When should I use ClickHouse vs DuckDB?

Use ClickHouse for multi-terabyte datasets requiring 24/7 uptime, distributed query processing, and multiple concurrent users. Use DuckDB for local analytical workflows, data science projects, datasets under 100TB, and applications where simplicity matters more than clustering.

Can I migrate from DuckDB to ClickHouse?

Yes, but requires SQL dialect translation and schema adjustments. DuckDB's ANSI SQL must be converted to ClickHouse's custom syntax. Data export to Parquet/CSV and import into ClickHouse is straightforward. Most queries require rewriting due to dialect differences (estimated 30-50% manual effort).

What's the cost difference between ClickHouse and DuckDB?

DuckDB is free and open-source with no infrastructure costs. ClickHouse is free (open-source), but deployment costs $500-50,000+/month depending on cluster size, data volume, and infrastructure (CPU/storage). ClickHouse Cloud (managed) starts at $0 for shared tier or $1,000+/month for dedicated deployments.

How do query speeds compare for small datasets?

DuckDB is 10-100x faster than ClickHouse for datasets under 10GB due to single-machine optimization and lower overhead. ClickHouse's advantage emerges at 100GB+ where distributed parallelization and columnar compression outweigh startup costs. For datasets under 1TB, DuckDB typically wins on raw query speed.

Can DuckDB handle real-time analytics like ClickHouse?

DuckDB can ingest data in real-time at 10-50M rows/second, but lacks ClickHouse's distributed replication and fault tolerance. For single-machine real-time analytics (Kafka → DuckDB), DuckDB excels. For multi-node fault-tolerant streaming, ClickHouse is required.

ClickHouse vs DuckDB

Updated June 14, 2026

ClickHouse

Open-source distributed columnar database for real-time analytics at massive scale

Large enterprises, data warehousing teams, analytics platforms serving 100+ concurrent users

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DuckDB

Embedded analytical SQL database optimized for fast in-process queries on single machines

Data scientists, analytics engineers, embedded analytics in applications, local ETL workflows

Short Answer

ClickHouse is a distributed column-store database optimized for analytical queries on massive datasets across multiple servers, while DuckDB is an embedded in-process SQL database designed for analytical workloads on single machines. ClickHouse excels at petabyte-scale OLAP with horizontal scaling, whereas DuckDB prioritizes ease of use and performance for data analysis without infrastructure overhead.

Our Verdict

AI-assisted

Choose ClickHouse if you need to analyze petabyte-scale datasets across distributed infrastructure, require fault tolerance, or are building a shared analytics platform serving multiple teams. Choose DuckDB if you're performing local analytical queries, building data science workflows, need instant setup without DevOps overhead, or are embedding analytics in applications.

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ClickHouse6.9

8.1DuckDB

Choose ClickHouse if

Large enterprises, data warehousing teams, analytics platforms serving 100+ concurrent users

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Key Differences at a Glance

🔹

Architecture: ClickHouse wins (Distributed client-server with horizontal scaling vs Embedded in-process single-instance)

📏

Maximum Dataset Size: ClickHouse wins (Petabyte-scale (1000+ TB) vs Terabyte-scale (up to ~100 TB practical))

🔹

Setup Complexity: DuckDB wins (Zero setup - embedded library vs Requires cluster configuration and DevOps)

See all 7 differences

Key Facts & Figures

Metric	ClickHouse	DuckDB	Diff
Query Latency (1 billion rows)(seconds)	1.2 seconds	—	—
Monthly Cost (100 GB compressed)(USD)	$150	—	—
Ingestion Throughput(events/sec)	1,000,000 events/sec	—	—
Compression Ratio(ratio)	10:1 to 100:1	—	—

Key Differences

ClickHouse

Attribute

DuckDB

Distributed client-server with horizontal scaling🏆

Architecture

Embedded in-process single-instance

Petabyte-scale (1000+ TB)🏆

Maximum Dataset Size

Terabyte-scale (up to ~100 TB practical)

Requires cluster configuration and DevOps

Setup Complexity

Zero setup - embedded library

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Last updated: June 14, 2026AI generated

Architecture

ClickHouse

Distributed client-server with horizontal scaling🏆

DuckDB

Embedded in-process single-instance

Maximum Dataset Size

ClickHouse

Petabyte-scale (1000+ TB)🏆

DuckDB

Terabyte-scale (up to ~100 TB practical)

Setup Complexity

ClickHouse

Requires cluster configuration and DevOps

DuckDB

Zero setup - embedded library🏆

Query Performance (1GB dataset)

ClickHouse

~500-2000ms for aggregation queries

DuckDB

~10-50ms for aggregation queries🏆

Primary Use Case

ClickHouse

Large-scale distributed analytics infrastructure

DuckDB

Local data science and analytics workflows

Memory Footprint

ClickHouse

500MB-2GB per node minimum

DuckDB

10-50MB for typical operations🏆

SQL Compatibility

ClickHouse

Custom dialect (ClickHouse SQL)

DuckDB

Standard SQL (ANSI-compliant)🏆

Full Comparison

Attribute	ClickHouse	DuckDB

Query Latency (1 billion rows)(seconds)	1.2 seconds	—
Ingestion Throughput(events/sec)	1,000,000 events/sec	—
Query Latency (1GB aggregation)(milliseconds)	500-2000ms	10-50ms
Ingest Throughput(million rows/second)	1-5 million rows/sec	10-50 million rows/sec

