PostgreSQL vs MongoDB: A Founder's Guide to Database Decisions
Your developer told you the database choice "depends on your data model." That is true but unhelpful.
January 8, 2025 11 min read
Your developer told you the database choice "depends on your data model." That is true but unhelpful.
Here is what actually matters: PostgreSQL forces you to plan your data structure upfront. MongoDB lets you start messy and clean up later. PostgreSQL makes complex queries easy. MongoDB makes simple queries easy and complex ones painful.
For most startups building SaaS products, PostgreSQL wins. For specific use cases involving unstructured data or rapid schema changes, MongoDB makes sense. The decision is less about the technology and more about what kind of problems you will face in six months.
What These Databases Actually Do
Databases store and retrieve your application data. Users, posts, transactions, settings - everything your app remembers lives in the database.
PostgreSQL is a relational database. Data lives in tables with rows and columns, like a spreadsheet. Tables connect through relationships (foreign keys). You query data using SQL.
MongoDB is a document database. Data lives in collections as JSON-like documents. Documents can have different structures within the same collection. You query data using MongoDB's query language.
The structural difference creates practical consequences.
The Planning Tax vs The Refactoring Tax
PostgreSQL requires upfront schema design. Before storing data, you define tables, columns, data types, and relationships. This planning takes time but prevents problems later.
MongoDB lets you start immediately. Insert documents without defining structure. Add fields on the fly. Change document shape whenever you want. This speed creates technical debt that compounds.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
Revise schema as you learn during development (another 4-8 hours)
Write migration scripts when schema changes (30 minutes per change)
Total: 10-15 hours of upfront design work
Refactoring tax (MongoDB):
Start coding immediately (0 hours upfront)
Discover inconsistent data structures after 3 months
Write migration scripts to clean up 100,000 documents with varied schemas (8-20 hours)
Ongoing maintenance of schema validation logic (2-3 hours monthly)
Total: 20-40 hours spread across development lifecycle
The pattern is consistent: PostgreSQL frontloads complexity. MongoDB backloads it. For startups with limited resources, frontloaded complexity is better. You can budget time for it and avoid surprises.
Querying: Simple vs Complex
MongoDB excels at simple queries. Get one user by ID. Fetch all posts by an author. Retrieve documents matching a field value. These queries are straightforward and fast.
PostgreSQL handles simple queries equally well but shines at complex operations:
Complex queries that are easy in PostgreSQL, hard in MongoDB:
Joins across multiple tables: Get all users who commented on posts tagged "startup" in the last 30 days, along with their comment count. PostgreSQL does this in one query. MongoDB requires multiple queries or complex aggregation pipelines.
Aggregations with grouping: Calculate average order value by customer segment, filtered by date range. PostgreSQL handles this with GROUP BY and window functions. MongoDB's aggregation framework works but is verbose.
Full-text search: Search across multiple fields with ranking, stemming, and relevance scoring. PostgreSQL has built-in full-text search. MongoDB requires Atlas Search (paid add-on) or separate search infrastructure.
Transactional integrity: Update inventory, create order, charge customer - all succeed or all fail atomically. PostgreSQL guarantees this. MongoDB added transactions in version 4.0 but with limitations and performance costs.
If your application logic involves "show me all X where Y and Z, grouped by W" type questions, PostgreSQL saves weeks of development time.
Real-World Application Patterns
Let's map common startup products to database strengths:
Why: SaaS apps have structured data with clear relationships. Users belong to organizations. Projects have tasks. Tasks have assignees and due dates. These relationships are core to the product.
Example schema:
Organizations table
Users table (foreign key to Organizations)
Projects table (foreign key to Organizations)
Tasks table (foreign keys to Projects and Users)
Querying "show all overdue tasks for my organization's projects assigned to my team" is one SQL query in PostgreSQL. In MongoDB, you would fetch organizations, then users, then projects, then tasks, then filter in application code or use complex aggregation pipelines.
Content Platforms (Blogs, Documentation, Media Sites)
Best fit: PostgreSQL or MongoDB
Why: Content can be structured (articles with consistent fields) or unstructured (varied content types with different schemas).
