Technology
MongoDB development.
Flexible data, as long as someone still designs it.
MeetOnChai Studio builds on MongoDB when an app's data is naturally document-shaped: content with varying fields, product catalogs with different attributes per category, event logs and user-generated data. It pairs well with our Node.js and TypeScript backends. For data with lots of relationships we usually recommend PostgreSQL instead, and we will tell you which fits before we start.
Why we use it
Why MongoDB, and when not.
MongoDB stores data the way JavaScript apps already think about it: as objects with nested fields. A product with sizes, colours and a different set of specifications per category can live in one document instead of being spread across five tables. Early in a product, when the shape of the data changes every week, that flexibility lets us move quickly without a migration for every new field.
It also scales in ways that suit certain workloads well. Reads that fetch one whole document are fast, indexes cover the common queries, and MongoDB Atlas handles replication, backups and scaling as a managed service. Multi-document transactions exist when you need them, and Atlas Vector Search lets AI features query embeddings stored alongside the rest of your data.
When it's the wrong call: If your app is mostly about relationships between records, like orders, invoices, inventory and accounting, a relational database such as PostgreSQL will keep the data more consistent and make reporting much easier. Schemaless does not mean design-free; a MongoDB database without a planned structure turns into a mess quickly. We will not pick MongoDB just because it feels faster to start.
What we build
What we build with MongoDB.
- 01
Content and catalog backends
Products, listings or articles where each item can carry its own set of fields without a table for every variation.
- 02
MVPs with changing requirements
Early products where the data model is still moving, with validation rules added as the shape settles.
- 03
Activity feeds and event logs
High-volume, append-heavy data like user activity, notifications and audit trails, with indexes and TTL rules for old records.
- 04
User profiles and settings
Per-user preferences and profile data that varies between users and is almost always read as one piece.
- 05
Search and AI features on Atlas
Atlas Search and Vector Search for in-app search and retrieval, kept in the same database as the source data.
- 06
Mongo to Postgres or Postgres to Mongo moves
Migrating when the original choice no longer fits, with data checks and a cutover plan that does not stop the business.
The stack around it
What it ships with.
- Database
- MongoDBMongoDB AtlasAtlas SearchAtlas Vector Search
- Access layer
- Official MongoDB driverMongooseSchema validation
- Backend
- Node.jsTypeScriptNext.js route handlers
- Operations
- Indexes and query profilingAutomated backupsReplica sets
Honest comparison
MongoDB vs the alternatives.
vs PostgreSQL
Our default when data is relational. With JSONB it also handles flexible fields well, so many apps that start on MongoDB would be just as happy on Postgres.
vs Firebase Firestore
Quick for mobile apps that need real-time sync and offline support. Querying and costs get harder to manage as the app and its reports grow.
vs DynamoDB
Very scalable if you are already on AWS and know your access patterns up front. Less forgiving than MongoDB when queries change later.
Services
Services that fit.
- Web App DevelopmentNext.js web apps and SaaS products that hold up under real traffic.
- MVP & Product DevelopmentThe smallest real version of your product, in front of users in 4-8 weeks.
- SaaS DevelopmentMulti-tenant SaaS with accounts, roles and subscription billing built in from day one.
- Mobile App DevelopmentiOS and Android apps, tested on real phones and shipped to both stores.
- Maintenance & SupportBug fixes, updates and small improvements after launch, on a plan that fits.
FAQ
MongoDB questions.
Is MongoDB good for e-commerce?
It works well for the catalog, where products have different attributes. Orders, payments and stock benefit from strict relationships and transactions, which is why we often recommend PostgreSQL for the transactional side. We will look at your actual data before suggesting either.
Does MongoDB support transactions?
Yes. MongoDB supports multi-document ACID transactions on replica sets and sharded clusters. They carry a performance cost, so we design documents to keep most writes inside a single document and use transactions where correctness really demands it.
Should we use MongoDB Atlas or self-host?
For most teams, Atlas. It handles backups, upgrades, monitoring and failover, and it has a free tier for early projects. Self-hosting makes sense only if you have strict data residency needs and someone to look after the servers.
Our MongoDB queries are slow. Can you help?
Usually. The common causes are missing or wrong indexes, documents that grew far too large and queries that scan whole collections. We use the query profiler and explain plans to find the worst ones, then fix indexes or reshape the data.
Can MongoDB be used for AI features?
Yes. Atlas Vector Search stores and queries embeddings in the same database as your documents, which is handy for semantic search or chat over your own content. If you are not on Atlas, PostgreSQL with pgvector is a strong alternative.
Every project starts with a conversation
Got an idea? Let's talk over chai.
A free 15-minute call. No pitch deck, no obligation. You leave with an honest read on what to build first.
- We reply within 24 hours, usually much sooner.
- NDA signed before you share the details.
- Based in Kathmandu, working with Nepal, India and worldwide.