In today’s private artificial intelligence ecosystem, architectures that combine Dell or HPE hardware, VMware Cloud Foundation virtualization, Kubernetes orchestration, NVIDIA acceleration, and enterprise storage are increasingly common. Each component is individually supported by its respective vendor, but when the entire system fails, the key question is not 'what broke?' but 'who should investigate first?' This dilemma, known as 'vendor ping-pong,' can paralyze operations for hours or days. To avoid it, having support contracts is not enough; an internal operational model that clearly defines diagnostic, escalation, and resolution responsibilities is required. That model is embodied in a specific support RACI matrix for multivendor private AI platforms.
The RACI matrix (Responsible, Accountable, Consulted, Informed) is a classic project management tool that, when applied to technical support, allows unambiguous assignment of who takes the first diagnostic action for a given symptom. In a multi‑vendor environment, the most common mistake is to wait until the root cause is known to assign responsibilities. The reality is that root cause can take hours to identify, while the first diagnosis must begin in seconds. Therefore, the support RACI is not based on 'who caused the failure,' but on 'who should look first.'
Imagine a Kubernetes node that stops advertising GPUs. The infrastructure team might think it is a Kubernetes problem; the Kubernetes team might think it is an NVIDIA driver problem; and the NVIDIA team might ask for hypervisor evidence. Without a RACI, each team opens a case with its vendor and the customer ends up being the messenger between them. The solution is to designate a 'first diagnostic owner' for each symptom. In this example, the Kubernetes and AI platform team is the first diagnostic owner because the symptom manifests at their layer. Their job is to collect evidence and determine whether the GPU is missing at the operating system level, the device plugin, or the runtime. Only after that analysis is it decided which vendor should open a case.
At Q2BSTUDIO, as a software development and technology company, we help our clients design these operational models for their AI platforms. We not only develop custom applications that integrate with these environments, but we also offer consulting services to define the support architecture. We know that a private AI platform is not operationally complete when the hardware is installed and the first endpoint responds; it is complete when the organization knows who performs the first diagnostic action for any failure. That is why we recommend that the customer retains a single service owner and an incident commander, while individual platform teams own first‑line diagnostics for their respective layers.
The RACI matrix must also distinguish between different types of responsibility: service accountability always lies with the customer; first diagnostic ownership lies with the team best positioned to isolate the failure at the layer where the symptom appears; fault ownership is assigned only after evidence determines the cause; case ownership identifies which organization has an open case with a vendor; and remediation ownership belongs to the team authorized to implement the fix. Confusing these concepts is one of the main sources of inefficiency in multivendor support.
A crucial aspect is evidence collection before opening cases with vendors. Each platform layer (hardware, virtualization, Kubernetes, NVIDIA software, storage, network) must have a diagnostic runbook specifying which logs, metrics, and commands to collect before escalation. For example, for a problem with an NVIDIA NIM endpoint, the platform team must inspect the full startup chain: workload scheduling, GPU allocation, container registry access, model cache state, network connectivity, etc. It is useless to open a case saying 'NIM failed'; one must identify whether the failure occurs during image download, model initialization, or service exposure. Similarly, for a distributed training performance problem, evidence must include GPU metrics, RDMA network data, storage latency, and CPU utilization. Only then are circular referrals between vendors avoided.
For platforms using public cloud such as AWS or Azure, complexity increases. Q2BSTUDIO offers cloud AWS/Azure services that include designing hybrid architectures where private AI is combined with elastic cloud resources. In these environments, the support RACI must include the cloud provider as another actor with its own diagnostic responsibilities. For instance, if an Azure Local node does not see a GPU, the Azure Local team (or the customer with Microsoft support) should be the first diagnostic owner, not the hardware manufacturer. Evidence must include Azure Local diagnostic logs before escalating to Dell or NVIDIA.
Cybersecurity also plays a fundamental role. A private AI platform handles sensitive data that must not leak into logs or AI prompts. Therefore, diagnostic runbooks must include procedures to sanitize information before sharing it with vendors. Q2BSTUDIO integrates cybersecurity and pentesting services in its AI projects, ensuring that evidence does not expose business secrets. In addition, the use of AI agents and Business Intelligence solutions with Power BI can automate part of the monitoring and event correlation, reducing diagnosis time.
In short, implementing a support RACI for multivendor private AI is not a theoretical exercise. It is an operational necessity that prevents productivity losses and reduces incident resolution time. Practical steps include: defining the service boundary (what the AI platform includes), naming an accountable service owner, assigning first diagnostic owners for each symptom class, creating a version and compatibility register, establishing a known‑good configuration baseline, developing layer‑specific evidence runbooks, and testing the RACI through tabletop exercises. At Q2BSTUDIO we accompany organizations through this process, combining our experience in custom software development, cloud integration, cybersecurity, BI, and AI agents to ensure technology works not only when everything is healthy, but also when something fails.
