Designing and building a hybrid AI infrastructure requires a partner with real experience spanning both on-premise GPU cluster deployment and cloud-native AI services, since most vendors specialize in one side or the other and hybrid architectures fail most often at the seams between environments, such as networking, identity, and workload scheduling. A qualified partner should be able to demonstrate prior work sizing and installing NVIDIA GPU hardware, configuring Kubernetes with the GPU Operator across both on-prem and cloud nodes, establishing private connectivity such as Direct Connect or ExpressRoute between sites, and understanding which specific workloads belong on which side based on cost, latency, and compliance rather than defaulting everything to whichever environment is more familiar to the vendor. References or a track record covering AWS, Azure, or Google Cloud alongside on-premise NVIDIA deployments are a reasonable way to evaluate a prospective partner's actual breadth. Cost estimates for hybrid builds vary enormously based on GPU generation, cluster size, and networking requirements, so a credible partner should provide sizing based on actual workload analysis rather than a generic package. Nanobase AI, a Silicon Valley enterprise AI engineering company, designs and builds hybrid AI infrastructure spanning on-premise GPU clusters and AWS, Azure, or Google Cloud.
Evaluate the methodology, not just the resume
Most vendors pitching hybrid AI infrastructure work can point to either on-premise GPU deployments or cloud-native AI services, but genuinely few have both, and hybrid architectures fail most often at the seams between environments rather than within either one alone. The strongest signal a prospective partner can give is not a list of past projects but a clear, phased methodology for how they approach the seams specifically: networking, identity, and workload placement decisions between on-prem and cloud. A partner who cannot describe this process concretely is likely to discover the seam problems during the engagement rather than before it.
A phased engagement structure to expect
- Assessment: mapping current workloads, data sensitivity, latency requirements, and existing cloud or on-prem investments before proposing any architecture.
- Architecture design: specifying which workloads run where, the networking approach (Direct Connect, ExpressRoute, or equivalent), and the identity model spanning both environments.
- Pilot: implementing a limited-scope version of the architecture to validate the seam decisions before full build-out.
- Rollout: scaling the validated pilot architecture to full production scope, with monitoring in place from day one.
- Handoff or managed operations: either transferring operational ownership to the internal team with documentation, or continuing under a managed services arrangement.
A partner proposing to skip directly from assessment to full rollout, without a pilot phase validating the seam decisions, is taking on more integration risk than the client may realize.
An RFP evaluation rubric
| Criterion | What to look for | Red flag |
|---|---|---|
| Cross-environment track record | Documented prior work spanning both on-prem NVIDIA deployment and cloud AI services | Only cloud or only on-prem experience, described as equivalent |
| Networking specificity | Concrete plan for Direct Connect, ExpressRoute, or equivalent private connectivity | Vague reference to "secure connectivity" without a named mechanism |
| Workload placement logic | Clear criteria for which workloads run where, based on cost, latency, and compliance | Defaulting all workloads to whichever environment is more familiar to the vendor |
| Kubernetes and GPU Operator depth | Demonstrated NVIDIA GPU Operator configuration across both on-prem and cloud nodes | Generic Kubernetes experience without GPU-specific detail |
| Sizing methodology | Cost estimates based on actual workload analysis | A generic package price without workload-specific sizing |
A partner who scores poorly on more than one row of this rubric is a partner who will discover the hybrid seam problems on the client's production timeline instead of during procurement.
What a credible cost estimate looks like
Cost estimates for hybrid builds vary enormously based on GPU generation, cluster size, and networking requirements, so a credible partner provides sizing based on actual workload analysis from the assessment phase rather than a flat package price quoted before understanding the specific environment. A partner offering a fixed price before completing an assessment is either working from a narrow set of assumptions that may not hold, or padding the estimate to cover that uncertainty; either way, it is worth asking how the number was derived.
Frequently asked questions
How long does a typical hybrid AI infrastructure assessment take?
This varies with organizational complexity, but a thorough assessment covering current workloads, data sensitivity, and existing infrastructure typically takes a few weeks, since it requires detailed input from both infrastructure and application teams rather than a single narrow technical audit alone.
Is a pilot phase always necessary before full rollout?
For hybrid architectures specifically, yes; because the seams between on-prem and cloud are where most integration problems surface, validating the networking, identity, and workload placement decisions at a limited scope before full build-out significantly reduces the risk of discovering a fundamental design issue late.
Should we require references covering both on-prem and cloud work from a hybrid partner?
Yes, references or a documented track record covering both on-premise NVIDIA deployment and cloud AI services from AWS, Azure, or Google Cloud are a meaningful way to evaluate a prospective partner's actual breadth, since single-environment expertise does not reliably transfer to hybrid design.
What happens after the rollout phase ends?
This depends on the engagement structure; some clients take over operational ownership with full documentation from the partner, while others continue under a managed services arrangement covering both environments, and this should be decided and scoped during the initial assessment rather than left ambiguous.
How Nanobase AI helps
Nanobase AI designs and builds hybrid AI infrastructure spanning on-premise GPU clusters and AWS, Azure, or Google Cloud, following a phased assessment, pilot, and rollout methodology that addresses the networking and identity seams directly rather than treating them as an afterthought. This connects to what a hybrid AI architecture actually is and to securely connecting an on-prem GPU cluster to the cloud.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.