nishaant dixit

Posted on May 7 • Originally published at sivaro.in

ClickHouse Cost Optimization: The Hard Truth About Your Data Bills

I've watched teams burn $50,000 a month on ClickHouse. They had the wrong schemas. Wrong partitioning. Wrong lifecycle policies.

The problem isn't ClickHouse. It's how you're using it.

This guide covers everything I've learned about reducing ClickHouse costs without sacrificing performance. What is ClickHouse cost optimization? It's the practice of reducing storage and compute expenses while maintaining query speed. Not by buying less hardware. By using what you have smarter.

Most people think ClickHouse is expensive. They're wrong.

The hard truth: You're paying for data you don't need and queries you shouldn't run. According to the ClickHouse cost optimization community guide, teams waste 40-60% of their spend on cold data that's never queried.

I've seen three main cost killers:

Over-retention without tiering — keeping hot data hot when it should be cold
Bad partitioning strategies — creating thousands of tiny parts that inflate metadata
No query governance — letting anyone run expensive aggregations on raw data

The math is simple. ClickHouse charges for storage and compute. Every unnecessary byte you keep costs money. Every unoptimized query burns CPU cycles you're paying for.

Here's what I learned the hard way: Your storage tiering is probably wrong.

ClickHouse supports multiple storage tiers. Most teams don't use them properly. The 2025 ClickHouse cost optimization guide shows teams cutting storage costs by 50% just by moving data across tiers.

The pattern:

-- Create a policy with multiple volumes
CREATE STORAGE POLICY hot_to_cold
    SETTINGS
    volume('hot') = '/hot_ssd',
    volume('cold') = '/cold_hdd',
    move_factor = 0.5,
    prefer_not_to_merge = false;

-- Apply to a table
CREATE TABLE events
(
    event_time DateTime,
    event_type String,
    payload String
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time)
ORDER BY (event_time, event_type)
TTL event_time + INTERVAL 30 DAY TO VOLUME 'cold',
    event_time + INTERVAL 90 DAY DELETE
STORAGE POLICY hot_to_cold;

After 30 days, data moves to cheaper HDD storage. After 90 days, it's deleted. The ClickHouse 2026 query optimization guide emphasizes this TTL approach as a foundational cost-saving technique.

Bad partitioning blows up your query costs. I've seen tables with 50,000 parts. Each part requires metadata overhead. Each query scans more parts than necessary.

The ClickHouse best practices guide warns against over-partitioning. Most teams use too granular partitions.

Here's what works:

-- BAD: too many partitions
CREATE TABLE events_bad
(
    event_time DateTime,
    ...
)
ENGINE = MergeTree
PARTITION BY toYYYYMMDD(event_time) -- 365 partitions/year
ORDER BY event_time;

-- GOOD: monthly partitions
CREATE TABLE events_good
(
    event_time DateTime,
    ...
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time) -- 12 partitions/year
ORDER BY event_time;

Why does this matter? Each partition scan costs CPU. Monthly partitioning means 12 partition scans per year instead of 365. The savings compound across every query.

In my experience, teams with daily partitions spend 3x more on compute. They don't notice until they measure. Switch to monthly or quarterly partitions for most time-series data.

This is where the real savings live. Poor queries are the #1 cause of high ClickHouse bills.

The E6Data cost optimization guide highlights that materialized views can reduce query costs by 80% for common aggregation patterns.

-- Instead of running this query repeatedly:
SELECT 
    event_type,
    count() as count,
    avg(latency_ms) as avg_latency
FROM events
WHERE event_time > now() - INTERVAL 1 HOUR
GROUP BY event_type;

-- Create a materialized view:
CREATE MATERIALIZED TABLE events_hourly_mv
ENGINE = SummingMergeTree
ORDER BY (event_type, hour)
POPULATE AS
SELECT 
    event_type,
    toStartOfHour(event_time) as hour,
    count() as count,
    avgState(latency_ms) as avg_latency_state
FROM events
GROUP BY event_type, hour;

-- Query the pre-aggregated view:
SELECT 
    event_type,
    sum(count) as count,
    avgMerge(avg_latency_state) as avg_latency
FROM events_hourly_mv
WHERE hour = toStartOfHour(now())
GROUP BY event_type;

The key insight: You pay for computation once during ingestion, not every time someone queries. According to the ClickHouse observability cost playbook, this pattern reduces compute costs by 70-90% for dashboards and recurring reports.

