Supabase vs MongoDB: The Ultimate Comparison for 2025

Carlos Martinez
Oct 22
6 min read

MongoDB is well-established and widely used. Supabase is newer and growing, especially for teams using PostgreSQL or looking for a Firebase alternative.

They handle data differently and have their own limitations. MongoDB stores documents with flexible schemas and scales horizontally. Supabase keeps data relational and adds real-time updates, authentication, and APIs generated from your schema.

Let’s compare how they handle data modeling, querying, scaling, and deployment, focusing on the differences that matter for reliable systems.

Why Compare Supabase and MongoDB?

MongoDB stores documents without fixed schemas. It’s flexible, but you need to manage consistency yourself. Supabase uses PostgreSQL, so you get defined schemas and transactional guarantees.

Scaling: MongoDB supports horizontal sharding (reads and writes). Supabase (Postgres) scales vertically and uses read replicas for horizontal read scaling only.
Complexity: MongoDB’s flexibility can make joins, queries, and data integrity harder to manage. Supabase enforces structure early, so schema changes require planning.
Workflow: MongoDB has mature tooling and a broad ecosystem. Supabase adds real-time updates, auth, and auto-generated APIs that speed development.

Compare them to decide which fits your data model, workload, and operational constraints.

What is MongoDB?

MongoDB was released in 2009 to handle data that didn’t fit neatly into relational tables. It’s maintained by MongoDB Inc., which also offers MongoDB Atlas, a managed cloud service.

It stores data as BSON (Binary JSON), allowing nested objects and arrays. Collections don’t require a fixed structure, so you can add or remove fields without running migrations. That flexibility helps when data models change often.

Stores JSON-like documents with dynamic schemas.
Uses replica sets for high availability.
Supports horizontal scaling through sharding.
Includes features like full-text search and time-series collections.

MongoDB works well when you need flexible data models or expect your structure to evolve over time.

What is Supabase?

Supabase launched in 2020 as an open-source alternative to Firebase. Instead of creating a new database, it builds on PostgreSQL, benefiting from its reliability, performance, and ecosystem.

It includes a set of built-in services:

Auth: Manages users, sessions, and JWT-based authentication.
Storage: Handles file uploads with access control policies.
Edge Functions: Runs server-side code close to users.
Real-time: Streams database changes to clients over WebSockets.

Supabase also generates REST and GraphQL APIs automatically from your database schema. You can define row-level security in PostgreSQL to control access directly in the database.

It can be self-hosted or used as a managed service, giving teams flexibility in how they deploy and scale.

Data Model & Query Language

MongoDB documents contain nested objects and arrays:

{
  "user_id": "abc123",
  "name": "Alex Chen",
  "address": {
    "city": "Portland",
    "state": "OR"
  },
  "orders": [...]
}

You query using MongoDB's query language with operators like $match, $group, and $lookup. Aggregation pipelines process documents through stages, similar to Unix pipes. Complex queries chain multiple operations together.

Supabase uses standard SQL. The same data requires normalized tables:

SELECT u.name, a.city, a.state, o.order_id
FROM users u
JOIN addresses a ON u.user_id = a.user_id
JOIN orders o ON u.user_id = o.user_id
WHERE u.user_id = 'abc123';

SQL provides powerful constructs for complex queries. Window functions, recursive queries, and sophisticated joins handle analytical workloads efficiently. MongoDB's aggregation framework handles many of these cases but requires different thinking patterns.

Storage, Indexing & Scalability

MongoDB stores data in BSON using the WiredTiger engine for compression and concurrency. Data can be sharded across servers using a shard key, with each shard as a replica set. Queries route automatically to the right shard.

It supports indexes on any field, including nested or array elements. Compound and text indexes are also available.

Supabase uses PostgreSQL with B-tree indexes by default. You can index expressions or subsets of data. GiST and GIN indexes handle full-text search and JSON queries.

PostgreSQL scales vertically and with read replicas for load distribution. Citus adds sharding when needed, though it increases complexity.

Real-time and Replication Features

MongoDB offers change streams to watch for data changes in real time. Streams can target collections or databases but require managing cursors and connections.

Supabase uses PostgreSQL’s logical replication to push changes over WebSockets. The client handles reconnections automatically, and row-level security applies to real-time data.

Both support reactive apps - Supabase simplifies access control, MongoDB offers more event control.

Performance

Performance depends on workload:

MongoDB handles high write throughput well when sharded.
PostgreSQL (Supabase) is great at complex read queries and joins.
For simple CRUD, both perform similarly with good indexing.

