More developers now ask an AI assistant "which GPU cloud should I use for inference?" before they open a single pricing page. I wanted to know what those answers actually look like, so I asked.
Short version: ChatGPT and Gemini mostly send you to the hyperscalers. Perplexity sends you somewhere else entirely. In this run it did not name AWS or Google Cloud once.
How I tested it
I wrote 8 buyer-style questions about GPU inference. None of them names a brand:
- Which vendors are best for a startup needing low-cost GPU inference for prototypes?
- Who offers the best overall GPU cloud services for AI inference today?
- Which providers are ideal for small teams wanting easy GPU inference setup and support?
- Which vendors excel for real-time low-latency inference for customer-facing apps?
- Who has the most cost-effective options for high-volume batch GPU inference?
- Which providers integrate best with common MLOps tooling and CI/CD pipelines?
- Which vendors offer enterprise-grade security, compliance, and dedicated GPU options?
- Who provides the best hybrid or multi-cloud GPU inference solutions for on-prem integration?
Each question went once to each assistant through its official API, on October 2, 2026:
- ChatGPT:
gpt-5-mini - Gemini:
gemini-3.5-flash-lite - Perplexity:
pplx-fast
That gives 24 answers. A second model pass listed the products each answer recommended, in order, and I counted them.
The results
| # | Provider | Answers naming it (of 24) | ChatGPT | Gemini | Perplexity | Named first |
|---|---|---|---|---|---|---|
| 1 | Google Cloud | 13 | 6 | 7 | 0 | 2 |
| 2 | AWS | 10 | 7 | 3 | 0 | 5 |
| 3 | Microsoft Azure | 10 | 5 | 4 | 1 | 1 |
| 4 | Lambda | 9 | 3 | 3 | 3 | 1 |
| 5 | RunPod | 8 | 2 | 2 | 4 | 3 |
| 6 | CoreWeave | 5 | 0 | 2 | 3 | 0 |
| 7 | Modal | 4 | 0 | 1 | 3 | 0 |
| 8 | Groq | 3 | 0 | 2 | 1 | 3 |
Twenty products were named in total. The full table is on the leaderboard page.
Four things that stood out
1. Each assistant has a different favourite
- ChatGPT named AWS most often (7 of its 8 answers).
- Gemini named Google Cloud most often (7 of 8).
- Perplexity named RunPod most often (4 of 8), and never named Google Cloud or AWS.
If your team "just asks AI", the answer depends heavily on which AI you ask.
2. Lambda was the most consistent across all three
Lambda was named exactly 3 times by each of ChatGPT, Gemini and Perplexity. Azure and RunPod were also named by all three, but very unevenly (5/4/1 and 2/2/4). It did not top any single assistant's list.
3. Being named first is not the same as being named often
Google Cloud appeared in the most answers (13) but came first in only 2 of them. AWS came first in 5 of its 10, with an average position of 2.0. Groq was named only 3 times, but it was first every time.
So if you are tracking AI visibility for a product, mention count and position tell different stories. Track both.
4. The assistants blur categories
Some answers to "GPU cloud for inference" recommended OpenAI, Anthropic and Cohere, which sell model APIs rather than GPUs. Others recommended Groq, which runs its own LPU hardware rather than GPUs. I left them in, because that is what a buyer would actually read. It is also a reminder that AI answers mix "where do I run my model" with "whose model do I call".
Where Perplexity got its answers
Perplexity returns its sources, so you can see what it was reading. These were the most-cited sites across its 8 answers:
| Website | Answers citing it |
|---|---|
| gmicloud.ai | 4 |
| io.net | 4 |
| digitalocean.com | 4 |
| inworld.ai | 4 |
| dev.to | 3 |
| northflank.com | 3 |
| siliconflow.com | 3 |
| spheron.network | 3 |
Two things are worth noticing:
- Most of these are vendor blogs and comparison posts, not neutral reviews. Whoever publishes the clearest "best GPU cloud for X" article gets read by the assistant.
- dev.to was cited in 3 of 8 answers. Posts on this site are part of what AI assistants learn GPU recommendations from.
That probably explains much of the gap. Perplexity answers from fresh web search results, which are full of specialist providers' own comparison content. ChatGPT and Gemini answer from training data, where the hyperscalers dominate. That is my reading, not something I can prove from 24 answers.
Is it stable?
I ran the same questions on September 28. The top two were the same: Google Cloud (12 answers) and AWS (9). Below them, the order shuffled. RunPod, Modal, Azure and CoreWeave each moved by a few answers.
So the broad pattern held, but small differences between providers are noise at this sample size.
Caveats
- These are API answers from fast, cheap models. The consumer ChatGPT, Gemini and Perplexity apps, with browsing, memory and bigger models, may answer differently.
- 24 answers is a snapshot, not a census. Treat a gap of 1–2 answers as a tie.
- It measures what assistants say, not which provider is best. It is not a benchmark of price, latency or reliability.
- No provider paid to be included, excluded or moved.
Try it yourself
The questions, models, date and raw data (CSV/JSON) are on the GPU cloud leaderboard, and the full method is here. Run the questions against your own assistant and tell me in the comments if you get a different #1.
I'm also curious which you trust more for this kind of question: an assistant that searches the web live (Perplexity) or one that answers from what it learned in training (ChatGPT, Gemini)?
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