We default to PostgreSQL for almost every project. It handles relational data, JSON documents, full text search, and geospatial queries, all in one database. MongoDB has legitimate strengths for specific use cases, but for most applications PostgreSQL is the better foundation.
| Feature | PostgreSQL | MongoDB |
|---|---|---|
| Data Model | Relational tables with full JSON/JSONB support. You get structured schemas where they help and flexible documents where they do not, best of both worlds | Document based (JSON/BSON). Flexible schema by default. Great for rapidly evolving data structures but can lead to inconsistent data without discipline |
| ACID Compliance | Full ACID transactions across any number of tables. Financial data, inventory management, and anything where consistency matters is safe by default | Multi document transactions added in v4.0 but with performance overhead. Single document operations are atomic. Cross collection consistency requires careful design |
| Query Language | SQL, the most widely known query language in the world. 50 years of optimization, tooling, and developer knowledge. Complex joins, window functions, and CTEs | MongoDB Query Language (MQL), JSON based syntax. Powerful aggregation pipeline but steeper learning curve for complex queries. No native join equivalent |
| JSON Support | JSONB columns with indexing, querying, and partial updates. Store unstructured data alongside relational data in the same database. GIN indexes make JSON queries fast | Native JSON storage, it is the core data model. Excellent for document centric workloads. Every query is a JSON query, which is natural for JSON heavy applications |
| Joins & Relations | Native foreign keys, joins, and referential integrity. The database enforces your data relationships, no orphaned records, no inconsistent references | No native joins. Use $lookup for cross collection queries (slower than SQL joins). Denormalization is the recommended pattern, which means data duplication |
| Scalability | Vertical scaling with read replicas for horizontal read scaling. Citus extension for horizontal sharding. Handles millions of rows efficiently with proper indexing | Built in horizontal sharding (auto sharding). Designed for distributed architectures from the start. Easier to scale writes across multiple nodes |
| Performance | Excellent query performance with B-tree, GIN, GiST, and BRIN indexes. Query planner is sophisticated and well optimized. Handles complex queries efficiently | Fast for simple document lookups and writes. Aggregation pipeline performance varies. Less efficient for complex queries that would benefit from joins |
| Ecosystem | Supabase, Neon, AWS RDS, Google Cloud SQL. Mature tooling: pgAdmin, DBeaver, Prisma, Drizzle. Extensions for everything, PostGIS, pg_trgm, pgvector | MongoDB Atlas (managed), self hosted. Good tooling with Compass and Mongoose ODM. Fewer extensions but the document model reduces the need for them |
| Full Text Search | Built in full text search with ranking, stemming, and language support. Good enough for most applications without adding Elasticsearch | Atlas Search (powered by Lucene) is excellent but requires Atlas. Self hosted MongoDB search is more limited |
| Cost | Free and open source. Managed hosting: Supabase free tier, Neon free tier, AWS RDS from $15/month. Extremely cost effective at any scale | Free Community edition. Atlas free tier for small projects. Managed hosting tends to be more expensive than equivalent PostgreSQL hosting at scale |
Why PostgreSQL
- +SQL is the universal database language, any developer can query PostgreSQL without learning a new syntax
- +JSONB support gives you document flexibility without giving up relational integrity and joins
- +ACID transactions guarantee data consistency, critical for payments, inventory, and any financial data
- +The extension ecosystem (PostGIS, pgvector, pg_trgm) means one database handles spatial, vector, and text search
- +Supabase makes PostgreSQL as developer friendly as any NoSQL database with real time subscriptions and auto generated APIs
Why MongoDB
- +Flexible schema makes rapid prototyping faster, no migrations needed for schema changes
- +Built in horizontal sharding scales write heavy workloads across multiple nodes more easily
- +Document model is natural for content management systems and catalog style data
- +Atlas managed service provides a polished developer experience with built in search and analytics
The Verdict
Our Honest Take
PostgreSQL is the right choice for 90% of applications. It handles relational data, JSON documents, full text search, and vector similarity all in one database. MongoDB makes sense when your data is genuinely document shaped with no relationships, you need easy horizontal write scaling from day one, or your team has deep MongoDB expertise. We build on PostgreSQL because it gives you flexibility without forcing you to choose between structure and speed.
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