Monthly Cost (100 GB compressed)(USD)	$150	—

Setup Time(minutes)	240 minutes	—

Data Retention for Time-Travel(days)	Not native	—

Compression Ratio(ratio)	10:1 to 100:1	—

Licensing Model	Open-source (free) + optional support	—

Learning Curve (1-10 scale)(difficulty rating)	7/10 (moderate-hard)	—

Maximum Cluster Size(nodes)	1000+	1 (single machine)

Compression Ratio (typical)(ratio)	10:1 to 40:1	4:1 to 8:1

Memory Required (minimal)(MB)	500-2000MB	10-50MB

Setup Time to First Query(minutes)	120-480 (with DevOps)	1-5 (pip install/npm)

SQL Standard Compliance(percent ANSI SQL)	70% (custom dialect)	95% (PostgreSQL-compatible)

GitHub Stars (2026)(stars)	38,000+	24,000+

ClickHouse

DuckDB

Query Latency (1 billion rows)(seconds)

1.2 seconds

—

Ingestion Throughput(events/sec)

1,000,000 events/sec

—

Query Latency (1GB aggregation)(milliseconds)

500-2000ms

10-50ms

Ingest Throughput(million rows/second)

1-5 million rows/sec

10-50 million rows/sec

Monthly Cost (100 GB compressed)(USD)

$150

—

Setup Time(minutes)

240 minutes

—

Data Retention for Time-Travel(days)

Not native

—

Compression Ratio(ratio)

10:1 to 100:1

—

Licensing Model

Open-source (free) + optional support

—

Learning Curve (1-10 scale)(difficulty rating)

7/10 (moderate-hard)

—

Maximum Cluster Size(nodes)

1000+

1 (single machine)

Compression Ratio (typical)(ratio)

10:1 to 40:1

4:1 to 8:1

Memory Required (minimal)(MB)

500-2000MB

10-50MB

Setup Time to First Query(minutes)

120-480 (with DevOps)

1-5 (pip install/npm)

SQL Standard Compliance(percent ANSI SQL)

70% (custom dialect)

95% (PostgreSQL-compatible)

GitHub Stars (2026)(stars)

38,000+

24,000+

Full Comparison

Attribute	ClickHouse	DuckDB

Query Latency (1 billion rows)(seconds)	1.2 seconds	—
Ingestion Throughput(events/sec)	1,000,000 events/sec	—
Query Latency (1GB aggregation)(milliseconds)	500-2000ms	10-50ms
Ingest Throughput(million rows/second)	1-5 million rows/sec	10-50 million rows/sec

Monthly Cost (100 GB compressed)(USD)	$150	—

Setup Time(minutes)	240 minutes	—

Data Retention for Time-Travel(days)	Not native	—

Compression Ratio(ratio)	10:1 to 100:1	—

Licensing Model	Open-source (free) + optional support	—

Learning Curve (1-10 scale)(difficulty rating)	7/10 (moderate-hard)	—

Maximum Cluster Size(nodes)	1000+	1 (single machine)

Compression Ratio (typical)(ratio)	10:1 to 40:1	4:1 to 8:1

Memory Required (minimal)(MB)	500-2000MB	10-50MB

Setup Time to First Query(minutes)	120-480 (with DevOps)	1-5 (pip install/npm)

SQL Standard Compliance(percent ANSI SQL)	70% (custom dialect)	95% (PostgreSQL-compatible)

GitHub Stars (2026)(stars)	38,000+	24,000+

ClickHouse

DuckDB

Query Latency (1 billion rows)(seconds)

1.2 seconds

—

Ingestion Throughput(events/sec)

1,000,000 events/sec

—

Query Latency (1GB aggregation)(milliseconds)

500-2000ms

10-50ms

Ingest Throughput(million rows/second)

1-5 million rows/sec

10-50 million rows/sec

Monthly Cost (100 GB compressed)(USD)

$150

—

Setup Time(minutes)

240 minutes

—

Data Retention for Time-Travel(days)

Not native

—

Compression Ratio(ratio)

10:1 to 100:1

—

Licensing Model

Open-source (free) + optional support

—

Learning Curve (1-10 scale)(difficulty rating)

7/10 (moderate-hard)

—

Maximum Cluster Size(nodes)

1000+

1 (single machine)

Compression Ratio (typical)(ratio)

10:1 to 40:1

4:1 to 8:1

Memory Required (minimal)(MB)

500-2000MB

10-50MB

Setup Time to First Query(minutes)

120-480 (with DevOps)

1-5 (pip install/npm)

SQL Standard Compliance(percent ANSI SQL)

70% (custom dialect)

95% (PostgreSQL-compatible)

GitHub Stars (2026)(stars)

38,000+

24,000+

ClickHouse vs DuckDB

ClickHouse

DuckDB

Short Answer

Our Verdict

Track this comparison

Key Differences at a Glance

Key Facts & Figures

Key Differences

Related Comparisons

Related Articles

Explore Entities

More Software

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Full Comparison

Visual Comparison

Pros & Cons

ClickHouse

Pros

Cons

DuckDB

Pros

Cons

Frequently Asked Questions

Resources & Learn More

Where to Buy

Wikipedia

Videos

Full Comparison

Visual Comparison

ClickHouse vs DuckDB

ClickHouse

DuckDB

Short Answer

Our Verdict

🔔Track this comparison

Key Differences at a Glance

Key Facts & Figures

Key Differences

Related Comparisons

Related Articles

Explore Entities

More Software

People Also Compare

Full Comparison

Visual Comparison

Pros & Cons

ClickHouse

Pros

Cons

DuckDB

Pros

Cons

Frequently Asked Questions

Resources & Learn More

Where to Buy

Wikipedia

Videos

Full Comparison

Visual Comparison

Track this comparison