Kolmos is a cloud database engine built for PostgreSQL, MySQL, and MongoDB workloads. It combines self-compressing storage, columnar architecture, and multi-wire compatibility to reduce database storage and cloud infrastructure costs.

Which database wire protocols does Kolmos support?

Kolmos natively supports PostgreSQL (port 5432), MySQL (port 3306), and MongoDB (port 27017) wire protocols. Standard drivers, ORMs (Prisma, Drizzle, SQLAlchemy, Hibernate), and database clients connect seamlessly with zero application code changes.

How does Kolmos reduce cloud database storage costs?

Kolmos uses an autonomous Explanation Ladder that mines Minimum Description Length (MDL) formulas and prototype clusters to compress data 1.66×–2.12× smaller than Parquet-zstd. It stores files directly on Cloudflare R2 object storage ($0.015/GB/mo) with zero data egress fees, cutting overall cloud database storage bills by 10× to 40×.

Is Kolmos data recovery 100% lossless?

Yes. Kolmos guarantees 100% mathematical fidelity via Invariant #1: decode(encode(x)) == x across all SQL and BSON data types. Every row decodes bit-perfectly with zero precision drift.

How does Kolmos execute fast analytical queries on cloud object storage?

Kolmos embeds min/max statistics and Bloom filters into segment headers. Using Apache DataFusion vectorized execution and predicate pushdown, the engine prunes irrelevant segments and streams zero-copy Apache Arrow memory buffers without downloading unneeded data.

What prevents vendor lock-in with Kolmos storage formats?

Every Kolmos segment footer pins an open, self-contained WASM decoder covenant executed in an isolated Wasmtime sandbox. This guarantees your database files remain readable perpetually decades later without requiring proprietary server runtimes.

Home/Journal/PostgreSQL

PostgreSQL·15 min read·November 1, 2026

KOLMOS vs PostgreSQL: Architecture, Storage Compression & Cost Comparison

A detailed engineering comparison between standard PostgreSQL and KOLMOS: storage bloat, vacuum overhead, vectorized SIMD execution, cloud storage economics, and drop-in wire protocol compatibility.

KOLMOS Engineering Team

Database Core & Storage Architecture · KOLMOS Systems

Follow on LinkedIn

KOLMOS vs PostgreSQL: Architecture and Cost Comparison

Key Takeaway: PostgreSQL is the gold standard for developer ergonomics and transactional reliability. But at petabyte scale, PostgreSQL's row-based heap pages and MVCC vacuum mechanics cause storage bloat and soaring cloud infrastructure costs. KOLMOS provides native PostgreSQL wire compatibility (port 5432) backed by a self-compressing columnar storage engine that cuts storage costs by up to 90% while executing analytical queries at vectorized speeds.

Head-to-Head Comparison: PostgreSQL vs KOLMOS

Feature / Metric	PostgreSQL (Standard RDS / Aurora)	KOLMOS Cloud Database Engine
Protocol Compatibility	Native PostgreSQL	100% Wire Compatible (Port 5432 pgwire)
Storage Architecture	8KB Row-Oriented Heap Pages	Immutable Self-Compressing Columnar Segments
Storage Medium	AWS EBS gp3 ($0.08–$0.12/GB/mo)	Cloudflare R2 / S3 ($0.015/GB/mo)
Vacuum & Bloat	Requires Autovacuum & `pg_repack`	Zero Vacuum Bloat (Immutable LSM segments)
Compression Ratio	1.0× (TOAST strings only)	1.66×–2.12× smaller than Parquet-zstd
Query Engine	Volcano iterator row-by-row CPU	Vectorized SIMD via Apache DataFusion & Arrow
Point Lookups	1–2ms via B-Tree index	2–4ms via in-memory PK Bloom filter sidecars
Data Egress Cost	Up to $0.09 per GB transferred	$0.00 (Zero egress fees on Cloudflare R2)

Technical Blueprint

Storage PlaneCloudflare R2 CAS

Execution CoreDataFusion SIMD

Decode Fidelity100% Bit-Exact

Wire DoorsPG · MySQL · Mongo

Official Channels

linkedin.com/company/kolmos

kolmos.dev

PostgreSQL

PostgreSQL Compression: How to Reduce Database Storage Without Sacrificing Performance

PostgreSQL

How to Make PostgreSQL Cheaper Without Sacrificing Performance

Cloud Costs

How to Reduce Amazon Aurora Costs in 2026: Taming the I/O and Storage Beast

1. Storage Architecture: 8KB Heap vs Columnar Segments

In standard PostgreSQL, all table data is stored in 8KB block pages on local or attached block storage. While this is optimal for single-row OLTP transactions, it introduces major pain points as tables scale beyond millions of rows:

Every column in a row is stored together, forcing the database to read entire rows into RAM even if your query only selects 2 columns.

Deleted or updated rows remain on disk as dead tuples until VACUUM processes them, leading to 20%–50% storage bloat.

KOLMOS stores records in columnar format:

Data for each column is stored contiguously in memory and on disk.

Aggregations (SUM, AVG, COUNT, filter scans) read only the exact columns requested, reducing I/O bandwidth by up to 10×.

Segments are compacted in the background into immutable .ksf archives, completely eliminating dead tuple bloat.

2. The Cloud Economics Difference

Consider a 10 TB production analytical dataset on AWS RDS PostgreSQL vs KOLMOS:

AWS RDS PostgreSQL (10 TB gp3 + Backups + I/O):

- Storage: 10,000 GB × $0.115 = $1,150/month - Provisioned IOPS (12,000 IOPS): ~$350/month - Multi-AZ Replication: $1,150/month - Total: ~$2,650/month

KOLMOS Cloud Storage (Compressed to ~2.5 TB on R2):

- 2,500 GB on Cloudflare R2 × $0.015 = $37.50/month - Zero egress fees on queries - Total: ~$75/month

This represents a 35× reduction in monthly storage bills without sacrificing PostgreSQL driver compatibility.

Explore Related Research & Documentation

What is KOLMOS? Complete Overview

PostgreSQL Compression & Storage Optimization Guide

How to Reduce Amazon Aurora I/O Costs

Database Benchmarks Suite

PostgreSQL Feature Parity Page

Try KOLMOS Today

Deploy Your First Self-Compressing Store.

Connect via PostgreSQL, MySQL, or MongoDB. 10 GB free developer storage included.

Start Free Trial ⚡Inspect Architecture