MongoDB development company
Krapton designs MongoDB data models around how your application reads and writes, then builds the aggregation pipelines, indexes, Atlas Search features and sharded clusters that keep those access patterns fast when collections reach production size.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
Document modeling decides most MongoDB outcomes
Krapton is a MongoDB development company that starts every engagement with access patterns, not an entity diagram. The flexibility that makes MongoDB quick to adopt also lets teams store data in shapes that hurt later: arrays that grow without bound toward the 16 MB document limit, references that force several $lookup stages on every page load, or a shard key on a timestamp that sends all writes to one shard. Our engineers decide what to embed and what to reference from the queries each screen and API issues, apply patterns such as bucketing, the subset pattern and computed fields where they fit, and enforce structure with $jsonSchema validators so flexible never means unchecked. Indexes follow the equality, sort, range rule and are verified with explain output. We also tell you when MongoDB is a poor fit: strongly relational data with many cross-entity transactions and ad hoc joins usually belongs in PostgreSQL, and we say so before building.
MongoDB development services
Document schema design
Embedding versus referencing decided per access pattern, bucket, subset, extended reference and computed patterns applied deliberately, and $jsonSchema validation rules that reject malformed writes at the database.
Aggregation pipeline development
Reporting and transformation logic written as pipelines using $match, $group, $lookup, $facet and $merge, ordered so early stages use indexes and memory-heavy stages stay within limits.
Index and query tuning
Compound indexes designed with the equality, sort, range rule, partial and TTL indexes where useful, and slow operations traced through the profiler, explain plans and Atlas Performance Advisor.
Atlas Search and vector search
Lucene-backed full-text search with analyzers, autocomplete and facets, plus vector indexes for semantic search and retrieval-augmented generation stored right alongside your operational documents.
Sharding and cluster design
Replica set sizing, read and write concerns chosen per workload, and shard keys selected for cardinality and even distribution, with resharding planned when an early key choice stops scaling.
Change streams and integration
Change streams feeding search indexes, caches, webhooks or Kafka through the MongoDB connector, with resume tokens stored so consumers recover cleanly after restarts or failovers.
Why teams choose Krapton for MongoDB
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Queries drive the model
Every read and write path is listed before collections are designed, so the schema serves your product’s screens and APIs instead of mirroring a relational diagram.
Validation without rigidity
JSON Schema validators, typed Mongoose or driver models and versioned migration scripts keep documents consistent, while still letting fields evolve as the product changes.
Honest datastore advice
If your data is deeply relational or your team needs ad hoc SQL reporting, we recommend PostgreSQL or a warehouse instead, even when MongoDB was the starting assumption.
Operational habits built in
Backups with point-in-time restore, alerts on replication lag and slow operations, connection pool limits per service, and documented failover behavior for every driver in use.
How a MongoDB project runs with Krapton
01
Access-pattern workshop
We list the queries, writes, volumes and growth rates behind each feature, which become the input for collection design and index planning.
02
Model and prototype
Candidate document shapes are loaded with realistic synthetic data and tested through explain plans, so design flaws appear before application code depends on them.
03
Build and integrate
Application code, pipelines, search indexes and change stream consumers arrive in sprints, with each pull request reviewed for index use and write concerns.
04
Load test and operate
Production-scale load tests, alert thresholds, backup restore drills and capacity notes on when to scale vertically or add shards complete the delivery.
The MongoDB stack we build with
MongoDB development: questions answered
What does a MongoDB development company build?
It builds the data layer and the application features that depend on it. Typical work includes document schema design, aggregation pipelines for reporting, search with Atlas Search, semantic retrieval with Atlas Vector Search, event processing with change streams, and cluster planning for growth. Krapton also takes over existing MongoDB codebases where slow queries, bloated documents or a poor shard key have started to limit the product.
When is MongoDB the right choice, and when is it not?
MongoDB suits data that is read and written as whole aggregates, such as catalogs, user profiles, content and event records, especially when fields vary between records. It is a weaker choice for heavily relational data needing many joins, frequent multi-document transactions or ad hoc SQL analysis by business users. Multi-document transactions exist, but a design that depends on them everywhere usually belongs in a relational database.
Can your MongoDB development services migrate SQL data to MongoDB, or MongoDB to PostgreSQL?
Yes, in both directions, and the remodeling is the real work. Moving from SQL means collapsing related tables into documents shaped by access patterns rather than copying rows one to one. Moving from MongoDB to PostgreSQL means normalizing nested arrays and deciding which fields become columns versus JSONB. Either way we run both systems in parallel, compare query results and cut over feature by feature.
How do you fix slow MongoDB queries?
Start with the profiler and explain output to see whether queries scan whole collections, pick the wrong index or sort in memory. Most fixes are compound indexes ordered by the equality, sort, range rule, projections that return fewer fields, and pipelines that filter before $lookup or $unwind. Where documents have grown too large or arrays are unbounded, the schema itself must change, which we plan as a staged migration.
Should we use MongoDB Atlas, self-hosted MongoDB or a compatible service?
Atlas is usually the simplest path because backups, upgrades, Search and Vector Search come managed. Self-hosting the Community or Enterprise edition makes sense when data residency or existing infrastructure requires it, at the cost of running replication, monitoring and upgrades yourself. Compatible services such as Amazon DocumentDB implement much of the MongoDB API but not all of it, so we test your queries before recommending one.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.