Firebase, Supabase, Vercel or AWS? How to Choose Infrastructure for AI Projects

Introduction

Choosing the right backend for your AI application isn’t about picking the most powerful tool—it’s about selecting the stack that fits your use case, team experience, and future scalability needs.

This guide provides a side-by-side breakdown of Firebase, Supabase, Vercel, and AWS, showing how each performs for different stages and types of AI projects—from prototypes to production systems.

What You’ll Learn

  • Strengths and weaknesses of each platform

  • Best-fit scenarios by project type

  • Trade-offs in performance, control, and developer experience

  • Step-by-step infra setup examples for AI use cases

Use Case Matrix

PlatformBest ForLimitationsFirebaseReal-time apps, MVPsBlack-boxed logic, limited queryingSupabasePostgres + Auth + RealtimeSome scale limitations, still maturingVercelFront-end + serverless APIsCold starts, backend constraintsAWSScalable, highly configurableSteeper learning curve, slower prototyping

Use Case: AI-Powered Quiz App with RAG

Scenario:

  • Form interface

  • Embedding and retrieval from vector DB

  • LLM call with OpenAI or Claude

  • Email or CRM integration

Step-by-Step: AI Infrastructure by Stack

Option 1: Firebase

When to use: Solo dev MVPs, fast mobile/web prototypes

  1. Set up Firebase project and Firestore DB

  2. Use Firebase Auth for login/session

  3. Trigger Cloud Functions to call OpenAI API

  4. Store conversation history in Firestore

Pros:

  • Great DX

  • Quick setup

  • Realtime database sync

Cons:

  • Poor SQL

  • Hard to migrate

  • Limited control over function triggers

Option 2: Supabase

When to use: Postgres-first teams, data-heavy apps

  1. Create project in Supabase

  2. Use built-in Postgres DB + RLS for data access

  3. Add Supabase Edge Functions for LLM callouts

  4. Use pgvector extension for vector search

Pros:

  • SQL-native

  • Realtime + auth included

  • Easy to inspect and extend

Cons:

  • Limited concurrency

  • Early-stage ecosystem

Option 3: Vercel

When to use: Frontend-heavy AI tools with light backend

  1. Build UI with Next.js

  2. Use Vercel serverless functions for prompt generation

  3. Use external DB (like Pinecone, Supabase, or Firebase)

  4. Deploy instantly, use environment variables for API keys

Pros:

  • Fast frontend deploys

  • Great for demos and marketing-facing apps

Cons:

  • Cold starts

  • Not ideal for background tasks or workflows

Option 4: AWS

When to use: Enterprise-grade, scalable, and regulated AI apps

  1. Use Lambda or ECS for model orchestration

  2. Store data in DynamoDB or RDS

  3. Use SQS or EventBridge for queues

  4. Integrate with Bedrock or call OpenAI API

Pros:

  • Full flexibility

  • Scales to millions of users

  • Secure and robust

Cons:

  • Higher complexity

  • Slower to iterate

  • Requires DevOps skills

Decision Framework

CriteriaFirebaseSupabaseVercelAWSSpeed to MVPHighHighHighLowData Access ControlLowHighMediumHighLong-Term ScalabilityMediumMediumLowHighIntegration PowerMediumHighMediumVery HighDev ExperienceHighHighHighLow

Conclusion

Each platform has its strengths:

  • Firebase for realtime MVPs

  • Supabase for SQL-native teams

  • Vercel for front-end focused apps

  • AWS for serious scale and flexibility

Choose based on:

  • Your current dev resources

  • Your tolerance for DevOps overhead

  • Your expected user load and complexity

Firebase, Supabase, VercelFrancesca Tabor