ClickHouse vs DuckDB
- ✦ Vectorized query execution
- ✦ Real-time ingestion
- ✦ Kafka integration
- ✦ In-process execution
- ✦ Parquet/CSV/JSON direct query
- ✦ S3 and GCS file access
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Ingest clickstream events via Kafka, query 100B rows in under 1 second for live dashboards
Store and query server logs at petabyte scale with 10x better compression than Elasticsearch
Count unique users and calculate click-through rates across billions of ad impressions in milliseconds
Replace InfluxDB with ClickHouse for metrics storage, gaining SQL query support and better compression
Query 50GB Parquet files on S3 directly from Python without ETL, returning results in seconds
Replace pandas aggregations with SQL-based DuckDB queries for 10-50x faster group-by operations
Run dbt models locally against DuckDB instead of cloud warehouses to cut development cycle time
Use DuckDB as a compute engine over Delta Lake or Iceberg tables without a dedicated cluster
Pricing Intelligence
ClickHouse Plans
Free tier available
- • Self-hosted
- • Full features
- • Community support
- • $0.20/hr compute from
- • Free trial credits
- • Managed service
- • Dedicated resources
- • SLA
- • Enterprise support
DuckDB Plans
Free tier available
- • MIT license
- • Embedded in-process
- • No server needed
Feature Matrix
5 differences found across 14 standardized features
- •Column-oriented storage
- •Vectorized query execution
- •Real-time ingestion
- •Kafka integration
- •SQL support
- •Distributed tables
- •Compression (LZ4/ZSTD)
- •Approximate query functions
- •Materialized views
- •Replicated tables
- •JSON support
- •Geospatial functions
- •Time-series optimizations
- •REST and native protocols
- •In-process execution
- •Parquet/CSV/JSON direct query
- •S3 and GCS file access
- •SQL support
- •Python/R/Node.js integration
- •Vectorized execution
- •Parallel query processing
- •Apache Arrow integration
- •Zero-copy pandas exchange
- •Window functions
- •ACID transactions
- •Column-oriented storage
- •Schema inference
- •HTTPFS extension
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Fastest OLAP query performance for analytical queries at scale
- +Aggressive compression cuts storage costs 5-10x vs row-oriented DBs
- +Open source with full feature parity on self-hosted
- −Not designed for OLTP workloads or frequent row updates
- −Complex cluster configuration for self-hosted HA deployments
- +Runs in-process with zero infrastructure setup
- +Directly queries Parquet and CSV on S3 without ETL
- +Outperforms many server-based DBs on single-machine workloads
- −Single-node only - no horizontal scaling or clustering
- −Not suitable for multi-user concurrent write workloads
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.ClickHouse Official Pricing–Vendor pricing page
- 2.DuckDB Official Pricing–Vendor pricing page
- 3.ClickHouse Official Website–Official product website
- 4.DuckDB Official Website–Official product website