Why We Chose FastAPI over Firebase for a Production Flutter App

When scoping a new mobile application in Flutter, the default choice for state storage and user authentication is often Firebase. It is easy to see why: Firebase requires zero backend coding, offers instant real-time synchronization, and has a generous free tier. It is the perfect playground for building mobile MVPs.

However, as an engineering agency that builds software systems meant to scale, we have to look past the first week of development. We must evaluate:

• What happens when the app gets 10,000 active users?
• How do we handle complex relational logic (like financial balances and ledger matching)?
• How do we integrate on-device machine learning with server-side AI?

When we built Paisa Track—our production personal finance and group split manager—we intentionally skipped Firebase and architected a custom backend using Python (FastAPI) and PostgreSQL.

Here is why we chose FastAPI over Firebase, and why you should consider a custom backend for your next production mobile application.

1. NoSQL Firestore vs Relational PostgreSQL

Firebase uses Firestore, a NoSQL document database. Firestore is great for hierarchical, isolated data (like a user profile page). However, it is notoriously difficult to use for relational datasets.

In Paisa Track, we have highly relational business logic:

• An Expense is paid by a Group Member, belongs to a Group, and is split among multiple Expense Splits.
• A Loan is shared between a creator and a counterparty, containing multiple Loan Payments and Loan Topups.

To calculate a group's balance in Firestore, you either have to:

1. Denormalize the data: Store duplicate balance records on every document (which easily leads to sync conflicts when multiple users edit offline).
2. Perform multiple queries: Fetch the expenses, fetch the splits, and fetch the members, then merge them in client memory (which spikes your billing reads).

By choosing a custom backend with PostgreSQL, we write simple, optimized SQL queries using INNER JOINs and database transactions. If a user deletes an account, foreign key cascades clean up splits, loans, and categories instantly and atomicaly at the database layer.

2. The Cloud Cost Trap: Read/Write Billing at Scale

Firebase’s pricing is based on operations (number of reads, writes, and deletes), not server resources.

For a collaborative expense tracking app, the client is incredibly "chatty."

• Every time a user opens the app, it syncs local edits.
• Every edit to a group split triggers document writes.
• Real-time listeners constantly read collections to check for updates.

If you have 10,000 active users logging expenses and viewing group balances, a single user session can easily trigger 50–100 document reads. At scale, this operation count can lead to unexpected, skyrocketing monthly bills.

With a custom FastAPI backend hosted on a standard Linux VPS (like DigitalOcean or Cloudways), we pay a flat, predictable rate (e.g. $10–$20/month) for a dedicated virtual server. The database handles millions of queries without costing a penny extra.

3. Server-Side AI and OCR Processing

Paisa Track features AI-driven receipt scanning. While we use Google ML Kit on the device for offline text extraction, we built a server-side backup using PyTesseract (Tesseract OCR) to parse complex receipt structures.

Integrating image processing into Firebase requires spinning up Firebase Cloud Functions (NodeJS runtimes).

• Node.js is not the native language of machine learning.
• Running Tesseract OCR on Cloud Functions requires compiling custom binaries into the container, leading to high cold-start latencies and performance overhead.

By running FastAPI, we have access to the entire Python ecosystem natively. We use Python’s Pillow library to apply image filters (contrast thresholding, deskewing) and run PyTesseract server-side within milliseconds.

4. Avoiding Vendor Lock-In

When you build an app on top of Firebase, your mobile code becomes tightly coupled with Firebase's SDKs and proprietary structures. If you ever decide to move your database to another provider or host in a different region, you have to rewrite a massive portion of your mobile application's data layer.

With our custom backend architecture:

• The Flutter app communicates using standard JSON REST APIs.
• We use Dio as our HTTP client on mobile.
• If we ever want to migrate our backend from FastAPI to Laravel, Node.js, or Go, the mobile app doesn't care. It continues to make the exact same HTTP requests, saving hundreds of hours of refactoring.

Summary Comparison

The Verdict

If you are building a simple prototype or a solo utility app, Firebase is a fast way to get started. But if you are building an app with complex relational logic, integrated AI features, or plans to scale to thousands of active collaborative users, investing in a custom FastAPI and PostgreSQL backend is the correct architecture.

Planning Your Next Mobile Architecture?

At ScoRpii Tech, we design and build custom scalable software. Whether you need a secure Python API backend, an offline-first Flutter application, or a migration from Firebase to an independent VPS, we have the engineering expertise to help.

Try the ScoRpii Tech App Cost Calculator to see what your project actually costs to build, or Book a Technical Consultation to discuss your ideas with our engineering team.