Technologies · MongoDB
MongoDB consulting for startups
We design, tune, and scale MongoDB: schema patterns that match your query shapes, indexes that make p95s boring, Atlas configured for HA without the runaway bill, and honest guidance on when sharding is real and when it's premature. Most 'MongoDB is slow' problems are schema and index problems — fixable in days.
Reference architecture
How we build with MongoDB
App
Cluster
Design
Guard
Scope
What our MongoDB consulting covers
- Schema review: embedding vs referencing decided by access patterns, not habit
- Index tuning from real query profiles — and deleting the indexes that only cost writes
- Atlas architecture: cluster tier right-sizing, backup policy, network peering, cost control
- Replica-set health: read preferences, write concerns, failover tested on purpose
- Migrations: self-hosted to Atlas, or major-version upgrades without downtime
System design
How we scale MongoDB
- 1
Schema is the first scaler: documents shaped like your queries turn N round-trips into one — the biggest wins predate any hardware change
- 2
Indexes match the workload: compound indexes ordered by equality-sort-range, covered queries for hot paths, and TTL indexes doing your data retention
- 3
Reads scale on secondaries where staleness is acceptable (analytics, feeds) — with explicit read concerns so correctness is a decision, not luck
- 4
Sharding is a last resort with a date: we size when it's genuinely needed and pick the shard key carefully, because bad shard keys are forever
In practice
What a typical engagement looks like
A startup's Atlas bill and p95 latency both climb monthly. Query profiling finds three collection scans on hot paths and 14 unused indexes taxing writes; two schema embeds remove the worst N+1 pattern. p95 drops from 900ms to 70ms, and the cluster steps down two Atlas tiers — the bill falls ~45% while getting faster.
Illustrative engagement — representative of typical work at typical scale, not a specific client. See a full sample audit deliverable here.
MongoDB work is covered by the Fractional DevOps retainer ($2,900/mo) and scoped fixed-price projects — start with the guaranteed $1,900 audit if you want findings before commitments.
Questions
MongoDB, straight answers
Atlas or self-hosted MongoDB?
Atlas for almost every startup: managed HA, backups, and upgrades outweigh the premium at small-team scale, and its per-tier pricing stays reasonable if someone right-sizes it (that's the part teams skip). Self-hosting makes sense at large scale or under strict data-locality constraints — with eyes open about the operational load.
When should we shard?
Later than you think. Sharding adds permanent operational and query complexity, and most workloads are rescued first by schema fixes, indexes, and read scaling on secondaries. The honest trigger is working-set or write throughput genuinely exceeding one primary's ceiling — we'll show you the math on your own metrics before recommending it.
Related technologies
Need senior MongoDB help without the hire?
A 15-minute call is enough to tell you exactly what we'd do and what it costs. No pitch deck, no pressure.