Every developer discussion about databases feels the same these days. We're all comparing which Postgres provider is the least terrible, complaining about PlanetScale's pricing changes, or debating whether Neon's free tier will actually stay free. Meanwhile, MongoDB sits in the corner like that reliable friend everyone stopped inviting to parties because they weren't cool anymore.
We get it. MongoDB had its moment in the early 2010s when everyone was throwing it at every problem, often disastrously. The "MongoDB is web scale" memes wrote themselves. But somewhere between the backlash and the SQL renaissance, we forgot that MongoDB actually solves real problems for a lot of applications.
For most applications, for most SaaS products, we think MongoDB is a great choice that allows you to move fast and not think about your database. While we're spending weeks setting up migrations, wrestling with ORM configurations, and designing normalized schemas, we could be shipping features.
Why MongoDB Makes You Move Faster
The biggest advantage isn't the scaling story or the performance benchmarks. It's that MongoDB gets out of your way when you're trying to build something. Your application objects map directly to database documents without the impedance mismatch that makes every ORM feel like a leaky abstraction. When your User object has an array of preferences or a nested address structure, you just store it that way instead of creating three additional tables and a handful of foreign keys.
Schema flexibility means you can iterate on your data model without migrations. Need to add a new field to track user preferences? Just start writing it. Want to experiment with a different way to structure product variants? Change your code and deploy. This isn't about being careless with your data—it's about removing friction from the development process when you're still figuring out what you're building.
You will need to do a bit more validation in your application code, but that's actually fine. You're probably way more familiar with TypeScript than you are with Postgres constraints anyway, and your validation logic is version controlled alongside the rest of your code. When business rules change, you update your validation in the same place you update your business logic.
The JSON-native storage eliminates entire categories of problems. No more wrestling with how to represent arrays in SQL, dealing with NULL semantics across different database engines, or writing complex queries to reconstruct nested objects. Your application logic stays clean because your database speaks the same language as your code.
MongoDB's aggregation pipeline gives you powerful data transformation capabilities without dropping into raw SQL or pulling everything into application memory. Need to group users by signup month and calculate retention rates? The aggregation framework handles this elegantly. Want to find all orders within a certain geographic area? The geospatial queries are built-in, not bolted on.
When you do need to scale, MongoDB's horizontal scaling story is straightforward. Sharding happens at the database level, not the application level. Compare this to the complexity of setting up read replicas, connection pooling, and manual partitioning strategies with traditional SQL databases.
When You Should Still Choose SQL
MongoDB isn't a silver bullet, and there are clear scenarios where SQL databases remain the better choice.
Complex reporting systems expose MongoDB's biggest weakness: cross-collection queries. If you're building analytics dashboards that need to answer questions like "Show me revenue by product category, broken down by sales rep region, for customers acquired through different marketing channels in Q4," SQL's JOIN capabilities shine. In a relational database, this is a straightforward query across products, orders, users, sales_territories, and marketing_campaigns tables. In MongoDB, you'd need multiple queries and application-level data combining, or you'd have to denormalize everything, leading to massive data duplication and consistency headaches.
Multi-step financial transactions reveal MongoDB's other limitation: cross-document consistency. Consider an e-commerce checkout where you need to reserve inventory, create an order record, charge the payment method, update the customer's loyalty points, and trigger a supplier notification. If any step fails, everything needs to roll back atomically. MongoDB's transactions work well within a single document, but coordinating across multiple collections becomes complex and error-prone. SQL databases handle this scenario elegantly with ACID transactions across multiple tables.
The Practical Choice
For most applications—user-focused SaaS products, content management systems, social platforms, or any system where data naturally clusters around user entities—MongoDB's trade-offs favor faster development cycles over complex querying capabilities.
The operational story has improved dramatically with hosted solutions. MongoDB Atlas and Digital Ocean's managed MongoDB eliminate the deployment and maintenance headaches that gave MongoDB a bad reputation in the early days. You get professional database management without the overhead of running your own infrastructure.
If you're using TypeScript, tools like Esix provide type-safe MongoDB interactions with the same development experience you'd expect from modern ORMs, but without the relational baggage.
We're not suggesting you rewrite existing SQL applications or choose MongoDB for every project. But if you're starting something new and your data model doesn't scream "highly relational," MongoDB deserves serious consideration. The database landscape doesn't have to be about choosing the least bad option. Sometimes the tools that fell out of fashion are exactly what you need to ship faster.