Quick Decision Framework
Default to PostgreSQL when:
- Your data has relationships (users → orders → products)
- You need transactions and data integrity
- You'll run complex queries with JOINs
- You want the largest talent pool for hiring
- You don't know your access patterns yet
Choose MongoDB when:
- Your data is document-shaped (CMS content, catalogs)
- You rarely query across document types
- Schema changes weekly (early prototyping phase)
- You need horizontal sharding at massive scale
- Event/log data with append-heavy workloads
Pro tip: If uncertain, PostgreSQL. It's easier to migrate from Postgres than to it.
Performance Reality Check
Myth: "MongoDB is faster than PostgreSQL."
Reality: Both databases handle 10,000+ operations/second on modest hardware. Performance differences are marginal for 99% of startup workloads.
When MongoDB is genuinely faster:
- Document retrieval without JOINs
- Append-only workloads (logs, events)
- Sharded reads at massive scale
When PostgreSQL is genuinely faster:
- Complex queries with multiple JOINs
- Transactions spanning multiple tables
- Aggregate queries on relational data
For most startups: The difference is unmeasurable. Choose based on data model fit, not benchmarks.
The Polyglot Persistence Reality
Most production systems at scale use multiple databases. Typical pattern:
- PostgreSQL: Core transactional data (users, orders, payments)
- MongoDB: User-generated content, event logs, analytics
- Redis: Caching, sessions, real-time features
- Elasticsearch: Search and log aggregation
The key is intentional selection, not accidental accumulation.
Building a web application? Learn more about our web application development services and how we approach data architecture.
Migration Cost Reality
PostgreSQL → MongoDB: Painful. Flattening relational data into documents loses information and constraints.
MongoDB → PostgreSQL: Moderately painful. Normalizing documents reveals missing relationship data.
Cost of migration at Series A scale: 4-8 weeks of full-time engineering work if schema was poorly designed.
Our advice: Spend 2-3 days on data modeling upfront. It saves months later.
