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Building Multi-Tenant SaaS Platforms with Laravel

A deep dive into designing multi-tenant architectures in Laravel, covering database separation strategies, permission systems, and scaling patterns.

Laravel SaaS Architecture PHP

Building a multi-tenant SaaS application is one of the most rewarding architectural challenges in web development. After years of building platforms like Super - The Family Office OS and contributing to Castos, I’ve developed strong opinions about what works and what doesn’t.

Why Multi-Tenancy Matters

In the SaaS world, multi-tenancy isn’t just a nice-to-have — it’s the foundation of scalable, cost-effective platforms. The ability to serve multiple clients from a single codebase while keeping their data isolated is crucial.

The Three Approaches

1. Shared Database, Shared Schema

The simplest approach: add a tenant_id column to every table. This is what most early-stage SaaS apps use.

// A simple scope in Laravel
class TenantScope implements Scope
{
    public function apply(Builder $builder, Model $model): void
    {
        $builder->where('tenant_id', auth()->user()->tenant_id);
    }
}

Pros: Simple to implement, easy to maintain, cost-effective. Cons: Data isolation relies entirely on application logic.

2. Shared Database, Separate Schemas

Each tenant gets their own schema within the same database. More isolation without the cost of separate databases.

3. Separate Databases

Maximum isolation. Each tenant has their own database. Best for compliance-heavy industries like finance.

For most SaaS platforms, I recommend:

  • Laravel with Eloquent global scopes for tenant isolation
  • Redis for caching tenant-specific data
  • Horizon for background job processing
  • Filament for admin panels with built-in multi-tenancy support

Key Takeaways

  1. Start with shared database approach — you can always migrate later
  2. Use middleware for tenant resolution
  3. Cache aggressively per-tenant with Redis
  4. Test with multiple tenants from day one
  5. Plan your permission system early

In future posts, I’ll dive deeper into each of these patterns with real code examples from production systems.