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Guides/Multi-tenant AI SaaS

Multi-tenant AI SaaS

Buyers assume isolation — prove it with partition keys in retrieval, tenant-scoped telemetry, and contract-aligned exports. The model stack can be shared; the customer data path cannot blur.

Reference sketch

Adapt boxes to your architecture — goal is a single slide sales + security can annotate together.

Tenant isolation for prompts retrieval and optional dedicated capacityTenant Apartition keyTenant Bpartition keyShared inferencerequests scopedRetrieval stores — row-level security +embedding namespaces per tenantCross-tenant leakage is usually a filter bug,not “the model forgot.”Dedicated stacks (VPC, keys) layer on for regulatedtiers — price and sell separately.
PM acceptance criteria should name tenant IDs in every retrieval query — not “trust the vector DB.”

Checklist lens

Isolation concernEngineering patternPM acceptance hook
Prompt & completion logsTenant-scoped storage + TTL; redact PII per contractExport / delete story matches sales promises
Retrieval corpusLogical isolation (prefix, collection, ACL on chunks)Demo “wrong tenant doc” regression test in QA
API keys & modelsBYOK pool vs shared pool — billing attribution per tenantInvoice line items reconcile with usage dashboards
Eval & analytics eventsStable tenant ID on every event — joins to tracesCS can filter incidents without engineering SQL

Deal hooks

  • Document BYOK / VPC options as SKUs — don't retrofit pricing after enterprise pilots.
  • Align DPA language with where embeddings and logs physically land.
  • Run quarterly “tenant bleed” drills — synthetic queries must never return neighbor chunks.