If your content types are consistent (blog posts always have title, author, date, body, tags), PostgreSQL works great. Use JSONB columns for flexible metadata.
If content types vary wildly (some posts have videos, some have galleries, some have interactive embeds, all with different metadata), MongoDB's schema flexibility helps.
Why: E-commerce involves complex relationships and transactional integrity. Understanding fintech architecture patterns also applies to payments-heavy applications.
Products have variants (sizes, colors)
Orders contain line items referencing products
Inventory must update atomically with order creation
Pricing may vary by customer segment or promotions
Analytics require aggregations across orders, products, and customers
PostgreSQL's ACID transactions ensure you never oversell inventory. Its join capabilities make complex reporting straightforward.
MongoDB works for product catalogs with highly variable attributes (different products have completely different specs). But you would still want PostgreSQL for orders and inventory.
Why: Collaborative documents change structure frequently as users add content, comments, and metadata. MongoDB's flexible schema accommodates this.
Chat messages and whiteboard objects are naturally document-shaped with varied properties.
But: You still need PostgreSQL (or similar) for user management, permissions, and billing. Most real-time collaboration tools use both - MongoDB for documents, PostgreSQL for structured data.
Analytics and Reporting Applications
Best fit: PostgreSQL
Why: Analytics lives on aggregations, joins, and complex queries. PostgreSQL's SQL dialect includes window functions, CTEs (common table expressions), and statistical functions that make analytics queries concise.
MongoDB's aggregation framework can do similar operations but requires more code and is harder to optimize.
For serious analytics workloads, you might use PostgreSQL with extensions (TimescaleDB for time-series data) or specialized databases (ClickHouse, BigQuery). But you would not choose MongoDB.
Performance Differences
Both databases are fast when used correctly and slow when misused.
PostgreSQL performance characteristics:
Excellent for complex queries with joins and aggregations
Slower for very simple key-value lookups compared to specialized databases
Scales vertically well (bigger machines = better performance)
Scales horizontally via read replicas, sharding is complex
Indexes are critical - missing indexes kill performance
MongoDB performance characteristics:
Excellent for simple document retrieval by ID or indexed field
Slower for complex queries requiring multiple collections
Sharding is built-in and easier than PostgreSQL
Horizontal scaling is a core design principle
Indexes matter but schema flexibility means you might not know what to index
For most startups, vertical scaling (bigger database server) is sufficient for years. PostgreSQL handles this better.
Cost Implications
Managed database pricing is similar across providers:
PostgreSQL options:
Supabase: $25/month for 8 GB database (includes auth, storage, APIs)
Render: $7/month starter, $25/month for production-ready instance
AWS RDS: $15-30/month for db.t3.small
Self-hosted: $10-20/month VPS
MongoDB options:
MongoDB Atlas: Free tier (512 MB, limited), $9/month for 2 GB, $57/month for 10 GB
Self-hosted: $10-20/month VPS
MongoDB Atlas is more expensive than comparable PostgreSQL hosting. The gap widens at scale - Atlas charges per GB of storage and per operation, similar to Firebase's model.
For typical SaaS applications, PostgreSQL hosting costs 30-50% less than MongoDB Atlas for equivalent performance.
Developer Ecosystem and Hiring
PostgreSQL has been around since 1996. Nearly every backend developer knows SQL. Hiring for PostgreSQL experience is straightforward.
MongoDB launched in 2009. Fewer developers have deep MongoDB experience. Its query language and aggregation framework have learning curves.
If you are hiring contractors or junior developers, PostgreSQL is safer. The knowledge base is broader. Stack Overflow has more answers. More tools integrate natively.
Risky: old code might not handle new document shapes, new code might not handle old shapes
Validation can be added but is opt-in
"Migrations" are actually data transformations across all documents
PostgreSQL's explicit migration approach is more work upfront but prevents bugs. MongoDB's flexibility creates subtle bugs that appear in production when old and new document formats coexist.
