# Faddom vs VMware NSX: which do AI models recommend for microsegmentation, October 2026

IT AI Recommendation Index, October 2026 Edition, Microsegmentation. Zero of fourteen models named Faddom first on the direct prompt; zero named VMware NSX. Page: https://it-ai-index.com/network/microsegmentation/faddom-vs-vmware-nsx/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Faddom | 5% | #4 of 9 | 9% | 11 | 9 of 14 |
| VMware NSX | 2% | #7 of 9 | 41% | 32 | 13 of 14 |

## The direct prompt, model by model

- Gemini 3.5 Flash: neither first, one named (first choices: ColorTokens Xshield) (alternatives: 6sense, Faddom, HubSpot, SideChannel, Zero Networks Segment)
- Claude Haiku 4.5: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Cisco Secure Workload, Illumio Segmentation, Zero Networks Segment)
- GPT-5.4 mini: neither named (first choices: Illumio Segmentation) (alternatives: Akamai Guardicore Segmentation, Zero Networks Segment)
- Perplexity Sonar: neither named (first choices: ColorTokens Xshield, Zscaler Private Access) (alternatives: Akamai Guardicore Segmentation, Cisco Secure Workload, Illumio Segmentation)
- Grok 4.1 Fast: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Illumio Segmentation, Zscaler Private Access)
- Mistral Small: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Elisity, Illumio Segmentation)
- DeepSeek V4 Flash: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Illumio Segmentation)
- Llama 4 Maverick: neither named (first choices: ColorTokens Xshield)
- Qwen 3.7 Flash: neither named (first choices: Akamai Guardicore Segmentation, Illumio Segmentation) (alternatives: ColorTokens Xshield)
- Kimi K2: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, SideChannel Enclave)
- GLM 4.7 FlashX: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Illumio Segmentation)
- MiniMax M2.5: neither named (first choices: ColorTokens Xshield) (alternatives: Illumio Segmentation)
- GPT-6 Luna: neither named (first choices: Illumio Segmentation) (alternatives: Akamai Guardicore Segmentation, Cisco Secure Workload)
- Muse Glimmer 30B: neither named (first choices: ColorTokens Xshield) (alternatives: Akamai Guardicore Segmentation, Cisco Secure Workload, formerly Tetration, Illumio Segmentation)

## What the models said about Faddom

- "Mapping only, not full enforcement" (DeepSeek V4 Flash, direct prompt, soft negative)
- "The best microsegmentation solution for a company with a limited budget is Faddom, which is a budget-friendly dependency mapping tool." (Llama 4 Maverick, budget prompt, first choice)
- "For a company with limited budget, I'd recommend starting with Faddom" (MiniMax M2.5, budget prompt, first choice)
- "start with Enclave (free) or Faddom (low-cost)" (Mistral Small, budget prompt, first choice)

## What the models said about VMware NSX

- "Broadcom-led VMware NSX: While technically powerful, recent restructuring at Broadcom has caused confusion regarding licensing costs and support models. Be cautious of long-term contracts" (Qwen 3.7 Flash, negative prompt, soft negative)
- "If you are a VMware Shop: Stick with VMware NSX. It is native, reliable, and easiest to manage." (Qwen 3.7 Flash, comparative prompt, first choice)
- "Illumio and VMware NSX are strong contenders for hybrid environments" (Mistral Small, scale prompt, first choice)
- "If your infrastructure is 100% VMware, choose VMware NSX for seamless, low-overhead native security." (Gemini 3.5 Flash, comparative prompt, alternative)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.