Flexible, document-shaped data.
Modelled properly in MongoDB.
MongoDB is our database of choice when a project's data genuinely doesn't fit a fixed relational schema — variable job records, evolving form structures, fast-changing product data. We've used it in production behind Node.js and NestJS backends.
Where MongoDB earns its place
MongoDB isn't our default database — PostgreSQL is. We reach for MongoDB specifically when the data itself is the reason: variable structure, rapid schema evolution, or document-shaped records that don't map cleanly to tables.
Variable-Structure Records
Job records, form submissions, or product catalogues where different entries legitimately carry different fields. Forcing that into rigid tables adds complexity MongoDB avoids by design.
Multi-Tenant SaaS Backends
Platforms where operational data (job records, integrations, statuses) evolves faster than a migration-heavy relational schema comfortably allows.
Rapidly Evolving MVPs
Early-stage products where the data model is still being discovered. A flexible document store reduces the migration overhead of frequent schema changes during that phase.
Content & Configuration Stores
Application settings, feature flags, and structured-but-varied content where document storage maps more naturally to how the application actually reads and writes it.
Not our default. Here's when it's the right call.
PostgreSQL is our default database for most projects. MongoDB gets chosen deliberately, when the data itself argues for a document model — not because it's trendier.
Schema flexibility, used deliberately
Documents can carry different fields without a migration. That's genuinely useful for variable data — and genuinely risky if used as an excuse to skip data modelling entirely. We still design a schema; MongoDB just doesn't force us to migrate it every time it evolves.
Natural fit for nested, document-shaped data
When a "record" is naturally a self-contained JSON-like object — an order with line items, a form submission with dynamic fields — a document maps to it more directly than several joined tables.
Scales horizontally when you need it to
Sharding is a first-class feature, not a bolt-on. For write-heavy, high-volume collections, that matters — though most projects we build never actually need it at their scale.
Mature Node.js tooling
Mongoose gives you schema validation and structure on top of MongoDB's flexibility, closing most of the gap with a relational ORM while keeping the document model's benefits.
MongoDB vs PostgreSQL — which fits your project?
We build with both, and we'll recommend based on your actual data — not a default preference. Here's the honest breakdown.
Choose MongoDB when
Your records genuinely vary in shape between entries The schema is still being discovered (early MVP) Data is naturally nested and self-contained per document You need horizontal write scaling from day one
Choose PostgreSQL/MySQL when
Your data has clear relationships between entities You need transactional guarantees across related records Reporting and complex joins are a core requirement The schema is genuinely stable and well understood
What we pair with MongoDB
MongoDB is one piece of a larger backend. Here's what we typically build around it.