Gitpod www.gitpod.io	14.3%	108	62.0%	5.1	Documentation	ChatGPT Search
DevPod devpod.sh	13.3%	66	25.8%	8.6	Documentation	ChatGPT Search
Codeanywhere codeanywhere.com	5.7%	19	15.8%	19.3	Landing page	xAI Search
Replit replit.com	2.2%	10	40.0%	25.9	Product page	xAI Search
StackBlitz stackblitz.com	1.0%	6	16.7%	21.7	Product page	ChatGPT Search
CodeSandbox codesandbox.io	0.7%	4	0.0%	6.3	Landing page	ChatGPT Search
Firebase Studio studio.firebase.google.com	0.7%	3	0.0%	6.0	Blog post	Gemini Search
Jetify www.jetify.com	0.1%	0	—	—	No owned citation	No owned citation
Daytona www.daytona.io	0.0%	0	—	—	No owned citation	No owned citation

Where ChatGPT Search finds evidence for Cloud Development Environments (CDEs)

This matrix contains only citation records from the selected category and platform. It shows whether answers rely on company pages, editorial coverage, community sources, reference material, or video, and which page types carry that evidence.

Company / commercial / Documentation

221 citations, 34 domains

20.87% of ChatGPT Search citations in this category

Community / Discussion

204 citations, 1 domain

19.26% of ChatGPT Search citations in this category

Company / commercial / Product page

128 citations, 34 domains

12.09% of ChatGPT Search citations in this category

Company / commercial / Blog post

105 citations, 35 domains

9.92% of ChatGPT Search citations in this category

Company / commercial / Comparison page

92 citations, 20 domains

8.69% of ChatGPT Search citations in this category

Company / commercial / Article

53 citations, 16 domains

5.00% of ChatGPT Search citations in this category

Reference / Comparison page

44 citations, 1 domain

4.15% of ChatGPT Search citations in this category

Other / Other

44 citations, 25 domains

4.15% of ChatGPT Search citations in this category

Company / commercial / Landing page

25 citations, 14 domains

2.36% of ChatGPT Search citations in this category

Editorial / Listicle

21 citations, 7 domains

1.98% of ChatGPT Search citations in this category

Reference / Article

17 citations, 2 domains

1.61% of ChatGPT Search citations in this category

Company / commercial / Other

16 citations, 7 domains

1.51% of ChatGPT Search citations in this category

Education / Listicle

15 citations, 2 domains

1.42% of ChatGPT Search citations in this category

Editorial / Article

13 citations, 9 domains

1.23% of ChatGPT Search citations in this category

Company / commercial / Listicle

9 citations, 4 domains

0.85% of ChatGPT Search citations in this category

Reference / Documentation

8 citations, 6 domains

0.76% of ChatGPT Search citations in this category

Reference / Other

7 citations, 2 domains

0.66% of ChatGPT Search citations in this category

Reference / Listicle

7 citations, 2 domains

0.66% of ChatGPT Search citations in this category

Editorial / Comparison page

7 citations, 2 domains

0.66% of ChatGPT Search citations in this category

Editorial / Product page

5 citations, 3 domains

0.47% of ChatGPT Search citations in this category

Marketplace / Article

3 citations, 1 domain

0.28% of ChatGPT Search citations in this category

Company / commercial / Discussion

2 citations, 1 domain

0.19% of ChatGPT Search citations in this category

Community / Article

2 citations, 2 domains

0.19% of ChatGPT Search citations in this category

Community / Listicle

2 citations, 2 domains

0.19% of ChatGPT Search citations in this category

Marketplace / Product page

2 citations, 1 domain

0.19% of ChatGPT Search citations in this category

Community / Blog post

2 citations, 2 domains

0.19% of ChatGPT Search citations in this category

Education / Blog post

1 citations, 1 domain

0.09% of ChatGPT Search citations in this category

Reference / Blog post

1 citations, 1 domain

0.09% of ChatGPT Search citations in this category

Other / Landing page

1 citations, 1 domain

0.09% of ChatGPT Search citations in this category

Editorial / Blog post

1 citations, 1 domain

0.09% of ChatGPT Search citations in this category

Other / Article

1 citations, 1 domain

0.09% of ChatGPT Search citations in this category

AEO is still the wild west. Spam reddit profiles, mass produced 'we're the best, obviously' listicles and comparison pages, llms.txt could be a thing now. Finding out what fight you're in, so you can test the right things obsessively feels like the way to uncover wins right now.

Matt HendersonDirector of Growth Marketing at Sentry

Report-wide · 9 views

Explore patterns across all categories

These views aggregate the complete report dataset. Use them to compare platforms, content types, sources, categories, and brands; they do not change the category profile above.

Return to your category profile

Google AI Mode13K citations / 1.60 per test

ChatGPT Search44.6K citations / 3.42 per test

Perplexity20.1K citations / 1.56 per test

Gemini Search33.8K citations / 2.64 per test

xAI Search15.4K citations / 2.33 per test

Platform citation pattern

Google AI Mode64.4% in sources #1-#3

Casts a wider net than Gemini Search. Strong pages on your own site help, but video, editorial coverage, and community discussion count too.

ChatGPT Search38% in sources #1-#3

Balanced mix of sources. Make your docs, product pages, and comparisons each answer buying questions on their own, without needing the homepage for context.

Perplexity29.6% in sources #1-#3

Puts heavy weight on its first few sources and likes third-party pages: listicles, short explainers, community threads, GitHub, and video.

Gemini Search46.8% in sources #1-#3

The most selective platform. It cites few sources, so every cited page matters. One definitive, current page beats ten average ones.

xAI Search12.7% in sources #1-#3

Pulls from the widest slice of the web. Community posts, video, social, and long-tail pages all feed its answers.

Chapter 04 · Category openings

Where category visibility is least settled

The category openness score weights citations per test (35%), cited-domain breadth (20%), lower average brand presence (25%), and lower tracked-brand citation share (20%). A higher score means AI answers draw on an active, broad source set that tracked brands control less. Pair it with your own demand, pipeline, and brand-monitoring data before setting priorities.

Fig. 04.1 · Category openness map

Activity against brand concentration

All 43 categories are plotted so every team can locate its market. Use category search, brand search, or the quadrant filter to isolate a market, then hover a bubble for the category name and score. The full category table is in the Strategy Lab. Bubble size encodes cited domains.

Filter categoryFilter brandQuadrant

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