When buyers ask AI which DevOps vendor to pick, who gets named?
We put 100 real infrastructure-buyer questions to ChatGPT and Gemini and Perplexity and checked which of 23 DevOps and infrastructure vendors each answer named. 0 of the 100 questions got no named vendor at all — spanning cloud platforms, CI/CD tooling, observability, infrastructure as code, cost optimization and supply-chain security, the everyday categories a platform engineer or SRE actually shops in. Run the check for your platform →
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Why we ran this
Infrastructure buying has quietly moved from "search r/devops and Hacker News" to "ask an assistant and act on the shortlist it gives you." That shortlist is assembled from whatever content the assistant can find — vendor docs, comparison blogs, community threads, benchmark posts — and it either names specific vendors or it doesn’t. This benchmark reads how visible DevOps and infrastructure vendors currently are across the categories a real platform engineer shops in, engine by engine, and where the biggest content openings are.
How we measured it
We wrote 100 questions in the language a real platform engineer or SRE actually uses — cloud platform choice, CI/CD tooling, observability, infrastructure as code and container orchestration, cost optimization, and software-supply-chain security (all 100 are explorable in full below). Each question was sampled once per engine, and every answer was checked for a mention of any of 23 identifiable DevOps and infrastructure vendors — from hyperscale cloud platforms like AWS and Google Cloud to specialized tools like Datadog and HashiCorp — using the same name matcher a live Inserviss scan uses.
A single pass per engine establishes direction and priority; it is not a trend line. Citations are reported as measured; query volumes are labelled estimates, shown as ranges. Engines are never blended. This run covers ChatGPT and Gemini and Perplexity; Microsoft Copilot is not currently sampled.
The tracked list covers 23 vendors a real platform engineer would plausibly shortlist across cloud hosting, CI/CD, observability, infrastructure as code and software-supply-chain security — a deliberately broad, category-spanning set rather than one narrow product line.
What we found, at a glance
- Visibility varies sharply by engine. ChatGPT named a tracked vendor in 89 of 100 answers, Gemini named a tracked vendor in 100 of 100 answers and Perplexity named a tracked vendor in 82 of 100 answers.
- Every single question named a vendor on at least one engine — the most crowded category this study series has measured. The competition here is over which vendor gets named, not whether anyone does.
- AWS is the most-named vendor, appearing in 154 of 300 answers. Only 22 of the 23 tracked vendors were named even once.
- Hyperscale cloud platforms and specialized tools get named at very different rates. See the full breakdown, and which archetype AI actually favors, in the table below.
How often does AI name a vendor at all?
Below is the share of the 100 answers that named at least one tracked vendor, per engine — then the full table, every vendor against every engine, sortable by any column.
Share of 100 answers naming at least one of the 23 tracked vendors. Per engine, never blended.
| Vendor | ChatGPT | Gemini | Perplexity |
|---|---|---|---|
| AWShyperscale | 42 | 76 | 36 |
| Microsoft Azurehyperscale | 38 | 51 | 29 |
| Google Cloudhyperscale | 32 | 55 | 27 |
| GitHub | 25 | 49 | 14 |
| GitLab | 17 | 31 | 14 |
| Datadog | 11 | 36 | 11 |
| HashiCorp | 11 | 23 | 11 |
| Grafana Labs | 10 | 14 | 8 |
| New Relic | 9 | 11 | 4 |
| Cloudflare | 6 | 12 | 4 |
| Vercel | 3 | 11 | 5 |
| CircleCI | 9 | 5 | 5 |
| Snyk | 3 | 10 | 5 |
| PagerDuty | 3 | 9 | 3 |
| Render | 3 | 9 | 3 |
| JFrog | 4 | 5 | 4 |
| Fly.io | 2 | 7 | 1 |
| DigitalOcean | 1 | 5 | 3 |
| MongoDB Atlas | 0 | 8 | 0 |
| Sentry | 1 | 4 | 2 |
| Fastly | 2 | 4 | 1 |
| LaunchDarkly | 2 | 2 | 2 |
| Confluent | 0 | 0 | 0 |
Mentions across 100 answers per engine. Select a column heading to re-sort. Scroll the table sideways on a narrow screen.
