Reviewed GPU infrastructure providers ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.
Cloud GPU platforms
Cloud GPU platforms prompt brand leaders
100.0%
What GPU cloud should a startup use for fast self-service access, clear hourly pricing, persistent storage, and simple deployment? ranks first
Leading brand engine consensus
12
Prompts ranked
Sep 17
Snapshot recorded
2026 · Rotating schedule
3
Answers behind each row
From the OpenAI, Anthropic and Google model APIs
All 12 prompts by leading brand engine consensus
What GPU cloud should a startup use for fast self-service access, clear hourly pricing, persistent storage, and simple deployment? leads at 100.0%; the middle of the board sits at 66.7%.
- Ahead of second place
- 0%
- Held by the top three
- 42%
- Middle of the board
- 66.7%
- Below half the leader
- 5 of 12
- 1What GPU cloud should a startup use for fast self-service access, clear hourly pricing, persistent storage, and simple deployment?100.0%
- 2Recommend cloud GPU providers with transparent on-demand access to H100 or H200 instances and no long-term commitment.100.0%
- 3Which GPU cloud platforms are best for an enterprise requiring private networking, compliance controls, auditability, and support?66.7%
- 4Which cloud GPU platforms are best for reliable multi-node training of large foundation models?66.7%
- 5Compare cloud GPU platforms on availability, accelerator breadth, interconnect, storage, reliability, support, and developer experience.66.7%
- 6Which GPU clouds are strongest for reserving hundreds of tightly networked accelerators for a multi-month training program?66.7%
- 7Recommend GPU infrastructure providers with broad regional capacity, current accelerator availability, and data-residency options.66.7%
- 8Which GPU clouds offer strong managed Kubernetes or Slurm support for distributed AI training and inference?33.3%
- 9Create a proof-of-concept scorecard for evaluating GPU clouds on real workload throughput, failure recovery, networking, and total cost.33.3%
- 10Recommend a GPU cloud for autoscaling production inference with custom containers, scale-to-zero, and predictable cold starts.33.3%
- 11Which criteria expose capacity, interruption, egress, quota, and vendor lock-in risk when selecting a cloud GPU provider?0.0%
- 12How should a buyer compare GPU cloud pricing across on-demand, spot, reserved, bare-metal, and committed-capacity contracts?0.0%
The prompt boards are re-run in rotation, about one category a day, so each board refreshes every few weeks and shows the date of its snapshot.
leading brand owned citation coverage: 0 on 6 of 12 rows · 3 checks behind each row · 100% confidence across rows.
Compare brands
One brand against up to three competitors, answer by answer
30.6%
of these answers name or cite CoreWeave
11 of 36 answers
40.7%
Its share of the compared brands' appearances
0
Answers citing its own site
0 links to its domains
2 of 3
Model APIs that name it
| Brand | Named or cited | Against primary | Share of compared | Mentions | Answers citing it | Links to its site | Model APIs |
|---|---|---|---|---|---|---|---|
| Lambda Cloudlambda.ai | 38.9%14 of 36 | +8.3 pts | 51.9% | 29 | 5 | 9 | 3 of 3 |
| CoreWeavecoreweave.com | 30.6%11 of 36 | Primary | 40.7% | 19 | 0 | 0 | 2 of 3 |
| Crusoe Cloudcrusoe.ai | 2.8%1 of 36 | -27.8 pts | 3.7% | 1 | 1 | 1 | 1 of 3 |
| Nebius AI Cloudnebius.com | 2.8%1 of 36 | -27.8 pts | 3.7% | 2 | 1 | 1 | 1 of 3 |
Every answer
Which compared brands each answer names
| Question | OpenAIopenai/gpt-4o-mini | Anthropicanthropic/claude-haiku-4-5 | Geminigoogle/gemini-2.5-flash |
|---|---|---|---|
| 01Which cloud GPU platforms are best for reliable multi-node training of large foundation models?Recommendation | CoreWeaveLambda Cloud 5 sources e26df8b1 | None named 0 sources dd4afeab | CoreWeaveLambda Cloud 7 sources 42e44b3b |
| 02Recommend cloud GPU providers with transparent on-demand access to H100 or H200 instances and no long-term commitment.Recommendation | CoreWeaveLambda Cloud, cited 1 6 sources b4cdad7f | Lambda Cloud, cited 2 6 sources 389992af | CoreWeaveLambda Cloud, cited 1 8 sources 44e25546 |
