Data & APIs

MongoDB Development Services

We build on MongoDB when the data is truly document-shaped, and tell you when it is not.

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How we work

MongoDB suits one particular shape of problem: data that naturally lives as self-contained documents, with few strict relationships and a schema that shifts as the product evolves. Our mongodb development services cover schema design for document collections, aggregation pipelines for reporting and analytics inside the database, and Atlas Search or Atlas Vector Search when a product needs full-text or semantic search next to its primary data.

We design collections around access patterns rather than trying to mirror a relational entity model in document form. A product catalog with variable attributes per category, a content management system, or an activity log that grows quickly are all comfortable fits. We are equally direct when a project's data is heavily relational and would be simpler and safer on Postgres, because forcing a relational model into documents tends to show up later as painful application-level joins.

Most of our MongoDB work runs on Atlas rather than self-managed clusters, since Atlas handles sharding, backups and failover with less operational burden than running MongoDB directly, and its Vector Search feature has made it a reasonable option for teams already standardized on MongoDB who want retrieval-augmented generation without adding a new database.

Fit

When MongoDB is the right choice, and when it is not

Good fit

Your data is naturally document-shaped: a product catalog, a content management system, or user-generated content where each record is largely self-contained.
Your schema changes frequently during early development, and you want to avoid a migration for every new field a product manager asks for.
You need very high write throughput that benefits from horizontal sharding across many nodes.
You are building a catalog or content system with attributes that vary significantly between record types, which would otherwise require many nullable columns in a relational table.

Consider something else

Heavily relational financial data, where transactions, accounts and reconciliation depend on strict joins and multi-table consistency, is a poor fit; PostgreSQL handles this far more safely.
Applications that need complex ad-hoc reporting across many related entities usually find SQL and a relational schema easier to query and reason about than MongoDB's aggregation pipeline.
Teams expecting strict schema validation at the database layer, rather than in application code, get that more naturally from a relational database's constraints.
Workloads with many-to-many relationships that change often, such as a permissions system, tend to become awkward in a document model compared with relational join tables.

What we build

What we build with MongoDB

Schema and collection design
Document structures designed around how the application actually reads and writes data, deciding deliberately between embedding related data in one document and referencing it across collections.
Aggregation pipelines
Multi-stage aggregation queries for reporting, filtering and reshaping data inside the database, reducing how much processing has to happen in application code.
Atlas Search and Vector Search
Full-text search with relevance scoring, and vector similarity search for retrieval-augmented generation features built on top of existing MongoDB data.
Sharding for scale
Horizontal partitioning of large collections across multiple nodes when a single replica set's capacity is no longer enough for write throughput or data volume.
Change streams
Realtime event feeds from collection changes, used to trigger downstream processing or keep a search index or cache in sync without polling.

Ecosystem

Libraries and tools we pair it with

MongoDB Atlas
Mongoose
Atlas Search
Atlas Vector Search
Compass
Atlas Data Federation

MongoDB Versus PostgreSQL for Transactional Systems

The honest answer for most financial or transactional systems is that PostgreSQL is the safer default. MongoDB does support multi-document transactions, but the aggregation pipeline and application code end up doing work that a relational database's joins and constraints would handle at the schema level, which increases the surface area for reconciliation bugs in systems where every number has to add up exactly.

We recommend MongoDB for these systems only when a specific part of the data, an audit log, a document store, or unstructured event data, is genuinely document-shaped, and even then we frequently keep the core financial ledger in Postgres and use MongoDB alongside it rather than as the system of record for money.

Schema Design for Document Databases

The central design decision in MongoDB is whether to embed related data inside a single document or reference it across separate collections. Embedding works well when related data is almost always read together and does not grow unbounded, such as an order and its line items. Referencing works better when related data is large, grows independently, or is shared across many parent documents.

Getting this wrong shows up later as either documents that grow past MongoDB's size limits, or as application code doing the equivalent of manual joins across many queries. We model access patterns explicitly before writing schema code, rather than defaulting to whichever structure looks cleanest on a whiteboard.

Embed data that is almost always read together and bounded in size
Reference data that grows independently or is shared across parents
Design around actual read and write patterns, not an abstract data model

Services

Services that use MongoDB

Industries

Where we use it

FAQ

Questions about MongoDB

Should we use MongoDB or PostgreSQL for a new SaaS product?

It depends on how relational your core data is. If most of your entities reference each other with strict rules, customers, subscriptions, invoices, PostgreSQL is usually the safer and more maintainable choice. If your core data is more like flexible, self-contained records, such as a content system or activity feed, MongoDB can be a comfortable fit, and many products end up using both for different parts of the system.

Is MongoDB a bad fit for financial or accounting data?

For the core ledger, generally yes. Financial data depends on strict multi-table consistency and reconciliation that relational databases handle natively through joins and constraints. MongoDB supports transactions, but replicating that same level of built-in integrity checking requires more application-level work than it would in Postgres, which is why we default to a relational database for the financial core of a system.

How does MongoDB Atlas Vector Search compare with pgvector for AI features?

If your application already runs on MongoDB, Atlas Vector Search lets you add retrieval-augmented generation without introducing a second database, which is a genuine advantage. If you are choosing a database for a new project with a search or retrieval component, the choice should still follow the shape of your primary data rather than which database has vector search bolted on.

Can MongoDB handle high write volume better than PostgreSQL?

MongoDB's sharding model is designed for horizontal scaling of write throughput across many nodes, which can be simpler to reason about than sharding a relational database. Postgres can also scale substantially with proper indexing, partitioning and read replicas, so the honest comparison depends on your specific write pattern and consistency requirements rather than a blanket claim that one database writes faster than the other.

How do we migrate from MongoDB to PostgreSQL if our data has become more relational over time?

We start by mapping which collections have genuinely developed relational structure and which remain comfortably document-shaped, then design a relational schema for the former while potentially keeping MongoDB or a JSONB column for the latter. Data migration itself is usually the smaller effort; rewriting application queries from the aggregation pipeline into SQL is where most of the work happens.

Compare

Alternatives we also work with

PostgreSQLWe design PostgreSQL schemas that hold up under real transaction volume as well as the demo dataset.

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