What we build with MongoDB
MongoDB is the document database JK Tech Hub uses when data is naturally nested, changes shape often, or arrives in high volume. Projects from our Rajkot team that run on it include:
- Backends for mobile apps and web applications with Node.js and Mongoose, especially consumer products where the data model evolves with each release.
- Product catalogues for eCommerce platforms, where every category has different attributes and a rigid schema would be a burden.
- Event, log and activity stores for SaaS products, IoT dashboards and analytics features that write far more than they read.
- Content and conversation storage for AI chatbots, including vector search over embedded documents.
- Migrations from relational databases where the relational model was fighting the data, and schema reviews for existing MongoDB deployments that have become slow.
We deploy on MongoDB Atlas or self-hosted replica sets, design indexes from real query patterns, set up backups and monitoring, and document the schema so your developers know which shapes are expected in each collection.
When MongoDB is the right choice, and when it is not
MongoDB is the right choice when records are self-contained documents with varying fields, when the application is read-heavy on those documents, when you need horizontal scaling across shards, or when the schema is still being discovered during an early-stage product. It pairs naturally with Node.js and JSON-based APIs, and Atlas takes most operational work off your hands.
It is the wrong choice when:
- The data is relational at heart: invoices with line items and ledgers, stock movements, payroll, anything where totals must always reconcile. PostgreSQL enforces that integrity for you.
- You need complex reporting across many entities. Aggregation pipelines can do it, but SQL is clearer and your accountants' tools already speak it.
- Transactions across many documents are common. MongoDB supports them, but it is not where it shines.
- Your team knows SQL and nothing else. The mental shift is real and shows up as design mistakes.
Most business systems we build use PostgreSQL as the system of record and MongoDB, if at all, for the unstructured parts. We will recommend the split, not the fashion.
Cost and timeline for MongoDB projects
MongoDB work is usually part of a larger application, but it is also available on its own. Basic schema design and setup, meaning collections, indexes, a replica set or Atlas cluster, backups and monitoring, starts at ₹25,000 and takes one to two weeks. A standard engagement such as a full Node.js backend on MongoDB, or a migration from a relational database with data transformation and validation, starts at ₹80,000 and takes four to eight weeks. Enterprise projects with sharding, multi-region clusters, large migrations or performance rescue of a production database start at ₹2,50,000 and run eight to sixteen weeks.
Atlas hosting is billed by MongoDB and estimated up front; small production clusters start around ₹4,000 per month, and self-hosting on your own servers is an option where data must stay in-house. Ongoing database management, including index tuning and capacity reviews, is offered as a monthly plan.
Quotes are fixed-price and, for clients outside India, issued in their own currency through our international page.
Updated 9 September 2026