The Nutanix Design Assistant: Here's How It Could Work for You

n March 2025, I introduced something I personally believed to be bold: a new kind of infrastructure co-pilot built on generative AI. The Nutanix Design Assistant my vision for how AI can understand, design, validate, and enforce complex hybrid/multicloud infrastructure all in plain English.

And while it’s still early days, here’s what it’s now capable of and how it could be deployed across a wide range of environments.

SaaS-Backed Assistant

Ideal for: Enterprises using Prism Central, Calm, Flow, and hybrid governance.

This is the default vision:

• Runs as a SaaS co-pilot, updated continuously.

• Connects directly to your Nutanix control plane (e.g., Prism Central).

• Interacts via GUI, Slack, or API to design DR plans, enforce policy, or run RCA graphs on command.

• Persona-aware interfaces adapt to Architects, FinOps, SRE, and SecOps users.

What this enables: You describe a goal “Make this DR-compliant and cost-optimized” and it builds, validates, and simulates it live.

Air-Gapped + Offline

Ideal for: Gov, energy, healthcare, or secure field deployments.

This assistant was designed with offline use in mind:

• Could run using a local LLM (Mistral 7B, LLaMA 2) embedded in Prism or on standalone edge hardware.

• Comes with pre-seeded Nutanix DSLs, mock APIs, and blueprint planners.

• Would allow users to simulate or pre-validate infrastructure plans without internet access.

• Ideal for training, DR prep, or blueprint iteration in disconnected sites.

Possible scenario: An oil rig runs DR simulations using a local GPT model that mirrors their actual Nutanix deployment.

Hugging Face + Fine-Tuned AI (Developer Pathway)

Ideal for: Data teams, MSPs, telcos, or anyone building custom assistants.

If you want to host your own version, it’s also being shaped to support:

• Fine-tuning via Hugging Face Transformers using Nutanix-specific DSLs.

• RAG-enabled blueprints using historical Prism logs, Flow policies, or cost patterns.

• Deployment via Hugging Face Inference Endpoints or in-cluster LLM hosts.

What’s possible: You could create “FinGPT” that only handles FinOps optimizations tuned on your real data.

MicroGPTs at the Edge (Concept Under Exploration)

Ideal for: Smart retail, edge AI, or hyper-constrained locations.

Here’s the vision:

• Run a lightweight GPT variant on edge compute (e.g., Nutanix CE, SmartNICs, retail sites).

• Handle local decisions cost, failure detection, micro-recovery without central orchestration.

• Periodically sync with Prism Central or SaaS GPT to learn from the fleet.

Imagine this: A single node detects network instability, analyzes impact, and launches a Flow policy locally autonomously.

The Bigger Picture

Infrastructure isn’t static and neither is AI. This assistant is architected for multi-modal deployment because your business isn’t one-size-fits-all.

• Want real-time design enforcement in SaaS? Done.

• Need offline simulation at an oil field or government base? It’s on the roadmap.

• Building your own GPT for FinOps, DR, or compliance? We’re enabling that path.

What’s Next

This assistant isn’t just a concept it’s already operational as a design and simulation tool within this GPT environment. While it hasn’t yet been deployed as a SaaS product, the building blocks are in place for multiple deployment paths: offline agents, Hugging Face integrations, and lightweight edge variants.

This is very much a work in progress but one with clear intent.

The next step? Figuring out which deployment model makes the most sense for your world.