The database you choose shapes how your data is stored, how fast it can be queried, and what kinds of questions you can answer about your business. This choice matters more than most founders realize, and it is made early, which is when it is hardest to evaluate.
The two fundamental types
PostgreSQL and MySQL are relational databases. They store data in tables with rows and columns, connected by relationships. A user table connects to an orders table. An orders table connects to a products table. The relationships between data are explicit and enforced.
MongoDB is a document database. Data is stored as flexible JSON-like documents rather than rigid table rows. A user document might contain their profile information, their preferences, and a nested array of their recent orders, all in one document.
This structural difference shapes when each type makes sense.
PostgreSQL
PostgreSQL is the most feature-rich relational database available. It handles complex queries efficiently, enforces data integrity rigorously, and supports advanced data types that most other databases do not.
Why PostgreSQL is often the right default:
ACID compliance. Transactions in PostgreSQL are reliable. When you write data, it either writes completely or not at all. This matters for anything involving money, inventory, or any data where partial writes would create inconsistencies.
Complex queries. PostgreSQL's query optimizer handles complex joins, aggregations, and analytical queries well. If you need to answer complex business questions from your database, PostgreSQL's SQL capabilities are extensive.
Data integrity. You define the rules for your data in the schema. The database enforces them. If a user must have an email address, PostgreSQL will not store a user without one. This prevents the data quality problems that cause subtle application bugs and reporting errors.
The pgvector extension. For applications that need AI-powered semantic search or recommendation features, pgvector adds vector similarity search to PostgreSQL. This allows one database to handle both your application data and your AI search requirements.
PostgreSQL is appropriate for: most SaaS applications, any application handling financial data, applications that need complex reporting, applications where data relationships are important.
MySQL
MySQL was the most widely deployed relational database for many years, partly because of its inclusion in LAMP stacks (Linux, Apache, MySQL, PHP) that powered early web development.
It is reliable, well-documented, and has a large ecosystem of tools and expertise. For most applications, MySQL and PostgreSQL produce similar results.
The reason PostgreSQL is now often preferred over MySQL: PostgreSQL has consistently been more feature-complete, particularly for complex query types and advanced data types. For new projects without existing MySQL infrastructure, PostgreSQL is usually the stronger choice.
MySQL is appropriate for: teams with existing MySQL infrastructure and expertise, applications that need to run on a specific hosting provider that supports MySQL but not PostgreSQL (increasingly rare).
MongoDB
MongoDB's document model is genuinely useful when your data is highly variable in structure and nesting those variations in a rigid table schema would be awkward.
When MongoDB's document model helps:
Unstructured or semi-structured data. Content management systems, user-generated data, and product catalogs where different items have different attributes fit naturally in documents.
Rapid schema iteration. Early-stage products where the data model changes frequently benefit from MongoDB's schemaless flexibility. Adding a new field does not require a database migration.
High write volumes at scale. MongoDB's sharding model distributes data across multiple servers, which handles very high write throughput at scale.
When MongoDB creates problems:
Transactions and data integrity. MongoDB added multi-document transactions but they are more limited than PostgreSQL's. For applications that require consistent state across multiple documents (booking a seat and recording the payment in one atomic operation), the lack of robust transaction support historically caused problems.
Complex queries across related data. A relational query that connects users to their orders to their products is natural in SQL. In MongoDB, this requires either denormalization (storing redundant data) or application-level joins, both of which have trade-offs.
Reporting. Business intelligence and reporting tools expect relational data. Connecting a BI tool to MongoDB is more complex than connecting to PostgreSQL or MySQL.
The practical recommendation
For most SaaS applications: PostgreSQL. The data integrity, transaction support, and query capabilities suit the reliability requirements of production software.
For rapid prototyping where schema will change frequently: MongoDB can speed up early development. Plan to evaluate whether to migrate to PostgreSQL once the data model stabilizes.
For applications with specific high-volume write requirements or very dynamic document structure: evaluate MongoDB specifically for those use cases rather than as a general replacement for relational databases.
Managed database services matter as much as the database choice
The operational burden of running a database differs significantly based on whether you use a managed service.
AWS RDS for PostgreSQL or MySQL, Google Cloud SQL, and DigitalOcean Managed Databases handle backups, high availability, and security patches for you. MongoDB Atlas does the same for MongoDB. The additional cost relative to self-hosting is almost always justified for production systems unless you have specific reasons to manage the infrastructure yourself.
Use the tech stack picker for a recommendation that considers your specific application type. Then get in touch with our development team to discuss database architecture as part of your overall technical design.