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Managed Kubernetes platforms · November 2026 Edition
DigitalOcean Kubernetes vs Civo Kubernetes
Zero of fourteen models named DigitalOcean Kubernetes first on the direct prompt; zero named Civo Kubernetes. DigitalOcean Kubernetes was named by fourteen of the fourteen models and Civo Kubernetes by eight and DigitalOcean Kubernetes carries 42 labels and Civo Kubernetes 10, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share24%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate12%10%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#6A position in a field of 12; printed, not drawn.
Labels4210A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, DigitalOcean Kubernetes reading right to left. Rank and label count are printed, not drawn.Google Kubernetes Engine was named alongside these two in twelve of the fourteen direct answers. Google Kubernetes Engine vs DigitalOcean Kubernetes · Google Kubernetes Engine vs Civo Kubernetes · DigitalOcean Kubernetes vs Azure Kubernetes Service
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.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
DigitalOcean KubernetesFirst choices, of fourteen modelsCivo Kubernetes
Direct00
Paraphrase00
Comparative00
Budget-constrained111
Scale-constrained00
Negative005 against DigitalOcean Kubernetes · 1 against Civo Kubernetes
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Across every category in the November 2026 Edition, DigitalOcean Kubernetes and Civo Kubernetes were named in the same answer twenty-five times, of the 117 answers naming DigitalOcean Kubernetes and the 28 naming Civo Kubernetes. In those answers Civo Kubernetes took the first choice three times and DigitalOcean Kubernetes thirteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where DigitalOcean Kubernetes and Civo Kubernetes stood in it.
| 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 | |
DigitalOcean Kubernetes Civo Kubernetes first choice named as an alternative argued againstblank: not namedEach cell is one answer, DigitalOcean Kubernetes on the left and Civo Kubernetes on the right.The direct prompt
The plain question, one answer per model, grouped by where DigitalOcean Kubernetes and Civo Kubernetes stood in it.
Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Perplexity SonarGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, DigitalOcean Kubernetes
DeepSeek V4 FlashGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, DigitalOcean Kubernetes
Llama 4 MaverickGoogle Kubernetes Engine alternatives: Amazon EKS, Atmosly, DigitalOcean Kubernetes
Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5no first choice
GPT-5.4 miniGoogle Kubernetes Engine alternatives: Amazon EKS
Gemini 3.5 FlashGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, Porter, Qovery
Grok 4.20Google Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, Red Hat OpenShift
Mistral SmallGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, Northflank, Red Hat OpenShift
Qwen 3.7 FlashGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, Rancher, Red Hat OpenShift
Kimi K2Google Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service
GLM 4.7 FlashXAzure Kubernetes Service alternatives: Amazon EKS, Google Kubernetes Engine, Rancher
MiniMax M2.5no first choice
GPT-6 LunaGoogle Kubernetes Engine alternatives: AKS Automatic, Amazon EKS
Muse Glimmer 30BGoogle Kubernetes Engine alternatives: Amazon EKS, Azure Kubernetes Service, Mirantis Kubernetes Engine, Rancher, Red Hat OpenShift, VMware Tanzu
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.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
DigitalOcean Kubernetes leads by fifty-eight points.
DigitalOcean Kubernetes62%#1 of 14Civo Kubernetes4%#3 of 14Mid-marketThe figures above
DigitalOcean Kubernetes leads by twenty-two points.
DigitalOcean Kubernetes24%#2 of 12Civo Kubernetes2%#6 of 12Enterprise
Level: the same share of first choices.
DigitalOcean Kubernetes0%#– of 8Civo Kubernetes0%#– of 8What the models said about DigitalOcean Kubernetes
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“it lacks the extensive ecosystem of tools and support channels of AWS/GCP... not suitable for mission-critical enterprise applications” GLM 4.7 FlashX · negative prompt · soft negative
“Be cautious if you: Need advanced networking features... Be most wary of smaller clouds with narrow version support” Kimi K2 · negative prompt · soft negative
“DOKS documents those limits and warns against modifying its managed components.” GPT-6 Luna · negative prompt · soft negative
“the most cited practical picks are DigitalOcean Kubernetes for a managed, simple start, and Linode LKE for a free control plane with price stability” Muse Glimmer 30B · budget prompt · first choice
“The best managed Kubernetes platform for a company with a limited budget is DigitalOcean Kubernetes (DOKS) or Linode Kubernetes Engine (LKE).” Llama 4 Maverick · budget prompt · first choice
“For strictest budget, DigitalOcean Kubernetes offers the best value with zero control plane costs and affordable node pricing.” MiniMax M2.5 · budget prompt · first choice
What the models said about Civo Kubernetes
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five 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
“Civo offers the best value with a free HA control plane, no egress fees, and very competitive worker node pricing” Kimi K2 · budget prompt · first choice
“Very cost-sensitive teams wanting rapid cluster creation | Smaller ecosystem and less mature enterprise feature set” Perplexity Sonar · budget prompt · alternative
“Other options include Civo, Vultr, and Scaleway Kapsule, which are also budget-friendly” Llama 4 Maverick · budget prompt · alternative
Also compared
Google Kubernetes Engine vs DigitalOcean KubernetesGoogle Kubernetes Engine vs Civo KubernetesDigitalOcean Kubernetes vs Azure Kubernetes ServiceDigitalOcean Kubernetes vs Amazon EKSDigitalOcean Kubernetes vs Linode Kubernetes EngineAzure Kubernetes Service vs Civo KubernetesAmazon EKS vs Civo KubernetesLinode Kubernetes Engine vs Civo KubernetesAll fifteen head-to-head pages and the full managed kubernetes standing
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.