| 03Which GPU clouds are strongest for reserving hundreds of tightly networked accelerators for a multi-month training program?Recommendation | CoreWeaveLambda Cloud 6 sources cf645d59 | None named 0 sources 1618941d | CoreWeaveLambda Cloud 7 sources 63153df5 |
| 04Recommend a GPU cloud for autoscaling production inference with custom containers, scale-to-zero, and predictable cold starts.Recommendation | None named 6 sources 2e810747 | None named 0 sources 31289165 | None named 7 sources b70c0396 |
| 05What GPU cloud should a startup use for fast self-service access, clear hourly pricing, persistent storage, and simple deployment?Recommendation | Lambda Cloud 7 sources 38feeacf | Lambda Cloud, cited 4 8 sources 66c62ad8 | None named 9 sources ab64d39b |
| 06Which GPU cloud platforms are best for an enterprise requiring private networking, compliance controls, auditability, and support?Recommendation | CoreWeaveLambda Cloud 5 sources 4712904b | None named 0 sources ded18530 | None named 8 sources 15999442 |
| 07Recommend GPU infrastructure providers with broad regional capacity, current accelerator availability, and data-residency options.Recommendation | CoreWeaveLambda Cloud 7 sources c7c938a5 | None named 0 sources 3ac9297a | CoreWeaveLambda Cloud 8 sources dcd37607 |
| 08Which GPU clouds offer strong managed Kubernetes or Slurm support for distributed AI training and inference?Recommendation | CoreWeaveNebius AI Cloud, cited 1 7 sources dacd6580 | None named 0 sources 5e058ae3 | Crusoe Cloud, cited 1 9 sources e2bfa496 |
| 09Compare cloud GPU platforms on availability, accelerator breadth, interconnect, storage, reliability, support, and developer experience.Commercial comparison | CoreWeaveLambda Cloud 7 sources a86dbb68 | None named 0 sources 7ee88389 | None named 6 sources 96362ac0 |
| 10How should a buyer compare GPU cloud pricing across on-demand, spot, reserved, bare-metal, and committed-capacity contracts?Commercial comparison | None named 6 sources b4bc35ba | None named 0 sources 301e1970 | None named 5 sources 48e89e4c |
| 11Create a proof-of-concept scorecard for evaluating GPU clouds on real workload throughput, failure recovery, networking, and total cost.Commercial comparison | None named 0 sources fc834653 | None named 0 sources 78741630 | Lambda Cloud, cited 1 4 sources 47480766 |
| 12Which criteria expose capacity, interruption, egress, quota, and vendor lock-in risk when selecting a cloud GPU provider?Commercial comparison | None named 9 sources dfe95a7e | None named 0 sources 51ee7ab8 | None named 5 sources dba5c807 |
Sources
The sites these answers cite
| Site | Answers citing it | Distinct pages | Model APIs | Best position |
|---|---|---|---|---|
| runpod.io | 14 | 2 | 2 | 1 |
| gmicloud.ai | 11 | 9 | 2 | 2 |
| reddit.com | 9 | 8 | 3 | 1 |
| northflank.com | 8 | 5 | 2 | 1 |
| spheron.network | 7 | 7 | 3 | 2 |
| modal.com | 6 | 8 | 2 | 1 |
| youtube.com | 5 | 6 | 3 | 2 |
| lambda.ai | 5 | 3 | 3 | 1 |
| rank.ai | 5 | 3 | 2 | 1 |
| medium.com | 4 | 4 | 3 | 2 |
| onesourcecloud.net | 4 | 4 | 2 | 2 |
| fluence.ai | 4 | 2 | 2 | 1 |
| cloud.google.com | 3 | 2 | 1 | 1 |
| deploybase.ai | 3 | 2 | 2 | 4 |
| getdeploying.com | 3 | 1 | 2 | 1 |
| forums.developer.nvidia.com | 2 | 2 | 2 | 2 |
| akamai.com | 2 | 2 | 1 | 3 |
| cudocompute.com | 2 | 2 | 2 | 3 |
| vcluster.com | 2 | 2 | 1 | 3 |
| linkedin.com | 2 | 2 | 1 | 4 |
What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.cloud-gpu-platforms-2026-07-27 / 4-cloud-gpu-platforms-2026-07-27-models-64232fd32583
Why this rank
Create a proof-of-concept scorecard for evaluating GPU clouds on real workload throughput, failure recovery, networking, and total cost. on Cloud GPU Platforms Prompt Brand Leaders
Derives prompt-level brand leaders, cross-engine brand-set disagreement, and leading-brand owned-citation opportunity from the complete public Rank.ai benchmark cohort.
