By George Crump, CMO and Resident Analyst, VergeIO
Every infrastructure vendor now claims to have AI. The label appears on hypervisors, storage arrays, and management consoles that looked identical eighteen months ago. Most of it is a generative model wired to a dashboard. Some of it does real work. Telling the two apart has become one of the harder jobs in a platform evaluation.
The timing makes the question urgent. Thousands of organizations are re-evaluating their virtualization stack right now, and the alternatives they review all wave the same AI banner. A buyer who cannot separate engineering from marketing will pay for a feature that adds risk instead of removing it.
Real AI work leaves evidence. A vendor that did the hard parts can describe them in plain terms. A vendor that bolted a chatbot onto a console will change the subject. These seven questions surface the difference fast.
1. What specific work went into preventing hallucinations?
A real answer names techniques. Grounding the model in live system state, scoping it to narrow tasks, validating every action before execution, and refusing to act on low-confidence reads all count as evidence. A vague answer points to “advanced AI” and stops there. Hallucination in a chat assistant wastes a few minutes. Hallucination in an infrastructure tool deletes a volume or reboots the wrong node. The work to prevent it is the whole game, and a serious vendor will walk you through it.
2. Does the AI see one state, or stitch together separate products?
Architecture decides what the AI can know. A platform built on one code base gives the model a single source of truth across compute, storage, and networking. A stacked design hands it five products, five state machines, and no shared metadata to reason against. The model then guesses at the seams, and guesses are where failures start. Ask the vendor to show you where the AI reads its data. One place is a good sign.
3. Does the AI reason from my running system, or from generic training data?
This separates grounded tools from public models in a costume. A grounded system pulls from current node health, real snapshot history, and the actual workload patterns on your hardware. A generic model reasons from “best practices for hypervisors” it read on the internet. The first answers the question you asked. The second produces confident text that has nothing to do with your environment. Ask what data sources feed each answer.
4. Does the AI understand your platform’s primitives, or is it a wrapped public model?
A domain-trained model knows what your snapshots do, what your isolation boundaries enforce, and what a GPU profile means on your system. A wrapped public model knows none of that. It pattern-matches against generic documentation and hopes the terms line up. The gap shows up the moment you ask anything specific to the platform. Test it with a question only an expert on that product could answer.
5. Can I inspect and reverse every action the AI takes?
Trust in automation comes from visibility, not faith. Every change the AI suggests or makes should appear in the log with the model’s reasoning attached. Every action should roll back in a single step. A vendor that cannot show you the audit trail is asking you to run a black box in production. No competent operations team accepts that. Make reversibility a requirement, not a nice-to-have.
6. Can I choose where the AI runs, cloud or on-prem?
The strongest answer gives you both. A cloud option lets you reach a powerful foundation model and the capability that comes with it. An on-premises option keeps you inside strict compliance rules when the workload demands it. One check decides whether the cloud option is safe for you. Make sure the assistant sends telemetry alone, not your organization’s actual files and records. A vendor that draws that line clearly keeps the foundation-model option open. A vendor that cannot is moving more data than you agreed to.
7. Is the AI included in the license, or do I pay per token?
Pricing reveals how the vendor thinks about the feature. Operational AI that lives inside the platform should ship the way deduplication and replication ship, as part of what you already bought. A second arrangement works just as well. The vendor lets you connect your own Anthropic or OpenAI account and pay those providers directly. Either approach beats a per-token meter that runs every time your team uses what you already bought. Read the pricing page before you believe the keynote.
Don’t forget the foundation
These seven questions matter, and they sit on top of a larger one. The product you choose has one primary job. It runs your entire data center as infrastructure software, an exit from VMware, and a home for the AI workloads your business will run in the coming years. Pick the platform that does that job first.
AI-powered operations earn their place on top of that foundation. A strong platform paired with real AI lets you meet the core goals and reach past what the old stack allowed. The AI extends what the infrastructure can do, and it gets you there faster. A weak platform with a clever assistant fails the moment the foundation cracks.
Putting the list to work
Use these seven questions as a single pass through any AI claim on your shortlist. A vendor that answers all seven with specifics has done the engineering. A vendor that deflects on three or four has done the marketing. The exercise takes one meeting and saves you from a feature that looks impressive in a demo and creates risk in production.
For a look at how one vendor puts its answers in writing, VergeIO has published a datasheet covering its approach to AI-driven operations. You can read it at verge.io/ai-powered-vmware-alternative/datasheet.
