Multi-Tenancy · Inference Infrastructure

The Noisy Neighbor Problem

In shared infrastructure, one tenant's workload spike degrades performance for everyone else. The problem compounds with latency-sensitive inference and bursty agentic workloads — making isolation architecture a critical enterprise decision.

200ms
latency spike invisible in batch,
catastrophic in real-time serving
~10×
burst factor for agentic
workloads vs. steady-state
Resource Contention
Steady State
Load is light — resources distribute evenly
Shared pool works well when tenants consume proportionally. Predictable latency, no throttling.
Tenant A
~25% GPU
Tenant B
~20% GPU
Tenant C
~30% GPU
Tenant D
~15% GPU
Under Contention
One tenant spikes — everyone degrades
Tenant A hammers GPUs. Remaining tenants hit rate limits, enforced throttling, unpredictable latency.
Tenant A
SPIKE — 72% GPU
Tenant B
Throttled
Tenant C
Rate limited
Tenant D
Queued
Why Inference Is Different
Traditional Cloud
Batch Processing
Latency spikes are absorbed — invisible to end users
+200ms
Spike absorbed by batch window — no user impact
AI Inference
Real-Time Model Serving
Same spike is directly felt — kills user experience
+200ms
User-facing latency spike — timeouts, degraded completions, dropped requests
Agentic Workloads Compound the Problem
The Compounding Factor
Agents are bursty by design
Chained Model Calls
Agents fire multiple sequential inference calls — think, act, observe, repeat. Each chain multiplies load unpredictably.
Spiky Demand Curves
Traditional capacity planning assumes smooth distribution. Agents produce spikes with long idle gaps — worst case for shared pools.
Tool Call Amplification
Agents hit tools, wait for responses, then fire again. One complex workflow can destabilize inference quality for all co-tenants.
Unpredictable Depth
Agent recursion depth isn't known in advance. A planning loop might require 3 calls or 30 — capacity can't be pre-allocated cleanly.
Traditional Workloads
Smooth, predictable demand
CAPACITY
Agentic Workloads
Bursty, unpredictable spikes
CAPACITY
Three Isolation Approaches
01
Logical Isolation
Software Boundaries
Separate queues and dedicated resource allocations within shared physical infrastructure. Namespace-level separation, priority scheduling, resource quotas.
Isolation strength Low
Cost Low
Noisy neighbor risk Reduced
02
Managed Isolation
Vendor-Managed Dedicated Infra
Dedicated infrastructure operated by the vendor. You own the control plane — agent logic, data, workflows. They own the data plane — model serving, GPU provisioning, scaling. No sharing, no ops burden.
Isolation strength High
Cost Moderate
Noisy neighbor risk Eliminated
03
Physical Isolation
Dedicated Hardware Per Tenant
Own GPUs, own racks, own cooling. Complete hardware-level separation. Maximum control, maximum operational burden — you run everything.
Isolation strength Maximum
Cost High
Noisy neighbor risk Eliminated
The Trend
Enterprise AI deployment is moving toward managed isolation — the operational simplicity of SaaS without the performance lottery of shared resources. As agentic workloads become standard, the noisy neighbor problem shifts from an annoyance to an architecture-level constraint that shapes procurement decisions.
Noisy Neighbor Problem Multi-Tenancy Inference AI Inference Infrastructure Agentic Inference Data Center MoC
Noisy Neighbor Problem
Multi-Tenancy · Inference Infrastructure