When MongoDB Makes Sense
MongoDB is not wrong for every use case. Choose it when:
Prototyping with uncertain requirements: If you genuinely do not know what data you need to store and expect to change it radically every week, MongoDB's flexibility helps in the first month. But plan to migrate to PostgreSQL once requirements stabilize.
Truly unstructured data: Log aggregation, event tracking, sensor data where each event has different fields. MongoDB handles this natively. PostgreSQL can use JSONB columns but MongoDB is cleaner.
Horizontal scaling is day-one requirement: If you know you will need to shard data across multiple servers from the start (rare for startups), MongoDB's built-in sharding is easier than PostgreSQL's solutions.
Team has deep MongoDB expertise: If your technical co-founder has spent years with MongoDB and is highly productive with it, do not force a switch. Expertise matters more than theoretical best practices.
When PostgreSQL Wins
PostgreSQL is the right choice for:
Anything with relational data: Users, organizations, projects, tasks, orders, inventory. If your data model has entities that reference each other, PostgreSQL makes querying and maintaining referential integrity vastly easier.
Applications requiring complex reporting: Dashboards with aggregations, filters, and joins. PostgreSQL's SQL capabilities save weeks of development time.
Transactional systems: Anything involving money, inventory, or state that must be consistent. PostgreSQL's ACID guarantees prevent data corruption.
Long-term maintainability: PostgreSQL's explicit schema and migration approach makes code easier to understand and maintain as teams grow.
Cost-sensitive projects: PostgreSQL hosting is cheaper and scales more cost-effectively than MongoDB Atlas.
PostgreSQL's JSONB column type offers flexibility within a structured database.
You can have a users table with standard columns (id, email, created_at) and a JSONB column for flexible metadata. This gives you:
Structured data for core fields (queryable, indexed, enforced)
Flexible data for variable attributes (per-user settings, feature flags, custom fields)
Example:
The metadata column can store anything. You can query it:
You can index it:
This pattern lets you start with structure and add flexibility where needed. It is better than MongoDB's "flexibility everywhere" approach for most applications.
Migration Difficulty
Migrating from PostgreSQL to MongoDB or vice versa is a multi-week project. Choose carefully.
PostgreSQL to MongoDB:
Export relational tables to JSON
Denormalize relationships (embed related data in documents)
Rewrite all SQL queries to MongoDB query language
Handle transactional logic differently
Test extensively for edge cases
MongoDB to PostgreSQL:
Analyze document structures to design relational schema
Write transformation scripts to normalize data
Rebuild indexes for new schema
Rewrite queries to SQL
Test referential integrity
Expect 40-120 hours of engineering time depending on application complexity. Migrations introduce bugs. Plan for extra testing and monitoring.
The migration cost is another reason to choose correctly initially.
Our Recommendation
For 90% of startups building SaaS applications, choose PostgreSQL.
Use MongoDB only if you have a specific reason:
Truly unstructured data that defies relational modeling
Sharding requirements on day one (rare)
Team expertise heavily weighted toward MongoDB
PostgreSQL offers:
Easier complex queries
Lower hosting costs
Stronger data integrity
Broader developer talent pool
Better tooling ecosystem
The conventional wisdom "use MongoDB for flexibility" is outdated. PostgreSQL with JSONB columns gives you flexibility where needed while maintaining structure where it matters.
We build almost every new project on PostgreSQL (via Supabase or Convex) because the tradeoffs favor it overwhelmingly for web applications.
Key Takeaways
Database choice affects development speed, query complexity, and long-term costs:
PostgreSQL: Structured data, complex queries, transactional integrity. Best for most SaaS applications.
MongoDB: Flexible schema, simple queries, horizontal scaling. Best for unstructured data or rapidly changing requirements.
Planning vs refactoring: PostgreSQL frontloads design work. MongoDB backloads it at higher total cost.
Cost difference: PostgreSQL hosting is 30-50% cheaper than MongoDB Atlas for comparable performance.
Choose PostgreSQL unless you have a compelling reason to use MongoDB. The default choice wins in most scenarios.
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