In [[multi-tenancy]] environments, one tenant's workload spike degrades performance for everyone else. That's the noisy neighbor problem. The problem compounds with latency-sensitive inference and bursty agentic workloads - making isolation infrastructure a critical enterprise decision. ![[Screenshot 2026-02-14 at 00.16.58.png]] Shared infrastructure pools resources across customers. Works great when everyone's load is light. Falls apart when one tenant starts hammering GPUs or saturating bandwidth. The result: unpredictable latency, enforced rate limits, and performance that's out of your control. ![[Screenshot 2026-02-05 at 00.31.07.png]] This matters more for [[Inference]] than it did for traditional cloud workloads. Inference is latency-sensitive. A 200ms spike in a batch processing job is invisible. A 200ms spike during real-time model serving kills user experience. ![[Screenshot 2026-02-05 at 00.31.16.png]] The problem compounds with agentic workloads. Agents are bursty by design: they chain multiple model calls, hit tools, wait, then fire again. Traditional capacity planning assumes smooth demand curves. Agents produce spiky, unpredictable ones. One neighbour running a complex agent workflow can destabilize inference quality for everyone else on shared infrastructure. ![[Screenshot 2026-02-14 at 00.19.19.png]] Solutions fall into three buckets: - Logical isolation (separate queues, dedicated resources within shared infra) - Physical isolation (dedicated hardware per tenant, more expensive) - Managed isolation (vendor-managed dedicated infrastructure, the middle path) The trend in enterprise AI deployment is moving toward managed isolation. You get the operational simplicity of SaaS without the performance lottery of shared resources. See [[Inference is Not Your Differentiator]] for the broader pattern. --- Links: [[multi-tenancy]] | [[Inference]] | [[AI Inference Infrastructure]] | [[Agentic Inference]] | [[Data Center MoC]] Tags: #deeptech #systems