Why I Chose Supabase Over Firebase for AI Projects

The Backend Decision

When I started building AI-powered applications that needed authentication, a database, file storage, and real-time features, the two main contenders were Firebase and Supabase. I had used Firebase extensively in previous projects and knew it well. But after evaluating both for AI-specific workloads, I chose Supabase. Here is why.

PostgreSQL vs Firestore

This is the decisive factor. Supabase runs on PostgreSQL. Firebase uses Firestore (or Realtime Database). For AI applications, PostgreSQL wins in several critical ways:

• Vector search: PostgreSQL with pgvector supports native vector embeddings and similarity search. This is essential for RAG applications, semantic search, and recommendation systems. Firestore has no equivalent.
• Complex queries: SQL joins, aggregations, window functions, and CTEs are bread and butter for analyzing pipeline data. Firestore's query model is limited by comparison.
• JSON support: PostgreSQL's JSONB type gives you the flexibility of a document database with the power of a relational database.
• Ecosystem: Every analytics tool, ORM, and migration framework speaks SQL. PostgreSQL compatibility means zero vendor lock-in.

Vector Search with pgvector

Setting up vector search in Supabase is straightforward:

-- Enable the extension
create extension if not exists vector;

-- Create a table with an embedding column
create table documents (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);

-- Create an index for fast similarity search
create index on documents
  using ivfflat (embedding vector_cosine_ops)
  with (lists = 100);

-- Query similar documents
select content, 1 - (embedding <=> '[0.1, 0.2, ...]') as similarity
from documents
order by embedding <=> '[0.1, 0.2, ...]'
limit 5;

Try doing this in Firestore. You cannot. You would need a separate vector database like Pinecone or Weaviate, adding another service to manage.

Authentication

Both platforms offer excellent auth. Firebase Auth is mature and battle-tested. Supabase Auth is built on GoTrue and supports the same providers: email/password, Google, GitHub, magic links, and more.

The key difference for me is that Supabase Auth integrates directly with PostgreSQL Row Level Security (RLS). You can write policies that reference the authenticated user directly in SQL:

-- Users can only read their own data
create policy "Users read own data"
  on pipeline_runs for select
  using (auth.uid() = user_id);

This moves authorization logic into the database layer, which is cleaner than implementing it in application code.

Real-Time Features

Supabase offers real-time subscriptions on database changes:

const channel = supabase
  .channel('pipeline-updates')
  .on('postgres_changes', {
    event: 'UPDATE',
    schema: 'public',
    table: 'pipeline_runs'
  }, (payload) => {
    console.log('Pipeline updated:', payload.new)
  })
  .subscribe()

Firebase's real-time capabilities are more mature and handle higher throughput. But for my use cases, monitoring pipeline status and displaying live updates on a dashboard, Supabase's real-time is more than sufficient.

Storage

Both platforms provide file storage. Supabase Storage is S3-compatible and integrates with the same RLS policies as the database. Firebase Storage (Cloud Storage) is tightly integrated with Firebase Auth.

For AI applications generating files (images, audio, video), I appreciate Supabase's straightforward API:

const { data } = await supabase.storage
  .from('generated-content')
  .upload(`videos/${videoId}/thumbnail.jpg`, file)

Edge Functions

Supabase Edge Functions run on Deno and deploy globally. Firebase Cloud Functions run on Node.js. Both work well for API endpoints and webhooks. I use Supabase Edge Functions for lightweight API proxies and webhook handlers that need to be close to users.

Pricing

Supabase's free tier is generous: 500 MB database, 1 GB storage, 50,000 monthly active users. Firebase's free tier (Spark plan) is also good but can get expensive quickly with Firestore reads at scale.

For AI projects where database queries can be heavy (vector searches, complex aggregations), Supabase's predictable PostgreSQL-based pricing is easier to forecast than Firestore's per-read model.

What I Miss from Firebase

Firebase has some advantages I acknowledge:

• Offline support: Firestore's offline persistence is excellent for mobile apps
• Cloud Messaging: Push notifications are deeply integrated
• ML Kit: On-device ML features for mobile
• Maturity: Firebase has been in production for much longer

For mobile-first applications, Firebase is still a strong choice. But for AI backends where PostgreSQL, vector search, and SQL flexibility matter, Supabase is the better foundation.

The Bottom Line

Supabase gives me PostgreSQL with batteries included. For AI applications, having vector search, complex queries, and a relational database as the foundation is worth more than Firebase's document model, no matter how polished the developer experience is.