Eleven of fourteen models named Google Kubernetes Engine first on the direct prompt; zero named Linode Kubernetes Engine. Google Kubernetes Engine was named by fourteen of the fourteen models and Linode Kubernetes Engine by twelve and Google Kubernetes Engine carries 77 labels and Linode Kubernetes Engine 20, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the managed kubernetes platforms page.
Across every category in the November 2026 Edition, Google Kubernetes Engine and Linode Kubernetes Engine were named in the same answer fifty-two times, of the 227 answers naming Google Kubernetes Engine and the 59 naming Linode Kubernetes Engine. In those answers Linode Kubernetes Engine took the first choice one time and Google Kubernetes Engine sixteen.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“What to Avoid (The "Expensive" Options) ... AWS EKS, Google GKE, and Azure AKS ... budget killers” Qwen 3.7 Flash · budget prompt · hard negative
“You need privileged containers, node-level access, or fine-grained control over node infrastructure. Autopilot deliberately restricts some of these” GPT-6 Luna · negative prompt · soft negative
“GKE is deeply integrated with Google Cloud's specific networking and observability tools. Moving workloads off GKE can be difficult.” GLM 4.7 FlashX · negative prompt · soft negative
“the recommended hosted Kubernetes service for a mid-sized B2B company is Google Kubernetes Engine (GKE), as it is the clear leader with 43% of first choices” Llama 4 Maverick · paraphrase prompt · first choice
“If you want the most automation out of the box and strong ML/AI integrations, GKE is the most recommended choice in current AI model consensus.” Muse Glimmer 30B · direct prompt · first choice
“Often ranked as the best overall for reliability, monitoring, and ease (especially Autopilot mode, which is more "hands-off").” Grok 4.20 · negative prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Mid-tier cloud providers are excellent for developer side-projects... but they carry distinct limitations for large-scale production” Gemini 3.5 Flash · negative prompt · soft negative
“Smaller Cloud Providers (DigitalOcean, Linode, etc.)... they lack the enterprise maturity of the hyperscalers” GLM 4.7 FlashX · negative prompt · soft negative
“Smaller/Niche Providers (DigitalOcean, Linode, Vultr, etc.) — ⚠️ Depends on Use Case” DeepSeek V4 Flash · negative prompt · soft negative
“is DigitalOcean Kubernetes (DOKS) or Linode Kubernetes Engine (LKE). Both offer a free control plane and nodes starting at $12/month.” Llama 4 Maverick · budget prompt · first choice
“Linode LKE for a free control plane with price stability” Muse Glimmer 30B · budget prompt · first choice
“Lightweight managed Kubernetes offerings designed for simplicity, ease of use, and predictable pricing.” Gemini 3.5 Flash · comparative prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.