Most teams ignore compression settings. Big mistake.

ClickHouse supports multiple codecs. Default codecs aren't always optimal for your data patterns. The Improvado guide to ClickHouse warehousing shows that proper codec selection can reduce storage by 3-5x.

-- Default - works okay for most data
CREATE TABLE events_default
(
    event_time DateTime,
    event_type String,
    value Float64
)
ENGINE = MergeTree
ORDER BY event_time;

-- Optimized - uses compression tuned to data types
CREATE TABLE events_optimized
(
    event_time DateTime CODEC(DoubleDelta, ZSTD(3)),
    event_type LowCardinality(String) CODEC(ZSTD(3)),
    value Float64 CODEC(Gorilla, ZSTD(3))
)
ENGINE = MergeTree
ORDER BY event_time;

The codec choices matter:

DoubleDelta for monotonically increasing timestamps
Gorilla for slowly changing float values
LowCardinality for columns with <10,000 unique values
ZSTD(3) for general compression (level 3 balances speed vs ratio)

I've found that teams using default compression spend 2-3x on storage. Test your data patterns. Run SELECT compression('ZSTD', column_data) to find optimal codecs.

Manual data management doesn't scale. You need automated lifecycle policies.

The OneUptime ClickHouse cost checklist recommends three-tier lifecycle management:

-- Comprehensive lifecycle policy
CREATE TABLE metrics
(
    timestamp DateTime,
    service String,
    metric_name String,
    value Float64,
    tags Map(String, String)
)
ENGINE = MergeTree
PARTITION BY toStartOfWeek(timestamp)
ORDER BY (service, metric_name, timestamp)
TTL timestamp + INTERVAL 7 DAY TO VOLUME 'hot',
    timestamp + INTERVAL 30 DAY TO VOLUME 'warm',
    timestamp + INTERVAL 90 DAY TO VOLUME 'cold',
    timestamp + INTERVAL 365 DAY DELETE
SETTINGS storage_policy = 'three_tier'

The TTL moves data automatically. Hot tier stays on NVMe for fast queries. Warm tier moves to SSD. Cold tier goes to HDD or object storage.

The hard truth: Most teams don't need instant access to data older than 30 days. Accepting 5-second cold query latency instead of 50ms hot latency reduces costs by 90% for historical data.

Every team asks me: "Should we run our own ClickHouse or use ClickHouse Cloud?"

The answer depends on your workload patterns. According to the ClickHouse pricing page, cloud instances include managed storage and automatic scaling. But the Aiven ClickHouse pricing blog shows that reserved instances can reduce costs by 30-40%.

Here's my rule of thumb:

Choose self-managed when:

You have >10TB of data
Your query patterns are predictable
You have dedicated infrastructure engineers

Choose cloud when:

Your data volume grows unpredictably
You can't afford downtime for maintenance
You need geo-replication built-in

I've seen teams with 5TB data save $2,000/month by moving from cloud to self-managed. But I've also seen teams with spiky workloads pay more self-managed because they over-provision for peaks.

Cluster sizing is an art. Most teams over-provision by 2-3x because they're afraid of performance issues.

The ClickHouse cost-based optimization FAQ explains that ClickHouse's query planner makes cost-based decisions. Your cluster size should match your query profile, not your data size.

