PostgreSQL or MongoDB for a New Product

The Short Answer

For most new products, we recommend PostgreSQL as the default. Most business software is relational at heart: customers have orders, orders have line items, users belong to teams, and invoices reference all of them. A relational database models those relationships directly, enforces them for you, and handles the reporting questions that always arrive later. PostgreSQL also stores and indexes JSON well, so you keep much of the flexibility people choose MongoDB for.

MongoDB is a strong choice when your data is genuinely document-shaped: records that are read and written as a whole, vary a lot in structure, and rarely need to be joined with other records. Content catalogues, event and activity data, and product listings with very different attributes per category are common examples.

Both are mature, widely used, and capable of running serious production workloads. The choice is less about which database is better and more about which one matches the shape of your data and the way your product will query it.

How the Two Models Differ

PostgreSQL is a relational database. Data lives in tables with defined columns, rows in one table reference rows in another, and you combine them with SQL joins. The schema is explicit: you declare what a customer record looks like, and the database rejects data that does not fit.

MongoDB is a document database. Data lives in collections of JSON-like documents. A single document can hold nested objects and arrays, so an order and its line items can be stored together as one record. By default, documents in the same collection do not need the same fields or data types, which is why MongoDB is often described as schema-flexible.

That flexibility is real, but it is worth understanding what it means in practice. Your application still has a schema, because your code expects certain fields to exist. The question is whether that schema is enforced by the database or only by your application code. MongoDB does support optional schema validation using JSON Schema rules, so teams can add enforcement once their data model settles.

Side-by-Side Comparison

Consistency and Transactions

For many products, especially anything involving money, inventory, or bookings, the most important question is what happens when several related changes must succeed or fail together.

PostgreSQL has been built around transactions from the start. Moving stock between warehouses, charging a customer and creating their order, or updating a booking and its payment record can all happen in one transaction, with the database guaranteeing that either every change is saved or none is.

MongoDB has always made operations on a single document atomic, which is one reason its documentation encourages embedding related data in one document. Multi-document ACID transactions arrived in MongoDB 4.0 in 2018 for replica sets and were extended to sharded clusters in 4.2 in 2019. MongoDB's own documentation notes that multi-document transactions generally carry a greater performance cost than single-document writes, and that they should not replace good schema design. In other words, MongoDB works best when you model your data so that most operations touch one document.

If your core workflows naturally span several records, that is a signal towards PostgreSQL. If they naturally fit inside one document, MongoDB handles them cleanly.

Flexibility: JSONB Narrows the Gap

A common reason teams choose MongoDB is that they do not yet know the final shape of their data. That is a fair concern for an early product, but PostgreSQL has a good answer for it.

PostgreSQL's JSONB type stores JSON in a decomposed binary format. It is slightly slower to write than plain text JSON but significantly faster to query, and it supports indexing, including GIN indexes for containment and key-existence queries. In practice, this means you can keep your stable, relational data in normal columns and put the parts that vary, such as per-customer settings, integration payloads, or product attributes that differ by category, in a JSONB column.

This hybrid approach gives you enforced structure where it matters and flexibility where you need it, in a single database. It is one of the main reasons we treat PostgreSQL as the default for new products.

Querying and Reporting

Early on, most queries are simple: fetch this user, list these orders. Six months later, the questions change. Which customers bought twice in their first month? What is the average order value by region and plan? Which features do paying teams use most?

These are relational questions, and SQL is very good at answering them. PostgreSQL also connects directly to most analytics and business intelligence tools. MongoDB's aggregation pipeline is capable and can answer the same kinds of questions, but reporting that combines many collections tends to be more work, and teams sometimes end up copying data into a separate relational store for analysis.

If you expect reporting, dashboards, or ad-hoc business questions to matter, factor that in now.

Scaling

Both databases scale well beyond what most new products will need in their first years. PostgreSQL scales vertically very effectively and supports read replicas for spreading read traffic. MongoDB was designed with horizontal scaling through sharding as a built-in feature, which can be an advantage for very large datasets with high write volume.

For a new product, though, scaling is rarely the deciding factor. Choosing a database for a scale you may never reach, at the cost of a data model that fits your product less well, is a common and expensive form of premature optimisation. The same logic applies to architecture as a whole, as we discuss in microservices vs monolithic architecture.

Licensing and Hosting

Licensing matters more than many founders expect, especially if you plan to host the database yourself or build a product on top of it.

PostgreSQL is released under the PostgreSQL License, a permissive open-source licence similar to the BSD and MIT licences. You can use, modify, and distribute it for any purpose without fees.

MongoDB Community Server releases from 16 October 2018 onwards are licensed under the Server Side Public License (SSPL). For most companies using MongoDB as the database behind their own application, MongoDB states there is no practical impact. The SSPL's main additional condition applies if you offer MongoDB's functionality to third parties as a service, in which case you must make the source code of the software used to run that service available. The SSPL is not approved by the Open Source Initiative, and MongoDB acknowledges that software under it is not considered open source by the OSI. If licence terms matter to your investors or customers, our overview of open-source software pros and cons covers how to think about them.

Both databases are available as managed services from the major cloud providers and from specialist vendors, so you do not need to run either yourself.

A Practical Decision Guide

Choose PostgreSQL if most of these are true:

• Your data has clear relationships: users, accounts, orders, payments, permissions.
• Several records often need to change together, correctly, every time.
• You expect reporting, dashboards, or analytics to matter.
• You want the database to enforce data rules, not just your code.
• You want some flexibility, and JSONB columns are enough to provide it.

Choose MongoDB if most of these are true:

• Your records are naturally self-contained documents that are read and written as a whole.
• Structure varies a lot from one record to the next, and joins are rare.
• Your team already has strong MongoDB experience.
• You expect very high write volumes that will benefit from built-in sharding.

If you are genuinely unsure, start with PostgreSQL. Moving a well-structured relational model to a document store later is usually easier than recovering from an unstructured document model once the data has grown and many parts of the code depend on it.

Mistakes We See Often

• Choosing MongoDB to avoid thinking about the data model. The schema still exists, it just moves into application code, where it is harder to see and easier to break. That is a quiet form of technical debt.
• Modelling MongoDB like a relational database. Splitting everything into many small collections and joining them in code gives you the drawbacks of both approaches.
• Putting everything in JSONB. A PostgreSQL database where every table is a single JSON column loses most of what makes PostgreSQL useful.
• Choosing for scale you do not have. Pick the model that fits your product today and for the next few years.

The Bottom Line

Both PostgreSQL and MongoDB are excellent databases. For a typical new product, with relational data, transactions that matter, and reporting needs that will grow, PostgreSQL is the safer default, and JSONB gives you room for the parts that do not fit neatly into tables. MongoDB is the better fit when your data is document-shaped and self-contained. The database is one part of the wider backend decision; our comparison of Node.js, Django, and Spring Boot covers the application layer that sits on top of it.

Choosing a database for a new product, or wondering whether your current one still fits? We help teams make that call before it gets expensive to change. Talk to us →