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Kolmos
Architectural Comparison

Kolmos vs MongoDB:
WiredTiger Disk Overhead vs R2 Columnar HTAP.

MongoDB offers flexible document modeling but becomes exorbitantly expensive at scale. Kolmos lets you keep Mongoose and MongoDB drivers while eliminating the need for expensive ETL warehouses.

Direct Technical Comparison

Comparing MongoDB Atlas against the Kolmos document storage engine.

Capability
MongoDB Atlas
Kolmos Cloud Database
Storage FormatWiredTiger block engine (row/document disk format, high disk overhead)Columnar Explanation Segments (.ksf) on Cloudflare R2
Storage EconomicsAtlas on AWS EBS ($0.12–$0.25/GB/mo) + costly provisioned IOPS$0.015/GB/mo flat on Cloudflare R2 with zero data egress fees
Complex AggregationsSingle-node document traversals; struggles on billion-row analyticsVectorized SIMD Arrow execution via embedded Apache DataFusion
Need for Separate Data WarehouseRequires complex ETL pipelines to Snowflake or BigQuery for reportingUnified HTAP: Query document collections as fast columnar data directly
Driver & Framework CompatibilityOfficial MongoDB ecosystem (Mongoose, PyMongo, Compass)100% Wire Compatible on Port 27017 (BSON OP_MSG protocol)
Point Lookup Speed1–2ms via WiredTiger B-Trees2–4ms via in-memory PK Bloom filter sidecars

When to Stick with MongoDB Atlas

  • Heavy reliance on Atlas proprietary search (Lucene Atlas Search integration).
  • Complex multi-shard sharded clusters across multiple cloud regions.

When to Choose Kolmos

  • Terabytes of event logs or documents causing astronomical Atlas storage bills.
  • Running heavy analytical aggregations ($group, $match) without building ETL pipelines.
  • Zero code refactoring: connect using your existing MONGO_URI in Mongoose.