DuckDB or ClickHouse: Choosing the Right Engine for Embedded Analytics

Deployment architectures

DuckDB operates as an in-process engine within a host process, which removes network overhead and eliminates the need for a separate server. The entire source tree compiles into a header and an implementation file, which simplifies deployment. DuckDB uses a columnar, vectorized execution engine that leverages SIMD instructions to process data in batches. This engine uses Morsel-driven parallelism to take advantage of multi-core processors. DuckDB is released under the MIT License and compiles for all major operating systems and CPU architectures like x86 and ARM. Because DuckDB runs within the same process as your application, it eliminates the need to move data between systems and provides high-speed data transfer to and from the database. ClickHouse runs as a dedicated server or cluster of servers and handles concurrent queries from many clients through HTTP or native protocols. ClickHouse requires infrastructure decisions like server sizing, replication, and sharding strategies. To keep p95 stability, teams must tune merges, partitions, and TTLs so ingestion does not starve queries. While DuckDB allows you to query Parquet, CSV, JSON, or Avro files directly without an ingestion step, ClickHouse typically requires you to ingest data into tables. DuckDB allows you to query heterogeneous sources, so a single query can join a DuckDB table with SQLite, CSV, Parquet, or JSON files. DuckDB provides APIs for C, C++, Go, Python, R, Rust, Java, and Node.js.

Concurrency and scale

Concurrency patterns dictate the selection between these two engines. DuckDB supports multiple readers but limits you to a single writer at a time. Each thread in DuckDB has its own private memory space to avoid locks. If ten different services attempt to write to the same database file, the system will struggle. You know the difference between a local script and a high-concurrency web application. If you need to manage many writers across many machines, you will end up adding a layer like a queue or partitioned files because DuckDB itself does not magically become a multi-writer cluster. ClickHouse manages thousands of concurrent queries using a sophisticated scheduler and supports query priority and resource groups.

Performance varies based on the data scale. For datasets under 100GB, DuckDB and ClickHouse perform comparably on a single machine. At a scale factor of 1000, which represents 1TB of data, ClickHouse on a cluster completes queries orders of magnitude faster than DuckDB. DuckDB provides up to 80X speedups on analytical queries compared to PostgreSQL. DuckDB also uses an out-of-core execution model to handle datasets larger than RAM. ClickHouse relies on its MergeTree engine and specialized storage like AggregatingMergeTree to optimize ingestion and query performance. ClickHouse can query S3 directly using the S3 table engine. High availability in ClickHouse requires running ReplicatedMergeTree tables across at least two replicas per shard, coordinated through Apache ZooKeeper or ClickHouse Keeper.

Operational costs and verdict

Operational costs diverge sharply. DuckDB requires no infrastructure investment and runs on existing hardware without additional fees. ClickHouse Cloud charges between $0.22 and $0.75 per compute unit-hour and $25.30 to $50 per TB-month for storage. Self-hosting ClickHouse requires an infrastructure investment of $2,435 to $30,720 per month and adds a 10% to 20% engineering overhead for maintenance. MotherDuck, a managed service for DuckDB, offers a Pulse tier starting at roughly $0.60 per hour. MotherDuck Mega ($12.00/hr) is 91% cheaper per hour than a Snowflake 3XL setup. Firebolt provides compute at $0.35 per FBU per hour and storage at $23 per TiB per month.

DuckDB is the better choice for embedded analytics or data science notebooks. Use DuckDB when your data fits on a single machine or you want zero operational overhead. ClickHouse provides the necessary power for multi-tenant analytical SaaS, real-time dashboards, large-scale data, or continuous streaming. ClickHouse handles continuous streaming ingestion from Kafka much better than DuckDB. Does the simplicity of DuckDB outweigh the massive scale of ClickHouse for your specific workload? Use DuckDB for local development and ClickHouse for production serving.