clickhouse-cost-tuning

name	clickhouse-cost-tuning
description	Optimize ClickHouse Cloud costs — compute scaling, storage tiering, compression, and query efficiency for lower bills. Use when analyzing ClickHouse Cloud bills, reducing storage costs, or optimizing compute utilization. Trigger with "clickhouse cost", "clickhouse billing", "reduce clickhouse spend", "clickhouse pricing", "clickhouse expensive", "clickhouse storage cost".
allowed-tools	Read
version	1.7.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","database","analytics","clickhouse","olap"]
compatibility	Designed for Claude Code

ClickHouse Cost Tuning

Overview

Reduce ClickHouse Cloud costs through storage optimization, compression tuning, TTL policies, compute scaling, and query efficiency improvements. This skill walks the bill from top driver to fix: identify what you actually pay for, then apply the codec, TTL, compute, and query changes that move the number.

Deep copy-paste queries for every step live in references/implementation.md; end-to-end scenarios live in references/examples.md.

Prerequisites

• ClickHouse Cloud account with billing access
• Understanding of current data volumes and query patterns

Instructions

Step 1: Understand what you pay for

ClickHouse Cloud bills on four axes — and the biggest one is usually compute, not storage, because ClickHouse compresses data 10-20x.

Component	Pricing Model	Key Driver
Compute	Per-hour per replica	vCPU + memory tier
Storage	Per GB-month	Compressed data on disk
Network	Per GB egress	Query result sizes
Backups	Per GB stored	Backup retention

Step 2: Find the top cost driver

Break storage down by table, then by column, to find bloated data. The starter query — full breakdowns in references/implementation.md:

SELECT database, table,
    formatReadableSize(sum(bytes_on_disk)) AS compressed_size,
    round(sum(data_uncompressed_bytes) / sum(bytes_on_disk), 1) AS compression_ratio
FROM system.parts WHERE active
GROUP BY database, table ORDER BY sum(bytes_on_disk) DESC;