9
Rank on this board
33.333 percent
Score
3
Answers behind the score
100% confidence · 1 of 1 components evidenced
What the score is built from
1 of 1 evidencedEvery component evidenced · 1 evidence record each
| Component | Value | Contribution |
|---|---|---|
| Leading brand consensus | 33.333333 | 33.333 |
Moved up since the previous snapshot
+2 places- Citation URLS3 to 4+1
- Presence events2 to 20
- Distinct reviewed brands2 to 20
- Engine disagreement66.667 to 66.6670
- Leading brand consensus33.333 to 33.3330
- Leading brand owned citation coverage33.333 to 33.3330
- Leading brand owned citation gap0 to 00
Where each number comes from
7 public recordsReviewed-brand presence eventsPresence events · observed Sep 17, 2026, 12:00 AM UTC2 presence_events100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Distinct citation URLsCitation URLS · observed Sep 17, 2026, 12:00 AM UTC4 urls100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Distinct reviewed brands surfacedDistinct reviewed brands · observed Sep 17, 2026, 12:00 AM UTC2 brands100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Cross-engine brand disagreementEngine disagreement · observed Sep 17, 2026, 12:00 AM UTC66.667 percent100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Leading-brand engine consensusLeading brand consensus · observed Sep 17, 2026, 12:00 AM UTC33.333 percent100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Leading-brand owned-citation coverageLeading brand owned citation coverage · observed Sep 17, 2026, 12:00 AM UTC33.333 percent100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
Leading-brand owned-citation opportunityLeading brand owned citation gap · observed Sep 17, 2026, 12:00 AM UTC0 percentage_points100% confidence
- Source
- Rank.ai Cloud GPU Platforms Prompt Benchmark
- Grade
- A
- Freshness
- fresh
- Locator
{"promptSlug":"cloud-gpu-proof-of-concept-scorecard","corpusVersion":"cloud-gpu-platforms-2026-07-27","runArtifactIds":["1292ed23-b3ee-446a-a00c-a9da79723573","f2554213-5548-4fc2-ba43-5f5a2614254a","87acbce0-3e5c-4376-9479-19a78ac5ffa6"],"parentSnapshotId":"83bdc888-6ae7-4cb1-8b8d-c037b9aa2951","providerMatrixVersion":"64232fd32583"}
More in Cloud GPU platforms
Specialist GPU clouds, marketplaces, and hyperscalers ranked across a fixed infrastructure buying corpus, with brand, prompt, engine and citation source views.
Reviewed providers ranked by the share of all the category's answers that name or cite them.
Reviewed providers ranked by distinct answer-level citations to their canonical domains.
See how often AI names you.
Enter your website. In about two minutes, Rank.ai asks ChatGPT, Claude and Gemini 12 questions your customers ask and grades how often they name you.
- Your grade out of 100How often AI names you, cites your site, and how high it ranks you.
- Who gets namedEvery competitor in the answers, most named first.
- The pages AI readsThe sources behind each answer.
- Three fixesWhat to fix first, with a brief for the first article.