How to Build Scalable Firebase Architecture for SaaS

Most teams start with Firebase because it's fast to ship. The problem: they don't design for scale upfront. When usage grows, they hit limits—read/write costs, security rule complexity, or the need to shard data—and end up rewriting.

Here's how to think about Firebase architecture so it scales with you.

1. Model your data for access patterns, not intuition

Firestore is a document store. Your collection structure should mirror how you query, not how you "naturally" group entities. If you need "all orders for user X in the last 30 days," you want a collection or subcollection that lets you do that in one query with an index. Avoid fan-out reads and N+1 patterns.

2. Use subcollections for 1:N when the child set grows

If one document can have thousands of children (e.g. events per user), put them in a subcollection. That keeps documents small and lets you paginate and scope security rules cleanly.

3. Security rules are part of your architecture

Complex rules don't scale—neither in performance nor in maintainability. Prefer allowlists, role-based fields, and validate that the client can only request what it's allowed to see. Offload heavy checks to Cloud Functions if needed.

4. Cost scales with reads and writes

Optimize for fewer reads: cache where possible, use aggregation in Functions instead of client-side rollups, and consider Cloud Run or Functions for batch or reporting workloads so you're not scanning entire collections from the client.

5. When to add event-driven layers

As you grow, you'll need to sync data to other systems, run background jobs, or fan-out updates. Use Firestore triggers to publish to Pub/Sub or call HTTP endpoints. That keeps your core Firestore model simple and moves "event-driven" to the edges.

If you're planning or refactoring a Firebase-backed product, an architecture review can map your current model to these principles and avoid costly rewrites later. Request an architecture review to get a clear picture.