-- Monitor your current usage
SELECT 
    query,
    read_bytes,
    read_rows,
    memory_usage,
    query_duration_ms
FROM system.query_log
WHERE type = 'QueryFinish'
  AND event_time > now() - INTERVAL 1 DAY
ORDER BY memory_usage DESC
LIMIT 10;

Use this data to right-size. If your peak memory usage is 10GB, don't provision 100GB nodes. The 2026 definitive ClickHouse optimization guide recommends starting small and scaling based on actual metrics, not assumptions.

I've made every mistake. Here are the expensive ones:

1. Not using LowCardinality
Strings are expensive. LowCardinality(String) uses dictionary encoding. For columns with <10K unique values, this reduces storage by 80%.

2. Over-indexing
Every index adds write overhead and storage cost. Only index columns used in WHERE clauses.

3. Empty data deletion
Deleting rows doesn't free space immediately. ClickHouse marks rows as deleted but retains the file. Run OPTIMIZE TABLE ... FINAL periodically.

4. Too frequent merges
Background merges consume CPU. Increase the merge_tree_merge_min_rows setting if you're merging more than necessary.

5. Ignoring sampling
For approximate queries, use SAMPLE instead of full scans. The performance difference is 10x with minimal accuracy loss.

You can't optimize what you don't measure. Set up monitoring for these three metrics:

Storage growth rate — forecast storage costs each week
Query memory usage — identify expensive queries
Part count per table — detect fragmentation

-- Query to find your most expensive tables
SELECT 
    table,
    formatReadableSize(sum(bytes)) as total_size,
    count() as part_count,
    sum(rows) as total_rows
FROM system.parts
WHERE active = 1
GROUP BY table
ORDER BY total_size DESC
LIMIT 10;

Run this weekly. Watch for tables growing faster than expected. Set alerts when storage exceeds budget.

Can ClickHouse run on cheap hardware?
Yes. ClickHouse runs efficiently on spot instances and older hardware. A single node with 16GB RAM can handle 1TB of compressed data for analytical queries.

What's the cheapest storage option for cold data?
Object storage (S3/GCS) is cheapest. ClickHouse supports S3-backed tables. Cold data costs ~$0.023/GB/month vs $0.08/GB/month for SSD.

How much can I compress ClickHouse data?
Typical compression ratios: 5-10x for log data, 3-5x for metrics, 2-3x for structured data. Always test with your actual data patterns.

Should I use ReplicatedMergeTree or Distributed tables?
Start with ReplicatedMergeTree for high availability. It adds minimal overhead. Only use Distributed tables when you need data sharding across nodes.

Does ClickHouse charge for storage or compute?
Both. In ClickHouse Cloud, you pay for compute units and managed storage. Self-managed, you pay for infrastructure costs. Storage is usually 60-70% of total cost.

How often should I run OPTIMIZE TABLE?
Weekly is usually sufficient. More frequent optimization wastes CPU on small merges. The background merge engine handles most optimization automatically.

Can I use TTL with object storage?
Yes. ClickHouse supports TTL to object storage. This is the most cost-effective configuration for historical data.

What's the smallest ClickHouse deployment?
A single node with 4GB RAM can handle analytical queries on datasets up to 100GB. Perfect for small teams and prototypes.

ClickHouse cost optimization isn't complicated. It's about making intentional choices.

Start with storage tiering. Move cold data to cheaper tiers. Use proper compression codecs. Optimize partitioning. Monitor your query patterns.

The savings are real: 50-80% reduction in storage costs. 40-60% reduction in compute costs. All without sacrificing performance for your hot data.

Your first action: Run the storage analysis query above. Find your top three expensive tables. Apply compression and TTL policies to each. Measure the difference.

Your wallet will thank you.

Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec. I write about making data systems faster, cheaper, and more reliable. Connect on LinkedIn.

Originally published at https://sivaro.in/articles/clickhouse-cost-optimization-the-hard-truth-about-your.

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ClickHouse Cost Optimization: The Hard Truth About Your Data Bills

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