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Interactive 3D/Multi-Tenant ML Platform Architecture
Namespace Isolation - Shared ClusterShared Kubernetes ClusterShared Control Plane (API Server, etcd, Scheduler)ns/tenant-aPodsServicesStoragens/tenant-bPodsServicesStoragens/tenant-cPodsServicesStorageNetworkPolicy enforces inter-namespace isolationShared: Monitoring · Service Mesh · Ingress · GPU Node Pool
Namespace Isolation: Shared Kubernetes cluster, separate namespaces per tenant. Network policies enforce isolation.
+ Good isolation, faster provisioning, moderate cost savings vs full isolation
- Noisy neighbor still possible for CPU bursts, shared control plane
Controls
Isolation Level
Tenant A - Resource Quotas
CPU30%
GPU40%
MEMORY40%
STORAGE30%
Options
Noisy neighbor: one tenant's burst uses cluster-shared resources (CPU steal, memory pressure) and slows others.

Chargeback: attribute shared infra costs back to tenants by actual usage. Requires metering.

Multi-Tenant ML Platform Architecture - Interactive Visualization

Multi-tenant ML platforms must serve multiple organizations or teams from shared infrastructure while maintaining data isolation, fair resource allocation, and cost attribution. Three isolation strategies exist: full cluster isolation (dedicated hardware per tenant, highest cost), namespace isolation (shared Kubernetes cluster with NetworkPolicy boundaries), and resource quota isolation (shared everything with CPU/GPU/memory quotas enforced per namespace). The noisy neighbor problem - where one tenant's burst degrades others - is the key challenge in shared environments. Chargeback metering enables per-tenant cost attribution for internal platforms.

  • Full isolation: dedicated clusters per tenant, zero noisy neighbor risk, 3-5x higher cost than shared
  • Namespace isolation: shared Kubernetes cluster, NetworkPolicy enforcement, still vulnerable to control plane contention
  • Resource quotas: CPU/GPU/memory hard limits per namespace, highest utilization, requires quota tuning
  • Noisy neighbor: one tenant's GPU burst starves others - quotas prevent this but require careful sizing
  • Chargeback: metering GPU hours, CPU seconds, storage, and inference requests per tenant for cost attribution
  • Feature store isolation: each tenant needs separate offline and online feature stores to prevent data leakage

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.