Why Go Multi-Tenant?

If you’re building a SaaS product, you’ll inevitably face these demands:

✅ Isolate customer data
✅ Scale to hundreds or thousands of customers
✅ Support flexible customization
✅ Simplify onboarding and billing

Multi-tenancy is the standard way to do this. Django, being one of the most mature Python web frameworks, can support multi-tenant designs with shared database or separate database strategies. Let’s break them down.

Architecture Options

1️⃣ Shared Database, Shared Schema

• All tenants share the same DB tables

• Tenant separation happens via a tenant_id field

• Easier to manage migrations

• Higher risk of accidental data leaks if filtering fails

2️⃣ Shared Database, Isolated Schemas

• Each tenant has its own schema (Postgres only)

• Good data separation, still one DB to manage

• Supported with libraries like django-tenants

• Schema migrations can get tricky

3️⃣ Separate Databases per Tenant

• Best isolation (e.g. per-customer DB)

• Harder to manage many database connections

• Great for very large or regulated clients

Typical Stack for Django Multi-Tenant

✅ Django + Postgres
✅ django-tenants (for schema-based)
✅ Django Rest Framework
✅ Celery + Redis for background jobs
✅ Docker & Kubernetes for deployment
✅ Stripe / Paddle for billing

Code Example with django-tenants

Let’s see schema-based isolation with django-tenants in practice.

Install it:

In your settings.py:

INSTALLED_APPS = [
    'django_tenants',
    'customers', # holds tenant metadata
    'yourapp',   # your main app
    # ...
]

Example tenant model in customers/models.py:

Run the migration for the tenant management schema:

This sets up the shared “public” schema, where you keep onboarding logic, payment models, etc.

Create a new tenant in a management command or the Django shell:

✅ Now every request routed to tenant1.yoursaas.com is automatically switched to that schema.

Routing Traffic

django-tenants uses a middleware that reads the Host header and sets the schema for every request. It can also support subfolders if needed with a small tweak.

Example:

• tenant1.yoursaas.com → tenant1

• tenant2.yoursaas.com → tenant2

This is seamless for your Django code: you keep writing queries as if it’s one app, but the schema automatically isolates data.

Advantages of Schema-Based Multi-Tenancy

• One database to maintain

• Full tenant data isolation

• Cheaper on cloud resources

• Easier horizontal scaling (more app pods on Kubernetes)

• Ideal if you expect 10s to 100s of tenants

Challenges to Plan For

• Complex migrations if you change models across all schemas

• Backup/restore must handle multiple schemas correctly

• Performance testing is critical to avoid noisy neighbor issues

• Database-level locks might block a shared DB under heavy writes

Security Best Practices

✅ Always test data leaks — double-check you cannot cross-query data
✅ Run integration tests per tenant
✅ Isolate staff/admin users per schema
✅ Automate schema backups with pg_dump
✅ Rotate tenant secrets on a schedule

Going Further

• Add Celery queues with a tenant_id context

• Integrate Stripe’s “Customer” model with your Client table

• Build tenant-aware API throttling

• Support per-tenant feature flags with django-waffle

• Add metrics with Prometheus labels per schema

Final Recommendations

✅ For 10–500 tenants, Postgres schemas + django-tenants is a strong, proven pattern
✅ For 1000+ or high-compliance customers, evaluate separate databases
✅ Document your migrations, test them, and rehearse disaster recovery
✅ Start simple — shared schema if data is very uniform — and refactor if you need deeper separation

Conclusion

Django’s ecosystem makes building multi-tenant SaaS surprisingly achievable with the right patterns. Schema-based isolation with django-tenants balances operational simplicity and strong data partitioning, letting you scale to a solid SaaS foundation.