ProductionPhase 8 50 hours(broken down below)

Multi-Tenant AI Backend

SaaS AI backend with strict tenant isolation for vector stores, databases, API keys, and per-tenant configuration.

PythonFastAPIPostgreSQLPineconeRedisStripe

Project walkthrough

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Project Goal

SaaS AI backend where tenant data never leaks across boundaries.

→API key auth resolving to tenant context
→PostgreSQL RLS + Pinecone namespaces
→Per-tenant config, quotas, and usage metering
→Stripe integration for usage-based billing

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Time breakdown (50h)

Each phase maps to the estimated hours — follow in order for a realistic build schedule.

Tenant model & auth

10h

•Tenant, user, api_key tables with bcrypt-hashed keys
•Auth middleware: key → tenant_id → request.state
•Tenant signup and API key rotation endpoints

Data isolation

12h

•PostgreSQL RLS policies on all tenant-scoped tables
•Pinecone namespace per tenant for vector data
•Integration tests proving cross-tenant access fails

Per-tenant config & billing

10h

•Tenant config: model preference, chunk size, quota limits
•Usage meter: tokens in/out per request → Redis counters
•Stripe webhook for usage-based invoicing

API gateway & quotas

10h

•Per-tenant rate limits and monthly token quotas
•429 responses with upgrade CTA when quota exceeded
•Admin API for tenant management and usage reports

Security audit & tests

•Pen-test: attempt cross-tenant data access (must fail)
•Audit log for all admin actions
•Load test 50 concurrent tenants

Architecture

Every request is authenticated via API key, which resolves to a tenant context injected into all downstream queries. PostgreSQL RLS enforces row isolation, Pinecone namespaces separate vector data, and a usage meter tracks tokens per tenant for billing and quota enforcement.

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Prerequisites

PostgreSQL row-level security (RLS) concepts
Multi-tenancy patterns: shared DB vs schema-per-tenant
FastAPI middleware and dependency injection
Pinecone namespaces or metadata filtering
API key hashing and rotation best practices

Setup steps

Design tenant schema: tenants, users, api_keys, usage_logs tables
Enable PostgreSQL RLS policies scoped to tenant_id
Configure Pinecone with per-tenant namespaces
Build tenant provisioning API (create tenant → namespace + RLS role)
Add API key auth middleware extracting tenant context
Integrate Stripe for usage-based billing webhooks

Features to build

Tenant isolation
Per-tenant vector namespaces
API key management
Usage metering
Row-level security

Expected result

Provision two tenants, ingest different document sets for each, demonstrate that Tenant A cannot retrieve Tenant B's data even with a crafted query, and show per-tenant usage dashboards with token counts and billing status.

Resume bullet points

→Architected multi-tenant AI SaaS backend with strict data isolation for 100+ tenants
→Implemented row-level security and per-tenant vector namespaces

Interview questions

How do you enforce tenant isolation in a shared-database architecture?

PostgreSQL row-level security policies filter every query by tenant_id from auth context. Combine with Pinecone namespaces, never trust client-supplied tenant_id, and integration-test cross-tenant access attempts.

Shared DB vs schema-per-tenant — when to choose each?

Shared DB with RLS scales to thousands of small tenants with lower ops overhead. Schema-per-tenant suits enterprise customers needing physical isolation, custom schemas, or regulatory compliance requiring dedicated resources.

How do you meter and bill AI usage per tenant?

Count input/output tokens per request, aggregate in Redis counters flushed to Postgres, enforce quotas at request time, and sync usage to Stripe via webhooks for usage-based invoicing.