Build an AI infrastructure landing zone for secure, repeatable GPU access across teams with standardized compute, storage, networking, quotas, and operations.
Giving one team access to a GPU is straightforward. Give them a GPU-enabled VM, attach storage, configure network access, and let them start building.
Giving many teams reliable, secure, and repeatable access is an architecture problem.
As AI adoption spreads across an organization, infrastructure teams need consistent answers: Who can provision GPUs? How are teams isolated? Where does data live? How is consumption measured? Who owns the platform?
An AI infrastructure landing zone answers those questions before workloads arrive. It creates a standardized foundation so teams consume GPUs, compute, storage, networking, and orchestration through known patterns instead of one-off environments.
What Is an AI Infrastructure Landing Zone?
An AI infrastructure landing zone is a standardized infrastructure environment that defines how teams access and consume AI resources.
It typically establishes:
- Compute and GPU access
- Tenant and workload boundaries
- Networking and storage
- Identity and access controls
- Resource quotas
- Security controls
- Observability and metering
- Automation and deployment standards
- Operational ownership
The goal is not to slow developers down. It is to make AI infrastructure repeatable.
When a new team requests GPU capacity, its network, permissions, storage, monitoring, and deployment process should already exist as reusable patterns.
Why GPU Access Alone Does Not Scale
Early AI projects are often provisioned manually. A team receives a GPU VM. An engineer creates a Kubernetes cluster. Storage is attached. Network rules and credentials are configured case by case.
That works for a few experiments. At scale, it creates infrastructure drift:
- Similar teams receive different environments.
- GPU consumption becomes difficult to control.
- Security and isolation vary by project.
- Teams duplicate clusters and storage.
- Capacity conflicts appear without clear priorities.
- Development environments become production by accident.
- Troubleshooting depends on how each environment was assembled.
- Ownership becomes unclear.
The problem is no longer getting a GPU. It is defining a consistent way for many teams to consume scarce infrastructure.
Define Tenant and Workload Boundaries
A multi-team AI platform needs an explicit isolation model.
Boundaries may use OpenStack projects, separate Kubernetes clusters, Kubernetes namespaces, dedicated compute pools, or dedicated and shared GPU pools.
A research environment may tolerate more shared infrastructure. A customer-facing inference service may require stronger isolation. Sensitive datasets may justify separate network or storage boundaries.
The appropriate model depends on security, performance, compliance, team autonomy, and cost allocation.
GPU isolation is one part of this decision. Organizations comparing full-GPU assignment, partitioning, and sharing can refer to VEXXHOST's guide to shared vs. dedicated GPUs for enterprise AI rather than treating that choice as the entire landing-zone architecture.
Connect Identity to Infrastructure Access
Shared AI infrastructure should not depend on shared administrator credentials or manually maintained access lists.
A landing zone should connect infrastructure access to centralized identity and define roles for administrators, platform engineers, developers, ML/MLOps teams, and service accounts.
OpenStack can scope infrastructure permissions through projects and role-based access. Kubernetes adds RBAC for clusters, namespaces, workloads, and service accounts.
The principle is least privilege: a developer deploying an inference workload should not automatically be able to modify the underlying GPU nodes or cloud network.
Put GPU Quotas and Resource Controls in Place
GPUs are scarce infrastructure resources. Their allocation should be explicit.
A landing zone can define GPU and VM quotas, Kubernetes resource requests and limits, GPU-specific node pools, scheduling policies, dedicated versus shared accelerator pools, and capacity reservations for critical workloads where appropriate.
The platform should also show which project, namespace, or business unit is consuming capacity, creating a predictable resource model for experimentation, training, and production.
Standardize the Network Architecture
Networking should be designed as part of the landing zone instead of rebuilt independently for each project.
Standards may cover private networks, tenant segmentation, security groups, external ingress, load balancing, Kubernetes networking, east-west connectivity, API access, and connectivity to data sources.
Training and inference can create very different traffic patterns. Distributed training may generate substantial east-west traffic between workers and storage, while inference often depends more on predictable request paths and load balancing.
For environments where network topology is becoming part of GPU performance, VEXXHOST's guide to AI networking for GPU infrastructure covers those considerations in more depth.
Treat Storage and Data Placement as Foundation Components
A GPU is useful only if the workload can reach its data.
The landing zone should define approved patterns for datasets, model artifacts, checkpoints, Kubernetes persistent volumes, object data, shared files, and backups.