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Where the openings are
Coverage varies by question type. Below is the best engine’s coverage for each cluster, out of ~17 questions.
Answers naming a vendor on the strongest engine, per cluster (out of ~17).
How this is estimated
Estimate — a range, not a keyword-tool export. Illustrative estimate, not a keyword-tool export. Each cluster has ~16-17 seed buyer questions; DevOps/infrastructure is a broad, high-volume technical category — long-tail intent phrases run ~120–450 monthly searches each globally, one or two head terms (e.g. "best CI/CD platform") add ~2,500–9,000/mo, and an AI-assistant reformulation multiplier of ×2–4 accounts for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific vendor, the real number comes from the check, not this table.
| Cluster | Best-engine coverage | Est. questions / mo |
|---|---|---|
| Cloud Platform & Hosting Choice | 17/17 | 14,000–50,000 |
| CI/CD & Developer Workflow Tooling | 17/17 | 8,500–32,000 |
| Observability, Monitoring & Incident Response | 17/17 | 9,000–34,000 |
| Infrastructure as Code & Container Orchestration | 17/17 | 7,000–26,000 |
| Cost Optimization & FinOps | 16/16 | 4,000–15,000 |
| Security & Supply Chain | 16/16 | 3,200–12,000 |
Volumes are an estimate, shown as a range. Illustrative estimate, not a keyword-tool export. Each cluster has ~16-17 seed buyer questions; DevOps/infrastructure is a broad, high-volume technical category — long-tail intent phrases run ~120–450 monthly searches each globally, one or two head terms (e.g. "best CI/CD platform") add ~2,500–9,000/mo, and an AI-assistant reformulation multiplier of ×2–4 accounts for the wider phrasing people use with an assistant. Ranges are rounded and widened. For a specific vendor, the real number comes from the check, not this table.
The check runs this same 100-question universe against your vendor’s name, per engine, and returns your AI Visibility Score plus the exact questions where a competitor — or no one — is being named.
Check your vendor’s AI visibility →Explore the 100 questions
Every question, and exactly what each engine did with it — which vendors it named, and which sources it pulled from. Filter by cluster, search for your own name, or show only the questions no vendor has claimed.
The 0 questions no tracked vendor owns on any engine, with the sources AI cites instead — a ready-made content brief.
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Where each engine gets its answers
The domains each engine actually pulled from, per engine — never blended. ChatGPT grounds almost entirely on vendor-owned documentation (docs.aws.amazon.com, learn.microsoft.com, docs.github.com, docs.gitlab.com) — the opposite pattern from most categories this study series has measured, where a third party usually wins. Perplexity leans hardest on Reddit (51 citations, more than any other domain in this study) plus a long tail of dev blogs and comparison sites (dev.to, thectoclub.com, spacelift.io, northflank.com). Gemini names a vendor in every single answer (100 of 100) but returns empty grounding metadata for all of them — a genuine finding, not a parsing gap: Gemini’s knowledge of major cloud and DevOps vendors is apparently strong enough that it doesn’t always invoke Search to answer.
Times a domain was cited across the 100 answers. Blue = a tracked vendor’s own site.
About this study
- Single pass per engine. One sample per question per engine establishes direction; it does not average out run-to-run answer variance.
- Snapshot in time. Collected September 14, 2026. AI engines change their answers week to week; this is not a trend line.
- Conservative name matching. Short one-token vendor names (Snyk, Render, Sentry…) require an exact whole-word match, so a slight undercount is possible. One well-known product, Elasticsearch, was excluded entirely for the opposite reason: its “Elastic” alias collided with AWS’s own Elastic-prefixed product names (Elastic Beanstalk, Elastic IPs) and generic engineering vocabulary (“elastic scaling”) far more often than it meant the product.
- ChatGPT and Gemini and Perplexity this run. Microsoft Copilot is not currently sampled; the measurement is identical when it is.
How to cite this study
To request the underlying data or discuss a vendor-specific analysis, contact igor@inserviss.app.
The same measurement, run against your vendor’s name — per engine, question by question — with your score and the questions to claim first.
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