Data modeling and query design usually matter more than database choice.

Scaling

MongoDB is built for horizontal scaling. You can add shards as data grows, with config servers tracking data placement. Poor shard keys cause uneven load or hot spots.

Supabase scales vertically and with read replicas for most workloads. Many large applications run comfortably on a single PostgreSQL instance. Horizontal scaling is possible with Citus if needed.

Built-in Services

Supabase includes:

Auth for user management and tokens.
Storage for files with access policies.
Edge Functions for running backend logic.

MongoDB focuses on the database. You’ll need external tools for auth (Auth0, Firebase Auth) or storage (S3, GCS). Atlas Functions and Triggers offer some integration but differ from Supabase’s all-in-one approach.

Extensions & Tooling

MongoDB: Compass for UI, Atlas Search for full-text search, Realm for mobile sync, and aggregation pipelines for data processing.

Supabase: full PostgreSQL extensions like PostGIS (geospatial), pgvector (vector search), and built-in full-text search.

Both integrate with ORMs - Supabase with Prisma, Drizzle, TypeORM; MongoDB with Mongoose and Prisma.

Managed vs Self-Hosted Costs

Both platforms start free and scale with usage. The main difference is how much control you want over infrastructure and scaling.

MongoDB Atlas

Free tier: 512 MB storage, shared CPU and RAM, up to 100 ops/sec.
Flex: ~$0.011/hour for dev or test use, 5 GB storage, daily backups.
Dedicated: Starts around $0.08/hour (≈$57/month for M10) with private networking, backups, and sharding.
Enterprise: On-prem or private cloud with Ops Manager and advanced security. Self-hosting lowers cost but adds overhead for maintenance and scaling.

Supabase

Free tier: 500 MB database, 1 GB storage, and 50 K monthly active users.
Pro: Starts at $25/month with 8 GB database and compute from $10/month.
Team: $599/month with SSO, SOC2 compliance, and longer retention.
Enterprise: Custom pricing for large workloads.Self-hosting is free and open source but requires managing performance and backups.

Cost range:

Small: Free tiers fit prototypes and early projects.
Medium: $100-$500/month depending on usage.
Large: MongoDB scales wider but costs more; Supabase grows through larger compute instances.

Best for Flexible, Evolving Schemas: MongoDB works best when your data model changes often or doesn’t fit into fixed tables. Its document model lets you iterate quickly without strict migrations.

Hybrid or Complementary Architectures

Some teams use both databases for different workloads. Transactional data stays in PostgreSQL, while analytics events or user-generated content go to MongoDB. PostgreSQL maintains relationships and consistency; MongoDB handles flexible or high-volume data.

Managing two databases means extra work - you’ll maintain separate backups, monitoring, and sync processes. It only makes sense if each one clearly adds value.

Using FerretDB for Mongo Compatibility

FerretDB lets you use MongoDB drivers and query syntax while storing data in PostgreSQL. It helps if you’re migrating or want MongoDB-style access on top of Postgres.

It works well for basic operations but doesn’t match MongoDB’s behavior in every case. Complex queries or pipelines may not perform the same.

Data Syncing & Migration

Migrating from MongoDB to Supabase means turning nested documents into relational tables. Subdocuments often become their own tables with foreign keys. pgLoader or custom scripts can automate part of it, but you’ll still need to plan and test.

Going from Supabase to MongoDB does the reverse - combining related rows into larger documents. This can simplify reads but may duplicate some data.

Strengths and Limitations

MongoDB:

Pros	Cons
Flexible schema for fast iteration	Complex queries across multiple documents
Scales horizontally through sharding	Eventual consistency in distributed setups
Matches document-style data structures	Steeper learning curve for SQL developers
Mature ecosystem and strong tooling	Relationships require manual handling

Supabase:

Pros	Cons
Built on PostgreSQL, familiar SQL model	Newer platform, smaller community
Integrated auth, storage, and real-time features	Limited vertical scaling
Strong data consistency with ACID transactions	Real-time depends on Supabase client libraries
Row-level security for granular access control

Getting Started

Choose the database that fits your project and your team’s experience.

Supabase works well if your data is relational and your team knows SQL. It includes auth, storage, and real-time updates out of the box.
MongoDB is better if your data structure changes frequently or you need flexible document storage that can scale horizontally.

Start with one database and focus on keeping the setup simple and reliable.

You can also connect with our experts for consultation and support in designing, optimizing, or migrating your database systems.