What is model serving isolation and why is it critical in multi-tenant MLOps systems?

Model serving isolation ensures that workloads from different tenants or models do not interfere with each other’s performance or reliability.

In multi-tenant ML systems, multiple models or customers share the same infrastructure. Without isolation, one heavy workload can degrade latency, throughput, or even crash other services (noisy neighbor problem). Isolation is achieved through container boundaries, dedicated model replicas, resource quotas (CPU/GPU/memory limits), and separate inference pools. Advanced setups use Kubernetes namespaces, node affinity, and GPU partitioning (MIG) to guarantee predictable performance. Isolation is also required for security and compliance in regulated environments.