MongoDB development services for teams building on the most popular document database. Schema design modelled around your queries — not forced into tables. Atlas for managed infrastructure without cluster babysitting. Aggregation pipelines that replace what used to take three microservices. We build on MongoDB with the indexing discipline and schema patterns that keep it fast at 100 million documents — not just at 100 thousand.
Why Entalogics for MongoDB
The MongoDB deployments we audit always have the same problems — no indexes on fields used in every query, schemas that embed everything until documents hit the 16MB limit, replica sets nobody monitors for lag, and Atlas clusters sized at M30 when M10 handles the load. MongoDB is flexible. Most teams mistake that flexibility for "no rules."
Embed data that's read together. Reference data that's updated independently. Don't embed unbounded arrays that grow forever. The schema you design in month one determines your query performance in month twelve.
Compound indexes matching your most common query patterns. Covered queries that return results from the index alone. Unused indexes dropped — they slow writes without helping reads. `explain()` before production, always.
`$match` → `$lookup` → `$group` → `$project` in the database, not in your Node.js service. Pipeline stages ordered so `$match` narrows the dataset before expensive operations. One round trip instead of five queries stitched together in code.
Atlas handles replication, backups, scaling, and patching. Self-managed MongoDB requires dedicated ops expertise that most teams don't have. We default to Atlas and only recommend self-managed when compliance or cost genuinely demands it.
When MongoDB, when not
MongoDB excels at flexible document storage, rapid iteration, and horizontal scaling. It also makes relational queries harder, lacks transactions across shards by default, and punishes bad schema design silently. We'll tell you on the first call if MongoDB fits.
PICK MONGODB WHEN
CONSIDER POSTGRESQL WHEN
WE SAY NO WHEN
What we build on MongoDB
The shapes of MongoDB development we deliver most. Each schema-designed and index-optimised.
Schema patterns, index strategy, aggregation pipelines. The document data layer your application needs — fast, flexible, and maintainable at scale.
Cluster sizing, auto-scaling configuration, backup policies, network peering. Atlas configured for your workload — not left on defaults.
Atlas Search for full-text queries. Atlas Vector Search for AI embeddings. Combined search + filter queries without a separate search engine.
Complex data transformations, reporting queries, and analytics built as pipelines — not application code. Stages ordered for performance.
`explain()` analysis, index review, slow query profiling, schema refactoring. Queries taking seconds reduced to milliseconds — measured.
SQL to MongoDB schema modelling. Mongoose to native driver. Self-managed to Atlas. Version upgrades. Data migrated with validation — not hope.
The playbook
MongoDB patterns from real production deployments — not tutorial examples.
P01
Access patterns documented. Embed vs reference decisions justified per relationship. Unbounded arrays avoided. Schema that performs at scale — not just at demo.
P02
Index fields in the order queries filter and sort. Covered queries where possible. `explain()` confirming index usage on every hot-path query. No COLLSCAN in production.
P03
Data transformation in the database, not in your service. Pipeline stages ordered to narrow data early. `$match` before `$lookup`. `$project` before `$group`. One round trip.
P04
Cluster tier auto-scales with traffic. Scale-down configured to avoid paying peak pricing 24/7. Alerts on IOPS, connections, and oplog lag.
P05
Change Streams replacing polling for real-time features. Resume tokens persisted for recovery. Event-driven architecture without a separate message broker.
P06
JSON Schema validation on every collection. Required fields, type enforcement, enum constraints. The flexibility of a document database with the safety of schema enforcement.
Signature case
A B2B SaaS platform on MongoDB — every listing page running full collection scans, no compound indexes, embedded arrays growing unbounded, and Atlas cluster at M40 because nobody profiled the actual workload. Remodelled schema, added compound indexes, capped embedded arrays, configured aggregation pipelines, and downsized to M20 in 7 weeks. Average query time dropped from 1.2s to 18ms. Atlas bill dropped 48%.
Before
COLLSCAN on listing pages · 0 compound indexes · unbounded arrays · M40 cluster · 1.2s avg query
After
Index-covered queries · 8 compound indexes · capped arrays · M20 cluster · 18ms avg query
Engagement shape
A typical MongoDB engagement. We design or tune collection by collection — the current database stays live while we work.
Two senior MongoDB engineers. `explain()` analysis, index audit, schema review, Atlas cluster assessment. A ranked, dollarized RFC.
Top slow queries indexed, unbounded arrays capped, schema validation added. Measurable improvement in week two.
Aggregation pipelines built, schema remodelled where needed, Atlas right-sized. Your application keeps running.
Profiler monitoring configured. Atlas alerts live. Runbook handed to your team — or we stay on retainer.
Stack
Our default MongoDB development stack — picked for production.
Engagement
No hourly retainer that bills for "thinking time." Pick a lane that matches your stage; everything is fixed-quote or transparently rated.
A defined scope, a fixed price, a senior-only team. From schema design to production in 6–10 weeks.
$15k–$30k
FIXED SCOPE
Embedded engineers in your team. Senior database engineers specialising in MongoDB. Pause, resize, end with 30 days' notice.
$5k / eng / mo
PER ENGINEER
A long-term partner for MongoDB-powered products — schema evolution, Atlas optimisation, search integration, hiring help.
custom
PROCUREMENT-FRIENDLY
Founder-direct
Thirty minutes with the founder. We'll bring a senior MongoDB engineer, the relevant playbook, and a candid read on whether MongoDB is the right database — or whether PostgreSQL or a different document store fits your data better.
