Schema design, performance optimization, and MongoDB infrastructure built by senior engineers who've shipped for companies like Setu, ESPN, and KredX.
Trusted by engineering teams at
Document schema design that balances read performance, write efficiency, and storage costs. We model your data around actual query patterns, not relational instincts. Embedding vs referencing, denormalization strategies, and polymorphic patterns designed for how your application actually accesses data, not how an ER diagram looks on a whiteboard.
Slow MongoDB queries found and fixed. We analyze query plans with explain(), design compound indexes that cover your most frequent operations, and eliminate collection scans. Index strategy is not just adding indexes to every field. We balance read speed against write overhead and storage impact. Typical result: 10-50x improvement on poorly indexed collections.
Full Atlas lifecycle management: cluster sizing, tier selection, backup configuration, network security, and cost optimization. We configure auto-scaling rules that match your traffic patterns, set up monitoring alerts, and implement access controls with database-level RBAC. If you are overpaying for your Atlas tier, we will find it.
Complex data transformations, reporting queries, and analytics built with MongoDB's aggregation framework. We design pipelines that handle grouping, lookups, windowing, and statistical operations without requiring a separate analytics database. For full-text and vector search, we integrate Atlas Search and Atlas Vector Search directly into your query layer.
Incremental migration from relational databases (PostgreSQL, MySQL, SQL Server) to MongoDB. We map relational schemas to document models, build data migration scripts with validation, and run dual-write patterns during transition. No big-bang cutovers. Your application stays live throughout. Typical migration: 4-12 weeks depending on data volume and complexity.
Event-driven architectures using MongoDB Change Streams. We build real-time notification systems, cache invalidation pipelines, and cross-service data synchronization triggered by database changes. Change Streams provide at-least-once delivery with resume tokens for fault tolerance, no separate message broker required for many use cases.
Relational thinking produces bad MongoDB schemas. We design documents around how your application reads and writes data, not how entities relate on a whiteboard. The schema serves the queries, not the other way around.
Indexes are not an afterthought. We plan indexing strategy during schema design because the two are inseparable. A well-indexed collection with the right schema serves queries in under a millisecond. A poorly indexed one scans millions of documents.
Not everything belongs in one document. We balance embedding for read performance against referencing for write efficiency. Documents that grow without bound (unbounded arrays, unlimited nested objects) cause performance degradation. We design schemas with growth patterns in mind.
Sharding adds complexity. Replica set topologies add complexity. We start with the simplest architecture that meets your requirements and add complexity only when data volume or traffic demands it. Most applications under 500 GB perform well on a properly indexed single replica set.
Every MongoDB system we design can be maintained by your team or a different vendor. Documented schema decisions, migration scripts, and operational runbooks. You own everything, always.
We analyze your data access patterns, query frequency, and growth projections. You get a schema design document covering collection structure, indexing strategy, embedding vs referencing decisions, and sharding considerations. No implementation until the data model is validated against your actual workloads.
Document schemas designed and validated with sample data. Compound indexes planned based on query patterns identified in discovery. Migration strategy defined if moving from a relational database. Atlas cluster configuration specified for your traffic and storage requirements.
Sprint-based MongoDB development with working endpoints and database operations shipped every two weeks. Data access layer built with Mongoose or Prisma based on your stack. Aggregation pipelines developed and performance-tested against realistic data volumes. Your team gets staging access from week one.
We load test with production-scale data volumes, analyze slow query logs, and tune indexes and aggregation pipelines. Connection pool sizing, read preference configuration, and write concern tuning based on your consistency requirements. Nothing ships until queries meet your latency targets.
Complete documentation covering schema decisions, indexing rationale, operational runbooks, and monitoring setup. Your team owns the database architecture. Optional support retainer for ongoing optimization, but no lock-in.
Engineers with 3+ years building production MongoDB systems. Full database capability spanning schema design, query optimization, aggregation pipelines, and Atlas management. Backend development experience in Node.js, Python, or Go included.
A self-contained team that owns your MongoDB-backed application end to end. Schema design, API development, performance optimization, and deployment covered.
Fixed-scope engagement with clear deliverables, timeline, and budget. Defined schema design, agreed milestones, and transparent pricing.
Talk directly with engineers, not sales. We will assess fit and give honest next steps.
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MongoDB development costs depend on scope. Schema design and optimization for an existing application typically runs $10,000 to $30,000. A full application backend with MongoDB, including API layer and data modeling, sits in the $40,000 to $120,000 range. Enterprise platforms with sharding, Atlas Search integration, and complex aggregation pipelines can run $100,000 to $250,000 or more. Our initial architecture consultation is free.