Krunal Panchal

Posted on Apr 22 • Originally published at groovyweb.co

MongoDB vs Firebase vs Supabase for AI Apps (2026): Honest Comparison from 200+ Projects

#javascript #ai #database #webdev

Choosing a database for an AI-powered application in 2026 is no longer just "relational or document" — it's about vector embedding storage, LLM pipeline integration, and scaling without cost surprises.

At Groovy Web, our AI-First teams have built on all three platforms across 200+ client projects. This is the honest comparison — not vendor-neutral, but from teams that hit production limitations.

Why AI Changes the Database Decision

Every serious AI app now requires vector embedding storage. All three platforms responded:

MongoDB added Atlas Vector Search
Supabase exposes PostgreSQL's pgvector extension natively
Firebase partnered with Vertex AI but lacks native vector storage

Vector search quality now represents one of the most important factors in 2026 database selection.

MongoDB in 2026

Strengths: Most flexible for complex, variable document structures. Atlas Vector Search enables embeddings stored as arrays with compound filtering (vector similarity + metadata) in a single query.

Weaknesses: Cost becomes prohibitive at scale. Teams with SQL expertise find the aggregation pipeline frustrating vs SQL.

Firebase in 2026

Strengths: Zero backend for prototyping. Real-time listeners, Auth, Cloud Functions, and offline sync handled automatically.

Weaknesses: Most restrictive query model. No native vector search — requires Vertex AI or Pinecone, adding cost and operational complexity.

Firebase's AI story is the weakest of the three.

Supabase in 2026

Strengths: The AI application winner in 2026 for architectural elegance. Supabase is PostgreSQL + pgvector + RLS + real-time + auth + auto-generated REST/GraphQL APIs.

pgvector is the most production-proven vector extension. It integrates directly with PostgreSQL's query planner — vector searches combine with SQL WHERE clauses, JOINs, and aggregations natively.

Weaknesses: Vendor lock-in risk. Supabase Edge Functions (Deno-based) have a smaller ecosystem than Node.js for AI integrations.

Code: Atlas Vector Search vs pgvector

MongoDB Atlas Vector Search

import { MongoClient } from 'mongodb';
import OpenAI from 'openai';

const client = new MongoClient(process.env.MONGODB_URI);
const openai = new OpenAI();

async function semanticSearchMongoDB(query, filters = {}) {
  const db = client.db('myapp');
  const collection = db.collection('documents');

  const embeddingResponse = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: query
  });
  const queryEmbedding = embeddingResponse.data[0].embedding;

  const pipeline = [
    {
      $vectorSearch: {
        index: 'vector_index',
        path: 'embedding',
        queryVector: queryEmbedding,
        numCandidates: 100,
        limit: 5,
        filter: {
          category: filters.category || { $exists: true },
          ...(filters.userId && { userId: filters.userId })
        }
      }
    },
    {
      $project: {
        _id: 1,
        title: 1,
        content: 1,
        category: 1,
        score: { $meta: 'vectorSearchScore' }
      }
    }
  ];

  return await collection.aggregate(pipeline).toArray();
}

Supabase pgvector

import { createClient } from '@supabase/supabase-js';
import OpenAI from 'openai';

const supabase = createClient(
  process.env.SUPABASE_URL,
  process.env.SUPABASE_SERVICE_KEY
);
const openai = new OpenAI();

async function semanticSearchSupabase(query, filters = {}) {
  const embeddingResponse = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: query
  });
  const queryEmbedding = embeddingResponse.data[0].embedding;

  // SQL function in Supabase:
  // CREATE OR REPLACE FUNCTION match_documents(
  //   query_embedding vector(1536), match_count int, filter_category text DEFAULT NULL
  // ) RETURNS TABLE (id uuid, title text, content text, category text, similarity float)
  // LANGUAGE plpgsql AS $$
  // BEGIN
  //   RETURN QUERY
  //   SELECT d.id, d.title, d.content, d.category,
  //          1 - (d.embedding <=> query_embedding) AS similarity
  //   FROM documents d
  //   WHERE (filter_category IS NULL OR d.category = filter_category)
  //   ORDER BY d.embedding <=> query_embedding
  //   LIMIT match_count;
  // END; $$;

  const { data, error } = await supabase.rpc('match_documents', {
    query_embedding: queryEmbedding,
    match_count: 5,
    filter_category: filters.category || null
  });

  if (error) throw new Error(error.message);
  return data;
}

12-Dimension Comparison Table

Dimension	MongoDB Atlas	Firebase	Supabase
Data Model	Document (flexible schema)	Document (hierarchical)	Relational (PostgreSQL)
Vector Search	Atlas Vector Search (HNSW)	None native	pgvector (HNSW + IVFFlat)
SQL Support	No (aggregation pipeline)	No (limited query model)	Full PostgreSQL SQL
Real-Time	Change Streams	Native Firestore listeners	Supabase Realtime
Self-Hosting	Yes (Community Edition)	No (GCP only)	Yes (Docker)
Pricing at Scale	Expensive (Atlas M10+)	Expensive (per-read surprises)	Predictable ($25/mo Pro base)
Auth Built-In	No	Yes (Firebase Auth)	Yes (Supabase Auth)
TypeScript Support	Good (Mongoose)	Good	Excellent (auto-generated types)
Best For	Variable schemas, document-heavy AI	Rapid prototypes, mobile/offline	AI apps with vector search, SaaS

Our Recommendations by Project Type

Choose Supabase for most new AI apps

For new AI applications in 2026, Supabase is our default recommendation. pgvector is mature, SQL integration makes compound queries trivial, and pricing is predictable. For teams that know SQL, Supabase offers the highest-productivity platform.

Choose MongoDB for genuinely document-centric data

Knowledge management platforms, content CMSs, flexible product catalogs, user-generated content — MongoDB's schema flexibility provides genuine advantage here. Atlas Vector Search is good enough that you don't need a separate vector DB.

Choose Firebase only for rapid prototypes or mobile-first

Fastest path from zero to working app with real-time sync. For AI at its core, complex queries, or scale ambitions — plan a migration path before hitting Firebase's query and pricing ceilings.

Do You Need a Separate Vector Database?

No — for most AI applications. Both MongoDB Atlas Vector Search and Supabase pgvector handle datasets up to tens of millions of vectors in production. Compound queries (vector + metadata filtering) are significantly simpler when vectors stay in your operational database.

Dedicated vector databases (Pinecone, Weaviate) are only worth it at hundreds of millions of vectors with very high query throughput.

Database Selection Checklist for AI Apps

[ ] Does your app require vector embeddings? → MongoDB or Supabase (not Firebase)
[ ] Is your data model primarily relational? → Supabase
[ ] Is your data model document-based with variable schemas? → MongoDB
[ ] Does real-time sync need offline support for mobile? → Firebase
[ ] Is cost predictability critical? → Avoid Firebase per-read pricing; prefer Supabase
[ ] Does your team have SQL expertise? → Supabase delivers more productivity
[ ] Do you need self-hosting? → MongoDB Community or Supabase Docker
[ ] Do you need compound vector + metadata filtering? → MongoDB or Supabase (Firebase cannot)

TL;DR

Supabase wins for most AI apps in 2026: pgvector + SQL + auth + real-time, predictable pricing
MongoDB wins for document-heavy data models where schema flexibility is genuinely needed
Firebase is for prototypes and mobile-first apps — not for AI at the core

Originally published at groovyweb.co. Groovy Web builds AI-first applications — 200+ projects, starting at $22/hr.

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