Multi-Tenant Isolation
Multi-tenant isolation for AI platforms: data separation, model isolation, rate limiting, and tenant-aware architectures.
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After this section you can
- Explain pool, silo, and bridge isolation models and defend a choice with per-tenant cost reasoning (KMS key pricing, per-project overhead, shared vs dedicated vector collections)
- Trace the cross-tenant leak path through a RAG retrieval stack, explain why one occurrence is an existential incident, and design the controls that make the tenant filter un-skipable
- Walk the full isolation checklist: server-side tenant filters, Postgres RLS policies, per-tenant buckets and CMEK keys, tenant-namespaced caches, per-tenant log sinks, and crypto-shredding for GDPR erasure
- Implement per-tenant token-bucket rate limiting, explain the fairness property, and pair it with quotas, cost caps, and showback
Multi-Tenant Isolation
One platform, many customers, zero bleed. Pool, silo, and bridge architectures; the missing filter that becomes a company-threatening incident; and the per-tenant rate limits that keep one tenant’s batch job from starving everyone else.
A B2B AI platform almost never serves one customer. It runs one system for many tenants — customer organizations sharing your models, vector database, and GPU capacity under one strict contract: tenant A’s data, configuration, and load must be invisible to tenant B. This section covers the three ways to build that separation, the one bug that breaks it, and the machinery that keeps tenants from starving each other.
You have already built the pieces this section isolates: retrieval over a vector database in the RAG sections, a per-tenant cache namespace rule in SD-7 · Semantic Caching, and PII handling in PII Detection & Redaction — the prerequisite. What is new is doing all of it per tenant, plus the proof in CI that none of it leaks.