Program syllabus

Architecting Multi-Tenant AI Apps

Four modules to design, prove, and roll out multi-tenant architecture for AI assistants and platforms — without blowing up your product, data model, or on-call rotation.

Program overview · Audio

A short overview of the program: who it's for, what we cover, and how to get the most value out of it as a busy professional.

Curriculum at a glance

Four modules to design, prove, and roll out multi-tenant architecture without blowing up your product.

Module 1

Tenancy discovery

  • • Map customer types and isolation needs
  • • Choose between pooled, sliced, and hybrid models
  • • Identify compliance and data residency constraints

Module 2

Data & access boundaries

  • • Design tenant keys and scoping in your data layer
  • • Pattern: per-tenant schemas vs. shared tables
  • • Guardrails for background jobs & async work

Module 3

RBAC, audit, and observability

  • • Role models that survive real-world org charts
  • • Audit trails for actions, configuration, and data access
  • • Per-tenant dashboards, SLOs, and alerting

Module 4

Migration & upgrade paths

  • • Moving from single-tenant to multi-tenant safely
  • • Feature flagging and phased rollouts
  • • Versioning for prompts, flows, and data contracts

1. Tenancy discovery & isolation models

We start by understanding who your tenants are, how they grow, and what they’re afraid of.

Before you draw schemas, we map customer types, deal sizes, data sensitivity, and growth patterns. That drives the isolation model: pooled, per-tenant, or something in between.

  • • Tenancy models: pooled, siloed, and hybrid
  • • Mapping isolation needs to customer segments
  • • Data residency and regional constraints
  • • Impact on pricing, SLAs, and support

Artifact: tenancy decision doc

We capture a short decision record covering your chosen tenancy model:

  • • Current and target customer profiles
  • • Preferred isolation model and rationale
  • • Known tradeoffs and future escape hatches

2. Data & access boundaries

We define how tenant identity flows through your stack — especially in the data layer.

We design tenant keys, scoping rules, and sharding strategies in the data layer so that tenant boundaries are enforced by default, not just via application code.

  • • Tenant identifiers and key design
  • • Per-tenant schemas vs. shared tables
  • • Background jobs and multi-tenant safety
  • • Isolation for logs, traces, and analytics

Artifact: tenant-aware data schema sketch

Together we outline a target schema and access pattern for one core domain (e.g., workspaces, projects, or pipelines):

  • • Tables, keys, and indexes
  • • Scoping rules for queries and writes
  • • Examples of safe vs. unsafe access patterns

3. RBAC, audit, and observability

We design role models, audit trails, and per-tenant telemetry so you can debug issues without breaching boundaries.

  • • Modeling roles that mirror real org charts
  • • Tenant-scoped permissions for AI tools and data
  • • Audit logs for actions, configuration, and access
  • • Per-tenant dashboards, SLOs, and alert routing

Templates you can reuse

  • • Example role model and permission matrix
  • • Audit log event schema
  • • Per-tenant observability dashboard outline

4. Migration & upgrade paths

We focus on how to get from where you are today to multi-tenant — without a terrifying flag flip.

We design migration strategies and upgrade paths so you can move existing customers, prompts, and workflows into a multi-tenant world in phases.

  • • Moving from single-tenant to multi-tenant safely
  • • Feature flagging and phased rollouts
  • • Versioning prompts, flows, and data contracts
  • • Handling “big customers” with special needs

Artifact: migration playbook

We co-create a simple migration playbook for your next big change:

  • • Phases, flags, and rollback points
  • • Data backfill and validation steps
  • • Communication plan for internal and external teams

Ready to ship multi-tenant AI safely?

We typically run this as a 1–2 week engagement anchored on one core product surface or assistant. You bring real constraints; we bring patterns, templates, and hard-won war stories.

By the end, you'll have a tenancy model, data boundaries, RBAC design, and migration playbook your team can execute.

Talk to us about this programView all teams programs

This pairs especially well with Workflow AI Design Blueprint and AI Assistant Observability & SLOs for teams building multi-tenant AI platforms.