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Kolmos
Engine Architecture Comparison

Kolmos vs DuckDB:
In-Process Embedded vs Cloud Database Server.

DuckDB is fantastic for running local analytics inside a Python or notebook script. Kolmos takes vectorized columnar speed to the cloud as a true multi-tenant database server with PostgreSQL, MySQL, and MongoDB wire protocols.

DuckDB vs Kolmos: Core Differences

Comparing embedded analytic libraries to cloud-native database infrastructure.

Capability
DuckDB
Kolmos Cloud Database
Deployment ModelIn-process embedded library (runs inside Python, R, Node.js process)Distributed cloud database engine & multi-tenant server
Network Wire ProtocolsNone (Embedded API only; requires third-party server wrappers)Native Port 5432 (Postgres), Port 3306 (MySQL), Port 27017 (MongoDB)
Concurrent Client AccessSingle-process write lock; unsuited for multiple concurrent microservicesHigh-concurrency multi-client connection pool with crash-safe WAL
Query Execution EngineVectorized columnar execution engine (DuckDB C++)Vectorized SIMD Arrow execution engine (Apache DataFusion in Rust)
Distributed StorageLocal single-file format (.duckdb) or manual external Parquet readsNative Content-Addressed Store on Cloudflare R2 ($0.015/GB/mo, zero egress)
Compression EfficiencyStandard dictionary & bitpacking compressionAutonomous Explanation Ladder (Formulas, prototypes, FastCDC deduplication)

When to Choose DuckDB

  • Local data science workflows inside Jupyter notebooks, Python, or R scripts.
  • Single-user CLI tools analyzing local CSV or Parquet files.

When to Choose Kolmos

  • Multi-tenant web applications where dozens of services connect concurrently over network protocols.
  • Using standard PostgreSQL (Prisma, Django), MySQL, or MongoDB drivers.
  • Long-term cloud storage on Cloudflare R2 with automatic chunk deduplication.