Multitenancy in AI Clouds: Balancing Scale, Security & Performance
As enterprises expand their AI initiatives, one recurring challenge emerges: how to enable multiple teams or customers to share powerful AI infrastructure without sacrificing performance, security, or governance. At the heart of large-scale AI adoption lies a fundamental architecture decision: how do you build a multitenant cloud that serves many while still delivering the isolation and consistency that mission-critical workloads demand?
Why Multitenancy Matters in AI Infrastructure
Current trends make a compelling case for why multitenancy matters at this moment. McKinsey's 2025 survey reports that 78% of organizations already use AI data intelligence in at least one business function, up from 72% in early 2024. Adoption is growing across IT, marketing, and service operations, amplifying the pressure to serve more users and workloads on the same fleets. Meanwhile, a Microsoft empirical study across 400 real production jobs found average GPU usage at ~50% or less, meaning that smarter sharing and scheduling could reclaim substantial capacity without buying a single new GPU.
These statistics reflect the dual opportunity and risk of shared infrastructure. On one hand, sharing infrastructure across multiple tenants dramatically improves cost-efficiency, utilization, and scalability. On the other hand, inadequate isolation or poor scheduling in multitenant AI clouds leads to unpredictable performance, resource contention (the "noisy neighbour" problem), and governance and compliance risks.
Introducing Ori’s Approach: Secure Multitenancy with Ori AI Fabric
Secure multitenancy isn't just namespaces; it's a platform-wide philosophy spanning full-stack isolation (compute, storage, and networking), predictable performance (so one tenant can't starve another), centralized governance (identity, quotas, policy, audit, and cost attribution), and sovereignty controls (from data residency to air-gapping). Modern hardware helps. NVIDIA cloud providers offer Multi-Instance GPU (MIG) technology that can carve a physical GPU into isolated instances with guaranteed QoS and fault isolation, so one application failure cannot impact another running alongside it—making safe colocation feasible for many real-time inference and AI model training tasks. That philosophy is codified in Ori's Secure Multitenancy: a framework that lets multiple tenants share AI clusters without compromising isolation, performance, or governance.
Through Ori AI Fabric, we provide three tenancy modes, each calibrated for different balance points between resource efficiency, performance consistency, and isolation:
| Mode | Description | Benefits | Ideal for |
|---|---|---|---|
| Soft | GPU nodes are shared between tenants, with namespace-level isolation | Maximizes cluster utilization; end-customers control compute usage | Different teams or projects within a single organization where cost-effectiveness is key |
| Strict | Entire GPU node(s) reserved for a tenant with ring-fenced compute, storage and NVIDIA Infiniband networking | No workload sharing; predictable performance without “noisy neighbour” effect | Distinct external customers or business-critical workloads |
| Private | Dedicated, single-tenant deployment plus independen platform management capabilities | Ultimate level of isolation and data privacy; full control over underlying management | Organizations with strict data-residency or air-gapped regulatory requirements |
Isolation, performance, sovereignty, dialed in per workload
Multitenancy only works if boundaries hold. In Ori's Strict and Private modes, the platform enforces full-stack isolation, not just logical namespaces across compute, storage, and networking, while Soft maximizes fleet-wide efficiency when colocation risks are lower.
Strict multitenancy uses node-level reservations and segregated network/storage so allocations are restricted to the assigned tenant(s).
Tenancy reservations for GPU nodes in Strict Multitenancy
Private multitenancy goes further by delivering a self-contained instance of the Ori Cloud Operating System to provision, schedule, and manage resources independently in particular Private location(s), while the global plane provides a unified operator view. Additionally, you get the benefits of isolated compute, storage, and networking similar to Strict Multitenancy, and these locations are only visible and accessible to the authorized organizations.
Private Locations with isolated resources and independent management
Choosing the right tenancy mode for AI workloads
Think of tenancy modes as operational gears:
Start with Soft Tenancy when the goal is to squeeze idle GPU cycles and accelerate internal teams. MIG partitions keep small inference jobs neatly isolated while driving cluster utilization up;
Graduating to Strict is straightforward when SLOs tighten or contractual terms prohibit node colocation; ring‑fenced nodes neutralize noisy neighbors and make performance guarantees easier to uphold.
Finally, adopt Private when residency or operational separation is non‑negotiable for the tenant —say, a regulated customer, public-sector organization, a sovereign region, or an air‑gapped facility.
Another significant advantage of Ori’s Private Multitenancy is flexible cluster partitioning: large GPU clusters can be carved and re‑carved into Private locations, by team, by geography, or even by purpose (AI model training vs. real-time inference) so every team stays private while the organization as a whole makes better use of the fleet.
Flexible cluster partitioning among tenants within an organization
Benefits of Secure Multitenancy in Ori AI Fabric
- Optimized GPU Usage: Share high-performance infrastructure efficiently across teams or customers, maximizing ROI without idle compute.
- Powerful Isolation: Strict and Private tenancy modes ensure complete tenant data separation across compute, storage, and networking, eliminating "noisy neighbor" effects.
- Data Sovereignty and Compliance: Dedicated control planes and air-gapped options support regulatory and residency requirements for sensitive workloads.
- Operational Flexibility: Dynamically partition clusters for AI model training, real-time inference, or experimentation—securely and without downtime.
- Performance Consistency: Predictable throughput and latency across shared environments, supported by GPU-aware scheduling and network segmentation.
- Scalability by Design: Expand seamlessly from single-team to enterprise-wide AI infrastructure without re-architecting.
- Enhanced AI Service Delivery: Improve the efficiency and reliability of AI services across multiple tenants.
Make the most of your infrastructure without compromising on performance and security
Multitenancy is how you raise fleet utilization, protect every workload from noisy neighbors, and prove governance and sovereignty at scale. However, the key is to enable your teams and customers to choose the right boundary for each job and backing those choices with hardware partitioning, network/storage fencing, and a strong, layered control plane. That is precisely what Ori’s Secure Multitenancy and Ori AI Fabric provide: a practical, production-hardened way to share clusters confidently, without compromising isolation, performance, or governance.
Our multitenant platform offers advanced AI infrastructure orchestration and management capabilities, ensuring efficient GPU cloud orchestration and dynamic resource allocation. This approach not only optimizes AI data intelligence workflows but also streamlines AI infrastructure management across diverse workloads and tenant requirements.
Want to see how multitenancy works in practice? Let us walk you through it.
