The Short Answer
PostgreSQL is the best default database for most startups and new projects. It handles relational data, JSON documents, full-text search, and geospatial queries in a single system. Use MongoDB when your data is genuinely document-oriented with no relationships between entities. Use MySQL when integrating with legacy systems or platforms that require it (like WordPress). For managed hosting, Supabase (PostgreSQL) and Firebase (NoSQL) are the fastest ways to get started.
PostgreSQL: The Default Choice
PostgreSQL is the most capable open-source database available. It is not just a relational database; it is a complete data platform. Here is what makes it the default recommendation:
- Relational data with ACID compliance. Users, orders, products, invoices, and any data with relationships between entities is what relational databases are built for. PostgreSQL handles complex joins, foreign keys, and constraints efficiently.
- JSON support (JSONB). When you need to store flexible, schema-less data alongside structured data, PostgreSQL's JSONB column type gives you document-database capabilities without needing a separate system.
- Full-text search. PostgreSQL includes built-in full-text search with ranking, stemming, and language support. For many apps, this eliminates the need for a separate search engine like Elasticsearch.
- Extensions. PostGIS for geospatial queries, pgvector for AI embeddings and vector search, pg_cron for scheduled jobs, and hundreds more. These extensions let PostgreSQL handle specialized use cases without adding infrastructure.
- Scalability. PostgreSQL handles millions of rows and thousands of concurrent connections with proper indexing. Companies like Instagram, Stripe, and Reddit use PostgreSQL at massive scale.
For managed PostgreSQL, Supabase is the fastest option for startups. You get a PostgreSQL database with a REST API, real-time subscriptions, authentication, and a dashboard in under two minutes. AWS RDS and Google Cloud SQL are alternatives for teams that want more control over their infrastructure.
MongoDB: When Documents Make Sense
MongoDB stores data as JSON-like documents in collections rather than rows in tables. This model works well for specific use cases:
- Content management systems where each document has a different structure (blog posts, product pages, event listings with varying fields).
- IoT and logging data where you are ingesting high volumes of semi-structured data that does not need relationships.
- Catalogs with variable attributes where products in different categories have different properties (electronics have wattage, clothing has size, food has ingredients).
MongoDB's weakness is relational data. If your data has relationships (users have orders, orders have items, items belong to categories), you either denormalize (duplicate data across documents) or use lookup queries that are slower than SQL joins. Most SaaS products, marketplaces, and e-commerce platforms have relational data, which is why PostgreSQL is usually the better choice.
MongoDB Atlas is the managed service most teams use, offering global distribution, automatic scaling, and a generous free tier.
MySQL: The Legacy Workhorse
MySQL powers a huge portion of the internet, largely because of WordPress, Drupal, and other PHP-based platforms. It is reliable, well-understood, and has excellent hosting support.
However, for new projects, PostgreSQL has surpassed MySQL in almost every capability. PostgreSQL has better JSON support, more advanced indexing, superior extension ecosystem, and stronger standards compliance. MySQL remains the right choice when:
- You are building on WordPress or a platform that requires MySQL
- Your team has deep MySQL expertise and no PostgreSQL experience
- You are integrating with an existing MySQL-based system
For new projects without these constraints, PostgreSQL offers more features with no trade-offs.
Choosing a Managed Database Service
If you are a startup, you probably should not be managing your own database servers. Use a managed service:
| Service | Database | Best For | Free Tier | |---------|----------|----------|-----------| | Supabase | PostgreSQL | Full-stack apps, real-time features | 500 MB, 50K MAU | | Firebase | Firestore (NoSQL) | Mobile-first apps, quick prototypes | 1 GB stored, 50K reads/day | | AWS RDS | PostgreSQL/MySQL | Enterprise, custom infrastructure | 750 hours/month (12 months) | | PlanetScale | MySQL (Vitess) | MySQL at scale with branching | 5 GB, 1B reads/month | | Neon | PostgreSQL | Serverless PostgreSQL, branching | 512 MB, auto-suspend | | MongoDB Atlas | MongoDB | Document databases, global distribution | 512 MB shared cluster |
For most startups, Supabase is the fastest path to a production-ready PostgreSQL database with built-in auth, storage, and real-time capabilities.
How UniqueSide Can Help
We have shipped over 40 products and designed database architectures for SaaS platforms, marketplaces, healthcare apps, and fintech products. We help founders choose the right database, design efficient schemas, set up proper indexing, and implement Row Level Security. Our projects start at $8,000 with delivery in as little as 15 days.
Our team works extensively with PostgreSQL, MongoDB, and Supabase. Visit our MVP development services to learn how we build data-driven products that scale.
Frequently Asked Questions
Can I use multiple databases in one project?
Yes, and it is common at scale. Many companies use PostgreSQL for their core transactional data, Redis for caching and sessions, and Elasticsearch for search. However, for an MVP, one database (PostgreSQL) handles all these needs adequately. Add specialized databases only when you have a measurable performance need.
How important is database choice for an MVP?
Very important, because migration is expensive. Switching from MongoDB to PostgreSQL (or vice versa) means restructuring your data model and rewriting every query. Choose correctly upfront and you avoid a painful migration later. For most products, PostgreSQL is the safe, capable choice.
Should I use an ORM or write raw SQL?
Use an ORM like Prisma, Drizzle, or SQLAlchemy for most queries. ORMs speed up development, prevent SQL injection, and handle migrations. Write raw SQL for complex queries where the ORM generates inefficient code. This hybrid approach gives you the best of both worlds.