Different workloads may require different interfaces. Kubernetes applications may use persistent block volumes. Training pipelines may use object storage. Some workflows require shared file access.
Ceph can provide block, object, and file storage within a distributed storage layer, making it one option for environments that need multiple storage interfaces.
The objective is to standardize available storage patterns, durability expectations, and how teams request them—not force every workload onto one tier.
Publish Standard Compute and GPU Profiles
Unlimited configuration choice creates infrastructure drift.
Platform teams can publish approved profiles such as:
- Development GPU VM
- Memory-heavy GPU VM
- Standard training worker
- Production inference node
- Dedicated GPU Kubernetes node pool
- CPU-only preprocessing node
Profiles can define CPU-to-GPU ratios, memory, storage, networking, GPU allocation, and operational expectations.
Standard profiles make environments easier to automate, troubleshoot, document, and include in capacity planning.
Build the Landing Zone as Code
A landing zone that exists only in an operations document will eventually drift.
Infrastructure should be reproducible through automation. Depending on the environment, that may include Terraform or similar infrastructure-as-code tooling, Kubernetes manifests, GitOps workflows, automated cluster provisioning, standardized images, and declarative network and storage configuration.
The exact toolchain is secondary to the principle: the same approved request should produce the same infrastructure repeatedly.
Separate Development, Testing, and Production
Experimental AI infrastructure should not become production infrastructure by default.
Landing zones should define different expectations for development, testing, and production across availability, security, GPU allocation, storage durability, access, monitoring, change control, and cost sensitivity.
Development may prioritize flexible access and sharing. Production inference may require stricter change control, stronger isolation, persistent monitoring, and reserved capacity. Long-running training may need checkpointing and scheduling policies designed around extended jobs.
Establish Observability and Metering Before Consumption
Central observability becomes much harder after every team has built its platform differently.
Before broad access is enabled, infrastructure teams should be able to see:
- GPU utilization and memory
- CPU and system memory
- Kubernetes workload and compute-node health
- Storage performance and capacity
- Network utilization and errors
- Available GPU capacity
- Resource consumption by tenant or team
Metering is especially important when groups compete for the same GPU pool. Without shared visibility, capacity decisions become guesswork.
Decide Who Operates the Platform
A landing zone should also define operational ownership.
Responsibilities may span infrastructure, platform, ML/MLOps, application, and security teams. The operating model should assign responsibility for GPU capacity, monitoring, platform upgrades, Kubernetes lifecycle, storage operations, security updates, incident response, and capacity expansion.
What matters is that ownership is defined before an incident exposes the gap.
OpenStack and Kubernetes Are Complementary Layers
OpenStack and Kubernetes solve different parts of AI infrastructure architecture.
OpenStack can provide the infrastructure layer:
- Compute and GPU-enabled virtual machines
- Networking and storage integration
- Tenant isolation
- Quotas
- Infrastructure APIs
Kubernetes can provide the workload orchestration layer:
- Container and GPU scheduling
- Application lifecycle
- Services
- Namespaces
- Platform-level resource policies
Used together, OpenStack can define how infrastructure is provisioned and isolated, while Kubernetes controls how containerized AI workloads consume resources.
Organizations that do not want to operate the Kubernetes layer themselves can also use Managed Kubernetes while retaining standard Kubernetes APIs and an open infrastructure model.
Practical AI Infrastructure Landing Zone Checklist
Before expanding GPU access, verify.
If these decisions are still being made manually for each new AI project, workloads may be scaling faster than the infrastructure foundation supporting them.
Build the Foundation Before Expanding Access
An AI infrastructure landing zone needs to make the important infrastructure decisions repeatable.
That means defining who can consume GPUs, which environments they receive, how networks and storage connect, how resources are controlled, how infrastructure is reproduced, and who operates it once deployed.
VEXXHOST can support this foundation through GPU Infrastructure, OpenStack, Managed Kubernetes, Ceph, and private cloud environments. Teams that prefer not to own every Day 2 responsibility can also use managed infrastructure services for ongoing operations.
The goal is not simply more GPU access. It is a consistent, supportable infrastructure model that lets multiple teams consume AI resources without turning every new workload into a new architecture project.
Planning to expand GPU access across teams? Talk to VEXXHOST about the infrastructure architecture, deployment model, and operating requirements behind your AI workloads.
